{"meta":{"query_hash":"1726eb7b19d1","filters":{"venue":"IEEE Transactions on Human-Machine Systems"},"cohort_total":50,"direct_labels_cover":0,"predictions_cover":50,"exported":50,"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/1726eb7b19d1","api":"https://metacan.xera.ac/api/v1/cohort?venue=IEEE+Transactions+on+Human-Machine+Systems"},"results":[{"id":"W1990441124","doi":"10.1109/thms.2013.2294636","title":"Supporting Air Versus Ground Vehicle Decisions for Interfacility Medical Transport Using Historical Data","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transfer (computing); Estimation; Computer science; Process (computing); Operations research; Environmental science; Engineering","score_opus":0.32365519503919526,"score_gpt":0.5066983064589434,"score_spread":0.18304311141974816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1990441124","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7353217,0.0002773556,0.2588881,0.000814169,0.000057353038,0.00029328594,0.00094061653,0.0006247992,0.0027826698],"genre_scores_gemma":[0.97061306,0.00006179666,0.028744148,0.000019966641,0.00000928604,0.000035907233,0.00031378146,0.000013314152,0.00018871152],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99897265,0.00055266585,0.00008479431,0.00014295871,0.00015387071,0.000093073104],"domain_scores_gemma":[0.98859113,0.008931256,0.00089485716,0.00034731597,0.0009386375,0.00029677578],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030305956,0.0007280924,0.00047813272,0.0012249675,0.00062710256,0.0011146845,0.0006897598,0.0004720357,0.0012551323],"category_scores_gemma":[0.0158,0.0003416315,0.00030600227,0.0007420672,0.0002982478,0.0008715641,0.00051606714,0.0006210713,0.00012813079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032443355,0.00016270597,0.028492495,0.00009123688,0.00006756377,0.00016154512,0.00029691137,0.88884884,0.0015946212,0.0016243581,0.0009882465,0.07734698],"study_design_scores_gemma":[0.000020858253,0.00006828782,0.00415148,0.000015191397,0.000012101074,0.000011740089,0.00020950868,0.9927913,0.0010625232,0.0010721649,0.00057417236,0.000010703856],"about_ca_topic_score_codex":0.056004312,"about_ca_topic_score_gemma":0.06162974,"teacher_disagreement_score":0.056004312,"about_ca_system_score_codex":0.0018178003,"about_ca_system_score_gemma":0.0037005784,"threshold_uncertainty_score":0.111356676},"labels":[],"label_agreement":null},{"id":"W2007301751","doi":"10.1109/thms.2014.2382475","title":"An Observer/Predictor-Based Model of the User for Attaining Situation Awareness","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","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":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Task (project management); Estimator; Observer (physics); User interface; Automation; Human–computer interaction; Control (management); Interface (matter); Controller (irrigation); Invariant (physics); Artificial intelligence; Engineering; Mathematics","score_opus":0.23265357819880342,"score_gpt":0.40084395326360095,"score_spread":0.16819037506479753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007301751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01263385,0.00005831137,0.98501724,0.0001354914,0.000030586398,0.000043312426,0.000033421977,0.00022351838,0.0018241379],"genre_scores_gemma":[0.9164996,0.00026548587,0.07777574,0.00007980208,0.000053667605,0.00026914896,0.00009478874,0.000029998444,0.0049319114],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999451,0.00016508783,0.000032803546,0.00013705005,0.00015147042,0.00006264754],"domain_scores_gemma":[0.9990466,0.00039317456,0.00012839209,0.00013193954,0.0002548878,0.000044978016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007970481,0.0007537873,0.00064130087,0.0002918115,0.00031743103,0.0010642522,0.0010707653,0.001326124,0.0021079166],"category_scores_gemma":[0.0024594222,0.00034843863,0.00055652123,0.0001966621,0.0009761629,0.0016936961,0.00093206123,0.0017040272,0.00044688198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027230522,0.00016654385,0.0033887897,0.00028187849,0.0000765139,0.0003547466,0.00085469795,0.87047607,0.025690602,0.057661474,0.0010112423,0.039765123],"study_design_scores_gemma":[0.000010806994,0.00010646355,0.00024819895,0.000010959021,0.000014891155,0.00003506114,0.000027191281,0.9939844,0.0016625614,0.00334111,0.00054723513,0.000011240583],"about_ca_topic_score_codex":0.0038171508,"about_ca_topic_score_gemma":0.0020963815,"teacher_disagreement_score":0.0038171508,"about_ca_system_score_codex":0.00044583104,"about_ca_system_score_gemma":0.0010581928,"threshold_uncertainty_score":0.007589817},"labels":[],"label_agreement":null},{"id":"W2020076260","doi":"10.1109/thms.2014.2325558","title":"Anticipation in Driving: The Role of Experience in the Efficacy of Pre-event Conflict Cues","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":65,"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":"Networks of Centres of Excellence of Canada","keywords":"Anticipation (artificial intelligence); Competence (human resources); Event (particle physics); Psychology; Cognitive psychology; Driving simulator; Computer science; Social psychology; Simulation; Artificial intelligence","score_opus":0.048132391385258336,"score_gpt":0.40222838254609095,"score_spread":0.3540959911608326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020076260","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9960427,0.00017780134,0.0015121404,0.000039850835,0.000007075744,0.000012340953,0.000020049749,0.0000058944356,0.0021820345],"genre_scores_gemma":[0.9993687,0.00005481187,0.0004355546,0.000009357142,0.00000396328,0.000005990792,0.000016458576,0.0000023000637,0.000102915845],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9995395,0.00016135891,0.000030192383,0.00008210562,0.000114273724,0.00007267803],"domain_scores_gemma":[0.99407864,0.0034586338,0.0011858781,0.00031847056,0.00039631728,0.00056211115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092357124,0.0002036686,0.00014838814,0.00037035166,0.00024015465,0.0010756811,0.00022690702,0.0003982577,0.0014494944],"category_scores_gemma":[0.010376154,0.00019720326,0.00018001186,0.00014722258,0.00048590652,0.00066751736,0.00080853916,0.0004750827,0.000093891635],"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":[0.0057539265,0.0016157267,0.66673887,0.0006083893,0.00027835864,0.0008100277,0.017255576,0.00529363,0.13703799,0.0048560663,0.0005027701,0.15924858],"study_design_scores_gemma":[0.00003446651,0.0019935989,0.9825435,0.00005234267,0.00007941216,0.0003882478,0.0030224675,0.0032501034,0.0052965535,0.0022401942,0.0010342232,0.00006487641],"about_ca_topic_score_codex":0.00091965223,"about_ca_topic_score_gemma":0.0009155571,"teacher_disagreement_score":0.0014494944,"about_ca_system_score_codex":0.00023118574,"about_ca_system_score_gemma":0.00034889803,"threshold_uncertainty_score":0.004884362},"labels":[],"label_agreement":null},{"id":"W2028003743","doi":"10.1109/thms.2013.2284911","title":"Detection and Discrimination of Motion-Defined Form: Implications for the Use of Night Vision Devices","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Visual perception and processing mechanisms","field":"Neuroscience","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":"National Research Council Canada; York University","funders":"University of Toronto; York University","keywords":"Luminance; Artificial intelligence; Computer vision; Stimulus (psychology); Computer science; Image noise; Gaussian noise; Decorrelation; Noise (video); Night vision; Mathematics; Optics; Physics; Image (mathematics); Psychology","score_opus":0.13226616079115247,"score_gpt":0.35659178341108305,"score_spread":0.22432562261993058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028003743","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9711211,0.0012285927,0.024221051,0.0004505807,0.00003898406,0.00011539804,0.00011002037,0.00014282927,0.0025715658],"genre_scores_gemma":[0.98936254,0.00033680064,0.009542183,0.00011609763,0.000011872444,0.000036320183,0.00008271013,0.000025762605,0.00048563018],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9991478,0.00028251152,0.00008649681,0.00021199594,0.0002086376,0.00006258411],"domain_scores_gemma":[0.98782724,0.008032218,0.001304726,0.0013472534,0.0010462109,0.00044238818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001895135,0.00045234303,0.00031888543,0.00036307986,0.00022152493,0.0009809746,0.0007895243,0.0009500522,0.0018230529],"category_scores_gemma":[0.021396538,0.00032034164,0.0003094315,0.00018111408,0.0010021963,0.00143338,0.0005115438,0.0006155648,0.00026554582],"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":[0.0019798996,0.00050299475,0.021096352,0.00041841346,0.00003184693,0.00021060026,0.00046998513,0.0029018885,0.93083924,0.0013863563,0.00013985361,0.040022504],"study_design_scores_gemma":[0.0001104372,0.0044656913,0.47487357,0.00012918892,0.00010925981,0.0017647613,0.00038881603,0.034145623,0.47788066,0.0041915476,0.0018018616,0.00013859366],"about_ca_topic_score_codex":0.0018145363,"about_ca_topic_score_gemma":0.00073820236,"teacher_disagreement_score":0.001895135,"about_ca_system_score_codex":0.00051058125,"about_ca_system_score_gemma":0.0003928081,"threshold_uncertainty_score":0.010022521},"labels":[],"label_agreement":null},{"id":"W2042311736","doi":"10.1109/tsmcc.2012.2227959","title":"Robust Multimodal Person Identification With Limited Training Data","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Speech and Audio Processing","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":"Queen's University; Queen's University Belfast","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Modalities; Speech recognition; Modality (human–computer interaction); Identification (biology); Pattern recognition (psychology); Discrete cosine transform; Facial recognition system; Image (mathematics)","score_opus":0.1401146962662099,"score_gpt":0.2934333072960502,"score_spread":0.15331861102984032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2042311736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07031782,0.00047257944,0.92492837,0.000082776656,0.00006719486,0.00006824319,0.0001708888,0.002175031,0.0017171626],"genre_scores_gemma":[0.6182651,0.00032141316,0.3765297,0.00017741391,0.0000963112,0.0001284183,0.0007294333,0.00015347614,0.0035986654],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9982774,0.00032365165,0.00007923229,0.0004663431,0.00068676315,0.00016656885],"domain_scores_gemma":[0.9989729,0.00029848056,0.00013201199,0.00031623695,0.00023854023,0.00004180852],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012787611,0.00075807056,0.001642091,0.0008313428,0.0003838917,0.0005970599,0.0011298928,0.000881772,0.0017663051],"category_scores_gemma":[0.0035760438,0.00032156223,0.00071749045,0.00064389023,0.00038094076,0.001489972,0.0017486729,0.0010039237,0.0016285031],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080574583,0.00021403786,0.0028101227,0.00020297793,0.00016806145,0.00027387845,0.00019235074,0.04241852,0.12809758,0.0017942053,0.0023298783,0.82069266],"study_design_scores_gemma":[0.00002776245,0.00036937554,0.006447267,0.000036538146,0.00009745941,0.0011468274,0.00012097263,0.8609053,0.12407357,0.0034681093,0.0032154783,0.000091331574],"about_ca_topic_score_codex":0.000908791,"about_ca_topic_score_gemma":0.001039264,"teacher_disagreement_score":0.0017663051,"about_ca_system_score_codex":0.00026839506,"about_ca_system_score_gemma":0.00038910387,"threshold_uncertainty_score":0.0067628026},"labels":[],"label_agreement":null},{"id":"W2070613221","doi":"10.1109/thms.2014.2310953","title":"Affective Movement Recognition Based on Generative and Discriminative Stochastic Dynamic Models","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Emotion and Mood Recognition","field":"Psychology","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":"Discriminative model; Artificial intelligence; Computer science; Hidden Markov model; Representation (politics); Salient; Movement (music); Pattern recognition (psychology); Generative model; Speech recognition; Machine learning; Generative grammar","score_opus":0.051554895619185234,"score_gpt":0.32514340551483983,"score_spread":0.2735885098956546,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070613221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027527904,0.00011199232,0.9706189,0.00010983876,0.000027183525,0.00003100435,0.000067561166,0.00034091246,0.0011647203],"genre_scores_gemma":[0.8370873,0.00027635007,0.15722892,0.000108584456,0.00004990083,0.00013335409,0.0004318458,0.00010168494,0.0045820847],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997371,0.0000656045,0.000012559825,0.000086322376,0.00006705409,0.000031399042],"domain_scores_gemma":[0.9996511,0.00018544955,0.00004974181,0.000036517144,0.00005787235,0.000019335917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037509855,0.00044763662,0.000455465,0.00046988466,0.0002339165,0.00049019366,0.0005064899,0.00036490112,0.0012874476],"category_scores_gemma":[0.001536212,0.0003073399,0.00078729336,0.00041874137,0.00045177978,0.00041535022,0.00042787855,0.00081025605,0.00042875804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014923923,0.00010623051,0.002606068,0.000081904815,0.0000855836,0.000117529285,0.00017109512,0.68312114,0.026934471,0.01874437,0.0019945737,0.26588786],"study_design_scores_gemma":[0.0000014408978,0.000008916274,0.0005084691,0.0000019283184,0.0000032060072,0.000012112298,0.0000036492684,0.99687576,0.00048643016,0.0019045329,0.00018943117,0.0000040862774],"about_ca_topic_score_codex":0.0059781196,"about_ca_topic_score_gemma":0.008121915,"teacher_disagreement_score":0.0059781196,"about_ca_system_score_codex":0.0004941581,"about_ca_system_score_gemma":0.00045525472,"threshold_uncertainty_score":0.011886597},"labels":[],"label_agreement":null},{"id":"W2100726938","doi":"10.1109/tsmc.2013.2239595","title":"Effects of display mode and input method for handheld control of micro aerial vehicles for a reconnaissance mission","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Defence Research and Development Canada","funders":"Defence Research and Development Canada","keywords":"Joystick; Mobile device; Stylus; Computer science; Computer vision; Operator (biology); Target acquisition; Artificial intelligence; Real-time computing; Simulation","score_opus":0.029737938629055504,"score_gpt":0.3790941118361402,"score_spread":0.34935617320708473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100726938","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9951952,0.00048064243,0.0029651423,0.000033124248,0.00006365738,0.00009192487,0.00006241434,0.00012042124,0.0009873802],"genre_scores_gemma":[0.9919326,0.00041274386,0.005986989,0.000109729386,0.000042288997,0.00009385598,0.000107029184,0.00008703419,0.0012277956],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994036,0.00022444413,0.000064027976,0.0000995489,0.00013950985,0.00006893072],"domain_scores_gemma":[0.9840545,0.0132168485,0.00061363843,0.00037890745,0.0012896124,0.00044658198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006630414,0.0007453585,0.0003751606,0.0003811269,0.00027588184,0.0010354187,0.00046236615,0.000649619,0.0065049473],"category_scores_gemma":[0.011194725,0.0003161851,0.00034751953,0.00020891457,0.00018726832,0.00075650885,0.0004023669,0.00039083575,0.00048730243],"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":[0.030856796,0.0021379464,0.0141504295,0.0016039163,0.00023511612,0.000683681,0.0012219896,0.002074927,0.8567517,0.00016509877,0.0006526539,0.08946584],"study_design_scores_gemma":[0.0020278618,0.055536587,0.47626483,0.0005639069,0.0017226061,0.0017621622,0.0025694221,0.02280989,0.4308523,0.00047652939,0.005081197,0.00033268065],"about_ca_topic_score_codex":0.00096509827,"about_ca_topic_score_gemma":0.0010056683,"teacher_disagreement_score":0.0065049473,"about_ca_system_score_codex":0.0002046932,"about_ca_system_score_gemma":0.00018894495,"threshold_uncertainty_score":0.021761239},"labels":[],"label_agreement":null},{"id":"W2332551110","doi":"10.1109/thms.2016.2537760","title":"Expert-Driven Perceptual Features for Modeling Style and Affect in Human Motion","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human Motion and Animation","field":"Engineering","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":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Perception; Computer science; Artificial intelligence; Motion (physics); Affect (linguistics); Cartesian coordinate system; Motion capture; Style (visual arts); Computer vision; Pattern recognition (psychology); Mathematics; Geometry; Communication; Psychology","score_opus":0.03842233311049834,"score_gpt":0.293756535421546,"score_spread":0.25533420231104764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2332551110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11596183,0.000122001074,0.88142926,0.000053375585,0.000024012737,0.00007608175,0.00017993862,0.000292626,0.0018610022],"genre_scores_gemma":[0.900976,0.000098994744,0.097495496,0.000042291966,0.000029046032,0.00009118179,0.00018570934,0.00003603472,0.0010452688],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997644,0.00006648386,0.000008561076,0.00008043726,0.000061248786,0.000018844403],"domain_scores_gemma":[0.99958354,0.00017706779,0.000076421165,0.00004812754,0.00008093036,0.00003395463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004112735,0.00045841557,0.00026830166,0.00049062044,0.00010750312,0.00042922952,0.00043022927,0.000562947,0.0010341268],"category_scores_gemma":[0.0020124489,0.00022977867,0.0005147971,0.0002493661,0.00029653104,0.0005646201,0.00031988265,0.0004325049,0.00022313918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048541612,0.00032862832,0.014475283,0.0002472492,0.00013398392,0.00031456913,0.00080454734,0.64761364,0.08249298,0.007694897,0.0017244274,0.24368444],"study_design_scores_gemma":[0.0000051056722,0.00008976117,0.0063442704,0.000007059892,0.000009720729,0.00006407378,0.000028827453,0.9900793,0.0016131484,0.0013482538,0.0003977872,0.000012699774],"about_ca_topic_score_codex":0.002105593,"about_ca_topic_score_gemma":0.0025790303,"teacher_disagreement_score":0.002105593,"about_ca_system_score_codex":0.0002923261,"about_ca_system_score_gemma":0.00016110325,"threshold_uncertainty_score":0.00418669},"labels":[],"label_agreement":null},{"id":"W2553321217","doi":"10.1109/thms.2016.2620106","title":"Augmented-Reality-Based Indoor Navigation: A Comparative Analysis of Handheld Devices Versus Google Glass","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Spatial Cognition and Navigation","field":"Engineering","cited_by":139,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo","keywords":"Workload; Mobile device; Computer science; Wearable computer; Augmented reality; Navigation system; Wearable technology; Reliability (semiconductor); Human–computer interaction; Real-time computing; Embedded system; Simulation; World Wide Web","score_opus":0.06946273064031878,"score_gpt":0.33020971053351295,"score_spread":0.2607469798931942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2553321217","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98629737,0.005458085,0.0019296145,0.000077710036,0.00004872933,0.00011920069,0.00036308856,0.00009514327,0.00561102],"genre_scores_gemma":[0.9927451,0.0027450786,0.0029144536,0.000051164207,0.000026859268,0.000044739096,0.0003404387,0.000031857744,0.0011002758],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99870086,0.0004180728,0.00012937539,0.0001384455,0.00051654794,0.00009666215],"domain_scores_gemma":[0.9940685,0.0035497504,0.0006075323,0.00026008266,0.001312286,0.00020185355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010109898,0.0004482391,0.00044702366,0.0015584944,0.00026494218,0.0010046446,0.00046013744,0.00044792646,0.0020488112],"category_scores_gemma":[0.0077189356,0.00015090746,0.0007812767,0.0012787965,0.0003126173,0.0012147253,0.0005925162,0.00018768107,0.00032581334],"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":[0.014889231,0.0011805394,0.24630155,0.008626437,0.0015511002,0.0019005183,0.010170874,0.0045249457,0.02280479,0.000910194,0.0029188897,0.68422097],"study_design_scores_gemma":[0.00034593686,0.014068667,0.93081033,0.00092345866,0.0023341046,0.0028442673,0.014491244,0.008430079,0.008102373,0.00032273875,0.017080061,0.00024677577],"about_ca_topic_score_codex":0.006075506,"about_ca_topic_score_gemma":0.011943911,"teacher_disagreement_score":0.006075506,"about_ca_system_score_codex":0.0003769064,"about_ca_system_score_gemma":0.00034379915,"threshold_uncertainty_score":0.012080252},"labels":[],"label_agreement":null},{"id":"W2595039132","doi":"10.1109/thms.2017.2664358","title":"Guest Editorial Special Issue on Drawing and Handwriting Processing for User-Centered Systems","year":2017,"lang":"en","type":"editorial","venue":"IEEE Transactions on Human-Machine Systems","topic":"Hand Gesture 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":"Polytechnique Montréal","funders":"","keywords":"Handwriting; Focus (optics); Frontier; Computer science; Field (mathematics); Special section; Human–computer interaction; Data science; Artificial intelligence; Engineering; History","score_opus":0.0328404144930048,"score_gpt":0.32143325574590265,"score_spread":0.28859284125289786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2595039132","genre_codex":"editorial","genre_gemma":"editorial","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"editorial","genre_consensus":"editorial","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000049989707,0.002855041,0.00031714674,0.015077075,0.9793524,0.000018390465,0.000050074734,0.000073561656,0.0022062783],"genre_scores_gemma":[0.0005234003,0.0036947457,0.00016207113,0.0059089493,0.9758675,0.00002231835,0.000051785926,0.00007572642,0.013693446],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99653304,0.00036867184,0.0003940116,0.0004515836,0.0019502655,0.00030236668],"domain_scores_gemma":[0.9864044,0.0034051803,0.00093203894,0.00041621708,0.00682211,0.00202002],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004287652,0.0024362446,0.0021573245,0.0025957057,0.0019958473,0.007410225,0.0019174324,0.0069042305,0.031619426],"category_scores_gemma":[0.012608067,0.00067595596,0.0016902447,0.0010308517,0.0014576794,0.0037359472,0.0018896898,0.011404369,0.019782472],"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":[0.000047832324,0.00001596429,0.000035323807,0.00018127706,0.000012713236,0.0001306756,0.000010483085,0.000035517976,0.0001658464,0.0005910496,0.9893345,0.009438701],"study_design_scores_gemma":[0.000029829696,0.000024825862,0.00016685497,0.00017603206,0.00002171343,0.00021536741,0.000023645936,0.00015372927,0.0002506405,0.00088695996,0.99803907,0.000011305245],"about_ca_topic_score_codex":0.00040353023,"about_ca_topic_score_gemma":0.0011962268,"teacher_disagreement_score":0.031619426,"about_ca_system_score_codex":0.0016199134,"about_ca_system_score_gemma":0.0021486355,"threshold_uncertainty_score":0.1057775},"labels":[],"label_agreement":null},{"id":"W2608656908","doi":"10.1109/thms.2017.2706727","title":"A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future","year":2017,"lang":"en","type":"review","venue":"IEEE Transactions on Human-Machine Systems","topic":"Gaze Tracking and Assistive Technology","field":"Computer Science","cited_by":245,"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 Kent; Université de Lorraine; National Research Council Canada; Universidade de Aveiro","keywords":"Wheelchair; Assistive technology; Computer science; Data science; Engineering ethics; Human–computer interaction; Engineering; World Wide Web","score_opus":0.11132362243359879,"score_gpt":0.378502560642799,"score_spread":0.26717893820920025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2608656908","genre_codex":"review","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":"review","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00007825421,0.9992817,0.000060193364,0.00021910675,0.000066786764,0.00000782444,0.00003679792,0.0000037678228,0.0002455108],"genre_scores_gemma":[0.0005657993,0.9989459,0.00018089898,0.00012161139,0.000047229623,0.000012426811,0.000041145562,0.0000012024915,0.00008390861],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985972,0.00034875673,0.00045018128,0.00018219906,0.00035095544,0.00007066804],"domain_scores_gemma":[0.9956921,0.0027123268,0.0006084752,0.000073631294,0.0007755421,0.00013794091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025205093,0.0011747115,0.002802499,0.0087154005,0.0005082207,0.0017928879,0.0018667254,0.0015345322,0.0057466654],"category_scores_gemma":[0.0077044275,0.0006249385,0.0022457605,0.008090324,0.0005372213,0.0027256373,0.0010700286,0.0012165838,0.0011282708],"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":[0.00010567527,0.000047447917,0.00044893517,0.29091358,0.00067616627,0.00015498442,0.00024284158,0.0002458231,0.00039928485,0.0015663668,0.01902137,0.6861775],"study_design_scores_gemma":[0.00005052156,0.00022574514,0.0036694405,0.21442617,0.003769505,0.0012981138,0.00038260096,0.0001512536,0.00040614873,0.0016037999,0.773946,0.00007071294],"about_ca_topic_score_codex":0.0052446895,"about_ca_topic_score_gemma":0.012079586,"teacher_disagreement_score":0.0087154005,"about_ca_system_score_codex":0.0014417885,"about_ca_system_score_gemma":0.0063223005,"threshold_uncertainty_score":0.019224524},"labels":[],"label_agreement":null},{"id":"W2609014753","doi":"10.1109/thms.2017.2693245","title":"Force Exertion Affects Grasp Classification Using Force Myography","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":61,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"GRASP; Artificial intelligence; Electrical impedance myography; Computer science; Linear discriminant analysis; Thumb; Pattern recognition (psychology); Medicine","score_opus":0.05227165393494105,"score_gpt":0.2948206906414377,"score_spread":0.24254903670649663,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2609014753","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99487984,0.00014402528,0.004334721,0.00003630596,0.0000122486645,0.00002713352,0.00003477649,0.00003853179,0.00049245555],"genre_scores_gemma":[0.9979863,0.000052162555,0.0016670072,0.000017710268,0.000012669977,0.00000961307,0.000033446457,0.0000129753735,0.00020816465],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987405,0.00044021878,0.00013195297,0.00021134039,0.00035255743,0.00012350286],"domain_scores_gemma":[0.9889234,0.0082152495,0.0014171265,0.00055327924,0.0006021521,0.00028870834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016641604,0.0006756828,0.00048272297,0.00065209053,0.00023421599,0.00090913003,0.00019974531,0.0006644261,0.0011861349],"category_scores_gemma":[0.022696797,0.00026077428,0.0002939095,0.00024334723,0.0006206311,0.00077624264,0.000641771,0.00028322995,0.00022407812],"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":[0.006155637,0.00032270272,0.16902937,0.0004961538,0.0002582785,0.0012993688,0.0027065214,0.0044436716,0.691673,0.00020437475,0.00031084832,0.12310009],"study_design_scores_gemma":[0.000058463393,0.0028986216,0.94582146,0.00003389131,0.00012160649,0.0012734161,0.0006803593,0.014614513,0.03373499,0.0002670045,0.00043882732,0.00005685748],"about_ca_topic_score_codex":0.00061790814,"about_ca_topic_score_gemma":0.0004961927,"teacher_disagreement_score":0.0016641604,"about_ca_system_score_codex":0.00013487067,"about_ca_system_score_gemma":0.00009693591,"threshold_uncertainty_score":0.008801043},"labels":[],"label_agreement":null},{"id":"W2767934528","doi":"10.1109/thms.2017.2767284","title":"Influence of Information Layout on Diagnosis Performance","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Interface (matter); Abstraction; Computer science; Horizontal and vertical; Hierarchy; Human–computer interaction; Task (project management); Domain (mathematical analysis); User interface; Engineering; Systems engineering; Programming language; Mathematics","score_opus":0.03932742642295769,"score_gpt":0.36531749001544034,"score_spread":0.32599006359248267,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2767934528","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9891459,0.0002854234,0.008119638,0.000100737045,0.00003379421,0.000106539446,0.00006631354,0.00047635258,0.0016653682],"genre_scores_gemma":[0.9896853,0.00015011447,0.0093133645,0.00003760801,0.000018734743,0.000085508094,0.00014735243,0.00008263106,0.0004795234],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9951166,0.002745657,0.00050119235,0.0005435647,0.000784475,0.00030850517],"domain_scores_gemma":[0.8567044,0.12708203,0.0068248436,0.0033114809,0.0040922775,0.0019849273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003516708,0.0010432994,0.00055328524,0.0011285589,0.00038743566,0.002382202,0.0006048111,0.0007820946,0.0036805754],"category_scores_gemma":[0.09751566,0.00055080664,0.000471923,0.000635072,0.0005729836,0.0022129957,0.0013499337,0.00056169793,0.0006596329],"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":[0.02096025,0.0042513185,0.2493102,0.003192732,0.0006259894,0.002053685,0.01286405,0.038986664,0.24797972,0.0012996168,0.0022496656,0.41622618],"study_design_scores_gemma":[0.0016490377,0.026597599,0.74096423,0.0006919021,0.001765096,0.0019404985,0.016207641,0.0957449,0.10111744,0.003638702,0.00902198,0.00066090963],"about_ca_topic_score_codex":0.0010741963,"about_ca_topic_score_gemma":0.0007279585,"teacher_disagreement_score":0.0036805754,"about_ca_system_score_codex":0.00058899965,"about_ca_system_score_gemma":0.0005599777,"threshold_uncertainty_score":0.018598318},"labels":[],"label_agreement":null},{"id":"W2783282453","doi":"10.1109/thms.2018.2791570","title":"A Topology of Shared Control Systems—Finding Common Ground in Diversity","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":289,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","keywords":"Computer science; Axiom; Control (management); Context (archaeology); Common ground; Human–computer interaction; Generalizability theory; Distributed computing; Automation; Set (abstract data type); Artificial intelligence; Engineering","score_opus":0.06816260115683855,"score_gpt":0.3206184121259397,"score_spread":0.2524558109691012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2783282453","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032161575,0.0010842695,0.91610503,0.0027760658,0.00017790754,0.00016247184,0.00008730353,0.00036409585,0.047081344],"genre_scores_gemma":[0.83236825,0.00091287115,0.15883866,0.00043722912,0.00019301196,0.0005766286,0.00014320933,0.00017349892,0.006356755],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9944535,0.0020293144,0.0003620975,0.0013716304,0.0012319231,0.0005515594],"domain_scores_gemma":[0.9903183,0.003818827,0.0009416697,0.0027025938,0.001231831,0.0009867363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004453829,0.0006626172,0.0011787955,0.0024821206,0.0037246111,0.007280873,0.0021757693,0.0024957068,0.008478129],"category_scores_gemma":[0.012900249,0.0007675268,0.0013945006,0.0013184997,0.017372465,0.013057923,0.009895256,0.0031662888,0.0011008128],"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":[0.00003821605,0.000014863794,0.0004304003,0.00006494789,0.000024434934,0.00013205886,0.0010314011,0.0082257055,0.0011253626,0.9752495,0.00053398625,0.01312906],"study_design_scores_gemma":[0.00004057789,0.000073620926,0.00027213324,0.0000731964,0.000020577078,0.00013788005,0.00053986907,0.016795851,0.00079412735,0.96559215,0.015623619,0.00003635974],"about_ca_topic_score_codex":0.0014080502,"about_ca_topic_score_gemma":0.0006837646,"teacher_disagreement_score":0.008478129,"about_ca_system_score_codex":0.0020611628,"about_ca_system_score_gemma":0.0020449269,"threshold_uncertainty_score":0.028362215},"labels":[],"label_agreement":null},{"id":"W2811116136","doi":"10.1109/thms.2018.2836798","title":"Efficacy of Group-View Displays in Nuclear Control Rooms","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Computer science; Control (management); State (computer science); Human–computer interaction; Scale (ratio); Artificial intelligence; Physics","score_opus":0.03166666154079122,"score_gpt":0.3594813616581021,"score_spread":0.3278147001173109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811116136","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9943757,0.00027948042,0.0023252051,0.00008439493,0.000028365657,0.00008810763,0.000039819217,0.000100819656,0.0026783207],"genre_scores_gemma":[0.9963541,0.00015222387,0.0027997477,0.000051366416,0.000018324361,0.000058136928,0.00007682122,0.000020965494,0.0004682839],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9930519,0.0043429956,0.00034531936,0.0005286276,0.0014597262,0.00027144756],"domain_scores_gemma":[0.95631397,0.033724062,0.0035677527,0.002584463,0.002494395,0.0013154445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0069127395,0.0006185607,0.00036748094,0.0005503739,0.00052354287,0.0012893681,0.00087204017,0.0007708652,0.003313799],"category_scores_gemma":[0.04186826,0.00026110193,0.0004196407,0.00022821143,0.0007447571,0.0015048548,0.0021151567,0.0005393948,0.00046422056],"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":[0.027739516,0.013817247,0.23278219,0.0033871795,0.0008813204,0.00041410647,0.021503199,0.01517124,0.16136658,0.0025643462,0.003254042,0.51711905],"study_design_scores_gemma":[0.0013178503,0.08706507,0.7867175,0.0010000472,0.0013788673,0.0009041388,0.017010888,0.021773426,0.06702837,0.0018586938,0.013490606,0.00045454287],"about_ca_topic_score_codex":0.0009673583,"about_ca_topic_score_gemma":0.0009842236,"teacher_disagreement_score":0.0069127395,"about_ca_system_score_codex":0.0004966079,"about_ca_system_score_gemma":0.0004919621,"threshold_uncertainty_score":0.03655851},"labels":[],"label_agreement":null},{"id":"W2883511217","doi":"10.1109/thms.2018.2849024","title":"Negotiating Corners With Teleoperated Mobile Robots With Time Delay","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Teleoperation and Haptic Systems","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; Canarie","keywords":"Teleoperation; Mobile robot; Computer science; Robot; Telerobotics; Real-time computing; Simulation; Artificial intelligence","score_opus":0.013334973346723216,"score_gpt":0.23492298772572812,"score_spread":0.2215880143790049,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2883511217","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.7990035,0.00014366131,0.19881447,0.000043012944,0.000021138474,0.000063189305,0.000020257954,0.00029784467,0.0015929817],"genre_scores_gemma":[0.98383015,0.00004099618,0.015507798,0.000008461589,0.000004043064,0.000027665745,0.0000121245275,0.000011029784,0.00055781764],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997291,0.000046459085,0.000013388079,0.00006189499,0.00010604998,0.00004314366],"domain_scores_gemma":[0.99854374,0.00069457374,0.00034223593,0.00016206353,0.00017563533,0.0000818171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000276155,0.00034603497,0.00029471453,0.00019580104,0.000226364,0.00044804794,0.0004669028,0.0004422623,0.001396282],"category_scores_gemma":[0.0024112724,0.00020745044,0.00020891197,0.0001278251,0.0005133856,0.00075237534,0.00047651428,0.00035398116,0.00017616266],"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":[0.0019061031,0.00033012775,0.0064129485,0.00030206167,0.00007165082,0.0009567192,0.0007646089,0.360915,0.5119497,0.0028998624,0.00023344433,0.11325774],"study_design_scores_gemma":[0.00017433913,0.0033805668,0.0070730895,0.000031965716,0.00004910184,0.000621813,0.000506209,0.7778408,0.20461912,0.0026386043,0.0029971446,0.00006716445],"about_ca_topic_score_codex":0.0011972773,"about_ca_topic_score_gemma":0.0010181274,"teacher_disagreement_score":0.001396282,"about_ca_system_score_codex":0.00030238068,"about_ca_system_score_gemma":0.00032342106,"threshold_uncertainty_score":0.0046709776},"labels":[],"label_agreement":null},{"id":"W2885377338","doi":"10.1109/thms.2018.2860595","title":"Does Predictability Play a Role in Task Management? An Experimental Study With a Financial Trading Simulation","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","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":"Ontario Centres of Excellence","keywords":"Predictability; Task (project management); Situation awareness; Leverage (statistics); Workflow; Context (archaeology); Computer science; Situational ethics; Psychology; Cognitive psychology; Applied psychology; Social psychology","score_opus":0.12495528935864357,"score_gpt":0.42100434262217185,"score_spread":0.29604905326352826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885377338","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.998379,0.000020725187,0.0007717305,0.000060044084,0.000011015749,0.000089769,0.000026172545,0.0000090331105,0.0006324268],"genre_scores_gemma":[0.99551386,0.00005197812,0.0031930015,0.00007355517,0.00001923937,0.00039686207,0.00007601776,0.000012297426,0.00066314905],"study_design_codex":"nonrandomized_trial","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99838173,0.0008553221,0.00013243723,0.0002286613,0.0002230757,0.00017880487],"domain_scores_gemma":[0.9750333,0.019753085,0.0017230681,0.0014252656,0.0006252319,0.001440142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026009937,0.00063108257,0.00056567375,0.0003733211,0.00077048334,0.0015984975,0.0010796902,0.0009965013,0.004803366],"category_scores_gemma":[0.020624893,0.00051452435,0.0004962135,0.00026418394,0.00093391776,0.0016156989,0.0012190773,0.0016521958,0.00041220494],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.06329512,0.2684176,0.17499821,0.0028004302,0.0009772117,0.0023911593,0.059311863,0.07589941,0.18674845,0.01150471,0.004193247,0.14946271],"study_design_scores_gemma":[0.01416802,0.26571494,0.350631,0.00051139714,0.00125115,0.00075186725,0.020971946,0.25684822,0.04664906,0.02880833,0.012826829,0.00086727506],"about_ca_topic_score_codex":0.0011045897,"about_ca_topic_score_gemma":0.0011887549,"teacher_disagreement_score":0.004803366,"about_ca_system_score_codex":0.00045400532,"about_ca_system_score_gemma":0.00076757907,"threshold_uncertainty_score":0.016068816},"labels":[],"label_agreement":null},{"id":"W2952113293","doi":"10.1109/thms.2019.2917194","title":"High Cognitive Load Assessment in Drivers Through Wireless Electroencephalography and the Validation of a Modified <i>N</i>-Back Task","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Electroencephalography; Task (project management); Cognitive load; Cognition; n-back; Baseline (sea); Audiology; Elementary cognitive task; Computer science; Simulation; Physical medicine and rehabilitation; Psychology; Working memory; Medicine; Engineering; Neuroscience","score_opus":0.021926708533871436,"score_gpt":0.33552590668791515,"score_spread":0.3135991981540437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952113293","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99584407,0.00005395207,0.0035043578,0.000026323492,0.000012771684,0.0000748128,0.000096364136,0.000023123577,0.0003642286],"genre_scores_gemma":[0.9936091,0.00010075134,0.0053761546,0.0000624909,0.00003363229,0.00017991074,0.0002141068,0.000009539286,0.00041433037],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9994628,0.00015053558,0.00005473063,0.000099289784,0.00018526368,0.00004744267],"domain_scores_gemma":[0.99916685,0.00024321546,0.00018213285,0.00007437592,0.00025758147,0.00007579398],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061738567,0.000576932,0.000270881,0.0003246639,0.00016229879,0.00047911357,0.00037206762,0.0004891327,0.00060025626],"category_scores_gemma":[0.0029558265,0.00014035212,0.00020356552,0.00014132468,0.00021678947,0.00032906706,0.00049098,0.00022950518,0.00019904757],"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":[0.008992907,0.002582733,0.33663636,0.0010345776,0.0004194647,0.0006278364,0.0030392965,0.0030127817,0.46760306,0.00028664936,0.00090251013,0.1748618],"study_design_scores_gemma":[0.000207793,0.009382799,0.9261379,0.000045407833,0.00023150937,0.0010515599,0.0010070062,0.0091382945,0.05084121,0.00026923825,0.0016074472,0.00007969604],"about_ca_topic_score_codex":0.0013057012,"about_ca_topic_score_gemma":0.0024784459,"teacher_disagreement_score":0.0013057012,"about_ca_system_score_codex":0.00014932294,"about_ca_system_score_gemma":0.000249495,"threshold_uncertainty_score":0.0032650828},"labels":[],"label_agreement":null},{"id":"W2962902933","doi":"10.1109/thms.2017.2693242","title":"Qualitative Action Recognition by Wireless Radio Signals in Human–Machine Systems","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"Computer science; Quality (philosophy); Action (physics); Key (lock); Artificial neural network; Artificial intelligence; Identification (biology); Variety (cybernetics); SIGNAL (programming language); Wireless; Human–computer interaction; Machine learning; Telecommunications; Computer security","score_opus":0.07680294279126616,"score_gpt":0.35059736358463295,"score_spread":0.2737944207933668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962902933","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25394064,0.0009844687,0.74095774,0.0003182283,0.000083429666,0.00005598196,0.00016496467,0.0006755596,0.0028189702],"genre_scores_gemma":[0.97565776,0.00016486023,0.023313457,0.000046875775,0.000024922307,0.000020094812,0.00005541809,0.000016545237,0.0007000935],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99953306,0.0001593222,0.00002613883,0.00012098132,0.000114932576,0.00004552376],"domain_scores_gemma":[0.99920565,0.0004455039,0.00015178866,0.000064906286,0.00009590643,0.000036362282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006298747,0.00044723912,0.0004125489,0.00069845264,0.0001852019,0.0007517558,0.00041528625,0.00051450054,0.00083942467],"category_scores_gemma":[0.0028787942,0.00020369209,0.00028919286,0.0005290421,0.00094777433,0.00097017427,0.0005681127,0.00042160365,0.00020301077],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005048673,0.00019487081,0.023181984,0.00046934164,0.00018905776,0.00059433497,0.0008995662,0.5262286,0.08860416,0.03002872,0.0017717126,0.32733276],"study_design_scores_gemma":[0.000007993165,0.000120963356,0.014867628,0.000015709613,0.00002022999,0.000110560286,0.000117431824,0.9618718,0.008078538,0.013892968,0.00086557964,0.000030482382],"about_ca_topic_score_codex":0.0022892198,"about_ca_topic_score_gemma":0.0012082586,"teacher_disagreement_score":0.0022892198,"about_ca_system_score_codex":0.00046304057,"about_ca_system_score_gemma":0.00019081862,"threshold_uncertainty_score":0.004551828},"labels":[],"label_agreement":null},{"id":"W2989911746","doi":"10.1109/thms.2019.2947576","title":"Natural Human–Robot Interface Using Adaptive Tracking System with the Unscented Kalman Filter","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Hand Gesture 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":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Kalman filter; Computer science; Robot; Interface (matter); Cartesian coordinate system; Process (computing); Task (project management); Filter (signal processing); Noise (video); Computer vision; Tracking (education); Simulation; Artificial intelligence; Engineering","score_opus":0.04389706553892932,"score_gpt":0.29133492114076276,"score_spread":0.24743785560183346,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2989911746","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007094595,0.00012169949,0.9905421,0.000036782585,0.000053751082,0.00005652459,0.0000119166425,0.001126113,0.0009563939],"genre_scores_gemma":[0.540277,0.00039633663,0.45329264,0.00022624487,0.00009621745,0.00048012933,0.00014658598,0.00009516062,0.004989697],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989679,0.00022475615,0.00009337822,0.00025102505,0.00040195367,0.0000610546],"domain_scores_gemma":[0.99943596,0.00016644759,0.00007872833,0.00007255872,0.00021823167,0.00002802793],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081399496,0.0007184148,0.0005654964,0.00040885477,0.00043428614,0.0006183762,0.0008567962,0.00074839726,0.0019917607],"category_scores_gemma":[0.0015899718,0.00029217455,0.0005833071,0.0003505751,0.00041814422,0.0008442715,0.00081593177,0.0005009658,0.0006403676],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008419225,0.00046214435,0.0041930825,0.00083919213,0.00027301072,0.00056428777,0.0012393044,0.114373215,0.20492533,0.009733843,0.0058117863,0.65674293],"study_design_scores_gemma":[0.000085821375,0.00058075873,0.0023962872,0.000031550877,0.00007357175,0.00033740653,0.00005973138,0.9634676,0.023646118,0.0014393404,0.0078126285,0.000069192414],"about_ca_topic_score_codex":0.0036007757,"about_ca_topic_score_gemma":0.0026940077,"teacher_disagreement_score":0.0036007757,"about_ca_system_score_codex":0.00032288439,"about_ca_system_score_gemma":0.000721548,"threshold_uncertainty_score":0.0071596503},"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":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5781809,0.00031556477,0.41334373,0.0009047659,0.000019012832,0.00078033534,0.000697512,0.00032219777,0.005436012],"genre_scores_gemma":[0.9201788,0.000085695756,0.078519545,0.000052864445,0.000010637749,0.0004972513,0.00030577875,0.000019118444,0.00033024844],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9908433,0.006020696,0.00062856084,0.0009309006,0.0012076662,0.000369013],"domain_scores_gemma":[0.93825096,0.048556272,0.006010933,0.0032279731,0.0033612151,0.0005925614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00901558,0.0010390541,0.0005758936,0.002632946,0.00090793846,0.0022734303,0.0022226095,0.0012927788,0.0020011466],"category_scores_gemma":[0.050061613,0.0007006925,0.0006226547,0.0028381043,0.0015845002,0.0050923624,0.001634191,0.001978418,0.0002629719],"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":[0.0005784198,0.0027691284,0.3983786,0.0004778867,0.00036042815,0.00043076035,0.0077323434,0.41254547,0.0027798684,0.07159008,0.0019269583,0.10042999],"study_design_scores_gemma":[0.00002170846,0.000209757,0.015832739,0.000034190623,0.000028343402,0.00008115539,0.0013632036,0.9704049,0.00057156204,0.010538854,0.00088798534,0.00002564841],"about_ca_topic_score_codex":0.0061568804,"about_ca_topic_score_gemma":0.00515441,"teacher_disagreement_score":0.00901558,"about_ca_system_score_codex":0.0025927916,"about_ca_system_score_gemma":0.0014134519,"threshold_uncertainty_score":0.047679484},"labels":[],"label_agreement":null},{"id":"W3142032567","doi":"10.1109/thms.2021.3064815","title":"Investigating the P300 Response as a Marker of Working Memory in Virtual Training Environments","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council; Queen's University; Queen's University Belfast","keywords":"Event-related potential; Computer science; Latency (audio); Stimulus (psychology); Virtual reality; Context (archaeology); Electroencephalography; Psychology; Audiology; Cognitive psychology; Human–computer interaction; Medicine; Neuroscience","score_opus":0.08879536586935168,"score_gpt":0.3050038407637269,"score_spread":0.21620847489437522,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142032567","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9932534,0.000035279616,0.0062986533,0.000011270796,0.0000023175348,0.000012259444,0.000018159522,0.000022044766,0.00034652374],"genre_scores_gemma":[0.99728715,0.000034502737,0.002455484,0.000007879997,0.0000023989446,0.000016112428,0.000019720548,0.000004819886,0.0001719221],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9998216,0.00006820765,0.000011549955,0.000026083913,0.000046569545,0.000025965948],"domain_scores_gemma":[0.99912065,0.000522797,0.00011989972,0.000070288865,0.00009906855,0.00006719687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005481842,0.0002672646,0.00016069713,0.00031631053,0.000108239095,0.00036942775,0.00031190846,0.00034035847,0.0008934091],"category_scores_gemma":[0.0035111564,0.00010162489,0.00010305062,0.00017194184,0.00029360398,0.00045154212,0.0004364182,0.00020509174,0.00013740743],"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":[0.0019212731,0.00053480116,0.061079416,0.00044745082,0.000096790536,0.0007520624,0.002538132,0.0037805776,0.7814685,0.00043263513,0.00024787095,0.1467005],"study_design_scores_gemma":[0.00007097521,0.0077560563,0.74543375,0.000057081113,0.00013905272,0.0029080303,0.0023241057,0.02719234,0.21144076,0.0015647594,0.001043881,0.00006925937],"about_ca_topic_score_codex":0.0003673074,"about_ca_topic_score_gemma":0.00051040313,"teacher_disagreement_score":0.0008934091,"about_ca_system_score_codex":0.00007028838,"about_ca_system_score_gemma":0.00009923029,"threshold_uncertainty_score":0.0029887557},"labels":[],"label_agreement":null},{"id":"W3142541787","doi":"10.1109/thms.2021.3066456","title":"Does Explicit Categorization Taxonomy Facilitate Performing Goal-Directed Task Analysis?","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Université de Sherbrooke; École de Technologie Supérieure","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Categorization; Computer science; Taxonomy (biology); Task (project management); Artificial intelligence; Task analysis; Natural language processing; Machine learning; Engineering","score_opus":0.053514017641382375,"score_gpt":0.33348567932866585,"score_spread":0.2799716616872835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3142541787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16519569,0.09472211,0.6158774,0.021520434,0.0020825136,0.008614147,0.006046922,0.0011588342,0.08478188],"genre_scores_gemma":[0.3310814,0.031958964,0.6197038,0.0020018842,0.00017862699,0.007124259,0.005252009,0.00015667778,0.0025422673],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9729997,0.0123096295,0.0056768637,0.0021247491,0.0063077887,0.0005812623],"domain_scores_gemma":[0.90212005,0.057164684,0.0072357594,0.0054182634,0.027345806,0.00071541104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03668774,0.0009702408,0.0013881074,0.021286497,0.0024435972,0.0069415956,0.0024502838,0.0019074877,0.0027313693],"category_scores_gemma":[0.11110344,0.000713643,0.0022446625,0.018603414,0.0023838403,0.018084165,0.0031401035,0.002096213,0.0012285081],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011783129,0.00019388227,0.01900006,0.031527806,0.00027419126,0.00023651552,0.04470415,0.00079474057,0.0030885078,0.071657404,0.012138131,0.8162668],"study_design_scores_gemma":[0.00024511406,0.00063401676,0.060345463,0.09619922,0.0019789296,0.0018141564,0.1578847,0.013846465,0.007157141,0.24689797,0.41242713,0.0005697122],"about_ca_topic_score_codex":0.006789701,"about_ca_topic_score_gemma":0.0149936145,"teacher_disagreement_score":0.03668774,"about_ca_system_score_codex":0.0050033713,"about_ca_system_score_gemma":0.016729401,"threshold_uncertainty_score":0.1940257},"labels":[],"label_agreement":null},{"id":"W3178787732","doi":"10.1109/thms.2021.3087902","title":"Toward Long-Term FMG Model-Based Estimation of Applied Hand Force in Dynamic Motion During Human–Robot Interactions","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Canadian Institutes of Health Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Motion (physics); Robot; Term (time); Artificial intelligence; Calibration; Population; Human–robot interaction; Machine learning; Simulation; Mathematics; Statistics","score_opus":0.02328230106461052,"score_gpt":0.27317170646930616,"score_spread":0.24988940540469565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3178787732","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15635055,0.0017627063,0.8369503,0.00031671,0.00010414533,0.0000966327,0.00072707306,0.0017297239,0.0019622075],"genre_scores_gemma":[0.889401,0.0007855611,0.10363238,0.0001973764,0.00008560253,0.000208615,0.0022740462,0.00012015749,0.0032952444],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996517,0.00007190392,0.000014028122,0.00015319754,0.000065905784,0.00004329748],"domain_scores_gemma":[0.9995122,0.0002110431,0.0000648183,0.00006349426,0.00011765533,0.00003089695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065869873,0.00086430553,0.0005394061,0.00047496628,0.00019729766,0.00053040386,0.0008134301,0.0009093487,0.0007515416],"category_scores_gemma":[0.0021021792,0.00029800535,0.00056201307,0.00038265632,0.0003104912,0.0005720466,0.00061607343,0.0009079255,0.00064643123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004531189,0.0004044712,0.013467991,0.00028368304,0.00028209298,0.0003634689,0.00032270455,0.49043,0.08453869,0.0012180146,0.0045252796,0.40371045],"study_design_scores_gemma":[0.0000067574565,0.000098803604,0.00832471,0.000019779422,0.000019835603,0.00008172536,0.000037003996,0.98598236,0.0040584435,0.00053197384,0.0008216475,0.000016876114],"about_ca_topic_score_codex":0.005992849,"about_ca_topic_score_gemma":0.009028556,"teacher_disagreement_score":0.005992849,"about_ca_system_score_codex":0.00030485328,"about_ca_system_score_gemma":0.00048009673,"threshold_uncertainty_score":0.011915982},"labels":[],"label_agreement":null},{"id":"W3195584230","doi":"10.1109/thms.2021.3107675","title":"Individualized Mutual Adaptation in Human-Agent Teams","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Université de Montréal","funders":"","keywords":"Adaptation (eye); Computer science; Task (project management); Metric (unit); Knowledge management; Artificial intelligence; Human–computer interaction; Process management; Psychology; Business; Engineering; Marketing","score_opus":0.06216651657609476,"score_gpt":0.3856208231288542,"score_spread":0.3234543065527594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195584230","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.54963404,0.00026229172,0.44339615,0.00029542577,0.000044617045,0.00011968802,0.000025413978,0.00052003097,0.005702274],"genre_scores_gemma":[0.98522675,0.000029473618,0.0139533635,0.000029892286,0.0000079061165,0.000039500137,0.000012065774,0.000019239815,0.0006817934],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9981014,0.0010031163,0.00007184489,0.00037946834,0.0002666016,0.00017746814],"domain_scores_gemma":[0.99545527,0.002631578,0.0006364952,0.0005637957,0.0003532653,0.00035960128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029157223,0.0005685195,0.0006664679,0.0005133007,0.00077688135,0.001010998,0.0008844799,0.0008177259,0.001018435],"category_scores_gemma":[0.010914525,0.00038543236,0.00036789948,0.00026906634,0.0012391614,0.0013665961,0.0022842274,0.00074446725,0.00020929617],"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":[0.00020422623,0.0002632088,0.00854131,0.00007854379,0.00014489518,0.00021887993,0.0011577588,0.9076948,0.0068722693,0.008975219,0.000672444,0.06517636],"study_design_scores_gemma":[0.000017794524,0.00015250925,0.0018199701,0.0000052341657,0.000020697005,0.000047394446,0.00019932278,0.98627543,0.0011076271,0.0097782705,0.00055819843,0.000017409688],"about_ca_topic_score_codex":0.0020730905,"about_ca_topic_score_gemma":0.0014438473,"teacher_disagreement_score":0.0029157223,"about_ca_system_score_codex":0.0005702853,"about_ca_system_score_gemma":0.0006939605,"threshold_uncertainty_score":0.01542002},"labels":[],"label_agreement":null},{"id":"W3199713003","doi":"10.1109/thms.2021.3107256","title":"Phase Variable Based Recognition of Human Locomotor Activities Across Diverse Gait Patterns","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Gait; Adaptability; Computer science; STRIDE; Mathematics; Machine learning; Physical medicine and rehabilitation; Medicine; Biology","score_opus":0.038140331136448144,"score_gpt":0.2931912207926373,"score_spread":0.25505088965618916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199713003","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23667105,0.00051453867,0.7578598,0.00007762522,0.00007308185,0.000108568944,0.00045023503,0.001155159,0.0030898242],"genre_scores_gemma":[0.8422443,0.00040910547,0.15445718,0.00005318151,0.00005307015,0.00009742588,0.0006343851,0.000077453726,0.0019738348],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99983823,0.000024755705,0.000011323875,0.00005915162,0.000051455863,0.000014978951],"domain_scores_gemma":[0.9997751,0.00007235058,0.000038348815,0.000024676478,0.0000746192,0.000014827188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000201824,0.00029294885,0.0003635074,0.0007839716,0.00011086218,0.00033257098,0.0002302785,0.00031506963,0.0011351223],"category_scores_gemma":[0.00070015714,0.000100386176,0.00022728855,0.00060310063,0.00013970013,0.0003022628,0.00021851753,0.00019335876,0.00045488685],"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":[0.00035724445,0.00010377248,0.012236505,0.00017783364,0.0000679458,0.00013068525,0.00014408166,0.010646839,0.12398488,0.00079573365,0.0015383484,0.84981614],"study_design_scores_gemma":[0.00006943117,0.0006762217,0.14251669,0.00007235276,0.0001411299,0.002105433,0.00029296166,0.75066566,0.09380982,0.002773511,0.0067890882,0.00008768004],"about_ca_topic_score_codex":0.000651002,"about_ca_topic_score_gemma":0.0012399005,"teacher_disagreement_score":0.0011351223,"about_ca_system_score_codex":0.000090251124,"about_ca_system_score_gemma":0.0001474724,"threshold_uncertainty_score":0.0037973523},"labels":[],"label_agreement":null},{"id":"W3205246920","doi":"10.1109/thms.2021.3112957","title":"Assistant Robot Enhances the Perceived Communication Quality of People With Dementia: A Proof of Concept","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"China Scholarship Council","keywords":"Robot; Dementia; Psychology; Applied psychology; Perception; Human–computer interaction; Humanoid robot; Computer science; Social isolation; Medicine; Artificial intelligence","score_opus":0.07426700498940422,"score_gpt":0.39180713500403896,"score_spread":0.31754013001463477,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205246920","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.990044,0.00041684226,0.007671855,0.00013592021,0.00006819445,0.0005394244,0.00006968698,0.00011275202,0.0009413272],"genre_scores_gemma":[0.9692217,0.0005464643,0.027826807,0.00013431077,0.000029216711,0.00087822665,0.00008168843,0.000016222313,0.0012652773],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9995592,0.00019211226,0.000028690001,0.00005943199,0.0001006521,0.00005986985],"domain_scores_gemma":[0.9988965,0.0006368975,0.000092077935,0.00005165814,0.00018830209,0.0001346619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014572797,0.00064583233,0.00036333542,0.00033642352,0.00023079437,0.00041516378,0.0005334477,0.00053890917,0.002932561],"category_scores_gemma":[0.0021029017,0.00019869696,0.0005964419,0.000077885736,0.0004884874,0.00075847126,0.0007960477,0.00039596966,0.00020070253],"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":[0.016650226,0.032630425,0.019991396,0.00699921,0.0004865499,0.0010486597,0.0059606237,0.0018119446,0.6279565,0.0010199725,0.0021149372,0.2833295],"study_design_scores_gemma":[0.0061307196,0.39574924,0.20009883,0.0006520353,0.002229733,0.0025495065,0.007445735,0.01542875,0.3527717,0.00092869235,0.015754184,0.00026094698],"about_ca_topic_score_codex":0.00042634908,"about_ca_topic_score_gemma":0.00036011185,"teacher_disagreement_score":0.002932561,"about_ca_system_score_codex":0.00014370095,"about_ca_system_score_gemma":0.0003709564,"threshold_uncertainty_score":0.0098103285},"labels":[],"label_agreement":null},{"id":"W4206041117","doi":"10.1109/thms.2021.3137032","title":"EEG Correlates of Driving Performance","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":9,"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":"Engineering and Physical Sciences Research Council; Queen's University; Queen's University Belfast","keywords":"Notation; Task (project management); Electroencephalography; Alpha (finance); Artificial intelligence; Session (web analytics); Computer science; Mathematics; Arithmetic; Psychology; Statistics; Psychometrics; Engineering; World Wide Web","score_opus":0.03450477912760559,"score_gpt":0.2817183517238656,"score_spread":0.24721357259626003,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206041117","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98072517,0.00080207083,0.009561249,0.000107985354,0.000040631836,0.00009089568,0.0018820566,0.00017100803,0.0066189724],"genre_scores_gemma":[0.99642015,0.00041649077,0.0016238822,0.000022343536,0.000045055796,0.00003072266,0.00063353556,0.000019506342,0.00078837556],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99978536,0.000050607006,0.000019700516,0.00006371819,0.00005809048,0.000022661816],"domain_scores_gemma":[0.99877256,0.00046258385,0.00031636236,0.00008156829,0.00029733978,0.00006965841],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026291827,0.0003588312,0.0001714144,0.00057256897,0.000104040766,0.0006369675,0.00013674279,0.00028737282,0.0019415509],"category_scores_gemma":[0.004257371,0.0001077142,0.00013423583,0.0005935412,0.00014152682,0.0002803871,0.00022426128,0.0002864474,0.00045543246],"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":[0.0016869755,0.00032428565,0.5358775,0.0007466053,0.0005707844,0.00067070685,0.0012501902,0.004022762,0.22758704,0.0007610912,0.0022009192,0.22430117],"study_design_scores_gemma":[0.000008906567,0.00027116312,0.9922242,0.000013816446,0.000037737944,0.0003671737,0.00011783019,0.0016351483,0.0043209144,0.00026156584,0.00073023053,0.00001120468],"about_ca_topic_score_codex":0.0010125696,"about_ca_topic_score_gemma":0.0011206528,"teacher_disagreement_score":0.0019415509,"about_ca_system_score_codex":0.00009415083,"about_ca_system_score_gemma":0.00008522882,"threshold_uncertainty_score":0.0064951777},"labels":[],"label_agreement":null},{"id":"W4213439100","doi":"10.1109/thms.2022.3146053","title":"Myoelectric Control With Fixed Convolution-Based Time-Domain Feature Extraction: Exploring the Spatio–Temporal Interaction","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Muscle activation and electromyography studies","field":"Engineering","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Natural Science Foundation of China","keywords":"Computer science; Generalizability theory; Leverage (statistics); Pattern recognition (psychology); Artificial intelligence; Feature extraction; Convolution (computer science); Feature (linguistics); Machine learning; Artificial neural network; Mathematics","score_opus":0.019079330672531418,"score_gpt":0.2322600840912812,"score_spread":0.21318075341874979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213439100","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03439154,0.00060591806,0.963625,0.00011069105,0.000029322826,0.000024717976,0.00005022492,0.00041095822,0.0007516321],"genre_scores_gemma":[0.6268672,0.00070233,0.36874393,0.00015206823,0.000050842427,0.000078809775,0.00026420318,0.00008919864,0.0030514693],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998399,0.000021595559,0.000012025219,0.0000504839,0.000053347518,0.000022576829],"domain_scores_gemma":[0.9997906,0.00010827088,0.000028723285,0.0000277301,0.00003264386,0.000012071942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036591938,0.00052796357,0.0004216651,0.0003139503,0.000118793185,0.00040613284,0.00051101943,0.000441062,0.0009344087],"category_scores_gemma":[0.0010410263,0.00020673074,0.0005053988,0.00046469056,0.00026701324,0.0006441578,0.0005126709,0.0004979743,0.00024938656],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025647113,0.00013092377,0.0019387617,0.00017206224,0.00010540909,0.0001763335,0.0001224522,0.19925803,0.08298769,0.0043149674,0.0013956882,0.70914125],"study_design_scores_gemma":[0.000005435669,0.0000431176,0.0009757245,0.000008206803,0.000014139989,0.00008147461,0.000007675479,0.98978466,0.0073625697,0.0010664266,0.00064369506,0.0000068576614],"about_ca_topic_score_codex":0.0027118898,"about_ca_topic_score_gemma":0.0028246443,"teacher_disagreement_score":0.0027118898,"about_ca_system_score_codex":0.00024777278,"about_ca_system_score_gemma":0.00043236392,"threshold_uncertainty_score":0.005392194},"labels":[],"label_agreement":null},{"id":"W4220951123","doi":"10.1109/thms.2022.3155714","title":"Situated Visual Alarm Displays Support Machine Fitness Assessment for Nonexplainable Automation","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Healthcare Technology and Patient Monitoring","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal College of Physicians and Surgeons of Canada; University of Ottawa","funders":"","keywords":"ALARM; Situated; Artificial intelligence; Computer science; Machine learning; Engineering","score_opus":0.046762208882050106,"score_gpt":0.3834482084951403,"score_spread":0.33668599961309015,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220951123","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.96371233,0.00025037304,0.025208645,0.00095591845,0.00007599222,0.00023819279,0.00016075023,0.0010693555,0.008328479],"genre_scores_gemma":[0.98932284,0.00006452397,0.009612035,0.000098083954,0.000018980923,0.0000875235,0.00003946792,0.000028751583,0.00072776305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99742824,0.0015757271,0.00014550325,0.00019554896,0.0004732628,0.00018169651],"domain_scores_gemma":[0.9714428,0.023144664,0.0023101945,0.0010410823,0.0013239827,0.0007372673],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024778175,0.0004698564,0.00023627521,0.00047695806,0.0003751742,0.0010886302,0.0007466951,0.0009745614,0.010158767],"category_scores_gemma":[0.039585706,0.00028882528,0.0004529609,0.00020437727,0.0003939395,0.0015113702,0.0015015862,0.00047821383,0.00096873846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0064303963,0.0025344046,0.15833673,0.0020695429,0.000107679734,0.002809254,0.040835585,0.008255533,0.09565957,0.0028435017,0.014959674,0.66515815],"study_design_scores_gemma":[0.00092912535,0.020152558,0.7474635,0.0023740102,0.0005120266,0.0046648635,0.038576722,0.060594566,0.07486275,0.0073648337,0.041775193,0.0007298717],"about_ca_topic_score_codex":0.00054824667,"about_ca_topic_score_gemma":0.0007643881,"teacher_disagreement_score":0.010158767,"about_ca_system_score_codex":0.00036391433,"about_ca_system_score_gemma":0.00048229867,"threshold_uncertainty_score":0.033984482},"labels":[],"label_agreement":null},{"id":"W4226167526","doi":"10.1109/thms.2021.3138684","title":"Toward Active Physical Human–Robot Interaction: Quantifying the Human State During Interactions","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Social Robot Interaction and HRI","field":"Psychology","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robot; Human–computer interaction; Perception; Personality psychology; Human–robot interaction; Software deployment; Exploratory research; Computer science; Task (project management); Artificial intelligence; Psychology; Engineering; Social psychology; Personality","score_opus":0.167364245123494,"score_gpt":0.4362022721921432,"score_spread":0.26883802706864923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226167526","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.8416833,0.0004414514,0.14987762,0.00024734033,0.000045161145,0.00034474663,0.00029047581,0.0002849518,0.006784989],"genre_scores_gemma":[0.9572829,0.00017108364,0.04151302,0.00009260948,0.000018650991,0.00020733444,0.0001456328,0.00002614583,0.00054265215],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9982077,0.00091890414,0.000080999045,0.0003356333,0.00036406115,0.00009275409],"domain_scores_gemma":[0.99525356,0.0026084816,0.0009388412,0.0003594012,0.0005779577,0.00026182501],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015866803,0.00055976375,0.00029744557,0.00074788113,0.00041972194,0.0018068529,0.0005061684,0.00077830954,0.0020329799],"category_scores_gemma":[0.009663237,0.00034909995,0.00029301588,0.0003949,0.0010290296,0.001637589,0.0013017296,0.00058290776,0.0004490727],"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":[0.0017874232,0.0012665377,0.1822914,0.0030310927,0.0004975274,0.00044628064,0.034350697,0.01374036,0.46473148,0.0055593494,0.0020419136,0.29025578],"study_design_scores_gemma":[0.00008391146,0.0034729175,0.8141143,0.00034189085,0.00025077123,0.0010228968,0.017812783,0.08130037,0.06214262,0.012271491,0.006813076,0.00037307927],"about_ca_topic_score_codex":0.0008725376,"about_ca_topic_score_gemma":0.0013528893,"teacher_disagreement_score":0.0020329799,"about_ca_system_score_codex":0.00025611924,"about_ca_system_score_gemma":0.00030891065,"threshold_uncertainty_score":0.008391261},"labels":[],"label_agreement":null},{"id":"W4230627982","doi":"10.1109/thms.2014.2338532","title":"IEEE Transactions on Human-Machine Systems publication information","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Canadian Standards Association","funders":"","keywords":"Computer science; Human–machine system; World Wide Web; Data science; Information retrieval; Human–computer interaction","score_opus":0.04222023821799235,"score_gpt":0.3524445768534288,"score_spread":0.3102243386354364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230627982","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008986935,0.03393339,0.17098814,0.0234054,0.11321063,0.000808821,0.016079718,0.0068200496,0.62576693],"genre_scores_gemma":[0.04120389,0.027209636,0.021586815,0.0017832088,0.008616865,0.00028206373,0.018150814,0.0010738362,0.88009286],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985789,0.00016837515,0.00010375973,0.0001331011,0.000907962,0.00010789552],"domain_scores_gemma":[0.99585766,0.00096081395,0.0001226402,0.00059159094,0.0022323919,0.00023490227],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0018315685,0.0011901355,0.0015894354,0.0027529295,0.0008160465,0.0035008525,0.0009622923,0.0017800565,0.35012335],"category_scores_gemma":[0.0050609848,0.00046921553,0.00058506074,0.0023842517,0.0005701214,0.0030975712,0.0012327965,0.0016410337,0.17071922],"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":[0.00013696513,0.00012784878,0.0006650789,0.00038113017,0.000026799478,0.000087922905,0.000042015556,0.0013318663,0.0013834946,0.008177731,0.6434677,0.3441714],"study_design_scores_gemma":[0.00004099743,0.00010619767,0.0025813223,0.0003408725,0.00006211202,0.00020456592,0.00009113043,0.009957694,0.0017261001,0.012221785,0.9726298,0.00003749964],"about_ca_topic_score_codex":0.0023691638,"about_ca_topic_score_gemma":0.0033528015,"teacher_disagreement_score":0.64987665,"about_ca_system_score_codex":0.0006723211,"about_ca_system_score_gemma":0.0017109369,"threshold_uncertainty_score":0.9269702},"labels":[],"label_agreement":null},{"id":"W4242628070","doi":"10.1109/thms.2013.2270319","title":"IEEE Transactions on Human-Machine Systems publication information","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Canadian Standards Association","funders":"","keywords":"Computer science; World Wide Web; Information retrieval; Data science","score_opus":0.04414786797967752,"score_gpt":0.3491400944733836,"score_spread":0.3049922264937061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242628070","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009096876,0.034872293,0.17835142,0.022657653,0.10856645,0.0008153383,0.015047354,0.00698058,0.623612],"genre_scores_gemma":[0.042883303,0.028216893,0.022815738,0.0017406816,0.008183866,0.00028891725,0.017157916,0.0010316853,0.87768096],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861836,0.0001685468,0.00010023822,0.00012816594,0.0008796182,0.00010511009],"domain_scores_gemma":[0.996068,0.00091386586,0.00011690166,0.00057783356,0.0021062074,0.00021704042],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0017906714,0.0011895637,0.0016062296,0.002697913,0.0008120385,0.0034551027,0.000962998,0.0017443391,0.33752567],"category_scores_gemma":[0.004948176,0.0004624823,0.00058117614,0.0023927267,0.00058453373,0.0030941218,0.0012216845,0.0015932611,0.16400234],"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":[0.00013955981,0.00012634708,0.00068875594,0.0003864145,0.00002827998,0.00009123118,0.00004425321,0.0013729102,0.001364179,0.008573034,0.6302813,0.3569037],"study_design_scores_gemma":[0.000040783543,0.00010306971,0.002610036,0.00034146698,0.00006587076,0.00021096037,0.00009438593,0.010314381,0.0017259193,0.012453193,0.97200215,0.000037690206],"about_ca_topic_score_codex":0.0024202839,"about_ca_topic_score_gemma":0.0034135445,"teacher_disagreement_score":0.33752567,"about_ca_system_score_codex":0.0006636407,"about_ca_system_score_gemma":0.0016844374,"threshold_uncertainty_score":0.94493926},"labels":[],"label_agreement":null},{"id":"W4242965482","doi":"10.1109/thms.2013.2292177","title":"IEEE Transactions on Human-Machine Systems publication information","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Canadian Standards Association","funders":"","keywords":"Computer science; World Wide Web; Business","score_opus":0.04414786797967752,"score_gpt":0.3491400944733836,"score_spread":0.3049922264937061,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242965482","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009096876,0.034872293,0.17835142,0.022657653,0.10856645,0.0008153383,0.015047354,0.00698058,0.623612],"genre_scores_gemma":[0.042883303,0.028216893,0.022815738,0.0017406816,0.008183866,0.00028891725,0.017157916,0.0010316853,0.87768096],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861836,0.0001685468,0.00010023822,0.00012816594,0.0008796182,0.00010511009],"domain_scores_gemma":[0.996068,0.00091386586,0.00011690166,0.00057783356,0.0021062074,0.00021704042],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0017906714,0.0011895637,0.0016062296,0.002697913,0.0008120385,0.0034551027,0.000962998,0.0017443391,0.33752567],"category_scores_gemma":[0.004948176,0.0004624823,0.00058117614,0.0023927267,0.00058453373,0.0030941218,0.0012216845,0.0015932611,0.16400234],"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":[0.00013955981,0.00012634708,0.00068875594,0.0003864145,0.00002827998,0.00009123118,0.00004425321,0.0013729102,0.001364179,0.008573034,0.6302813,0.3569037],"study_design_scores_gemma":[0.000040783543,0.00010306971,0.002610036,0.00034146698,0.00006587076,0.00021096037,0.00009438593,0.010314381,0.0017259193,0.012453193,0.97200215,0.000037690206],"about_ca_topic_score_codex":0.0024202839,"about_ca_topic_score_gemma":0.0034135445,"teacher_disagreement_score":0.66247433,"about_ca_system_score_codex":0.0006636407,"about_ca_system_score_gemma":0.0016844374,"threshold_uncertainty_score":0.94493926},"labels":[],"label_agreement":null},{"id":"W4243310653","doi":"10.1109/thms.2014.2321311","title":"IEEE Transactions on Human-Machine Systems publication information","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Canadian Standards Association","funders":"","keywords":"Computer science; Data science; Information retrieval; World Wide Web","score_opus":0.04222023821799235,"score_gpt":0.3524445768534288,"score_spread":0.3102243386354364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4243310653","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008986935,0.03393339,0.17098814,0.0234054,0.11321063,0.000808821,0.016079718,0.0068200496,0.62576693],"genre_scores_gemma":[0.04120389,0.027209636,0.021586815,0.0017832088,0.008616865,0.00028206373,0.018150814,0.0010738362,0.88009286],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9985789,0.00016837515,0.00010375973,0.0001331011,0.000907962,0.00010789552],"domain_scores_gemma":[0.99585766,0.00096081395,0.0001226402,0.00059159094,0.0022323919,0.00023490227],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0018315685,0.0011901355,0.0015894354,0.0027529295,0.0008160465,0.0035008525,0.0009622923,0.0017800565,0.35012335],"category_scores_gemma":[0.0050609848,0.00046921553,0.00058506074,0.0023842517,0.0005701214,0.0030975712,0.0012327965,0.0016410337,0.17071922],"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":[0.00013696513,0.00012784878,0.0006650789,0.00038113017,0.000026799478,0.000087922905,0.000042015556,0.0013318663,0.0013834946,0.008177731,0.6434677,0.3441714],"study_design_scores_gemma":[0.00004099743,0.00010619767,0.0025813223,0.0003408725,0.00006211202,0.00020456592,0.00009113043,0.009957694,0.0017261001,0.012221785,0.9726298,0.00003749964],"about_ca_topic_score_codex":0.0023691638,"about_ca_topic_score_gemma":0.0033528015,"teacher_disagreement_score":0.35012335,"about_ca_system_score_codex":0.0006723211,"about_ca_system_score_gemma":0.0017109369,"threshold_uncertainty_score":0.9269702},"labels":[],"label_agreement":null},{"id":"W4254158391","doi":"10.1109/thms.2017.2671618","title":"2017 IEEE International Conference on Systems, Man, and Cybernetics, October 5–8, 2017, Banff Center, Banff, Canada","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Advanced Data and IoT Technologies","field":"Engineering","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":"Center (category theory); Cybernetics; Library science; Geography; Computer science; Artificial intelligence; Chemistry","score_opus":0.061342363293128456,"score_gpt":0.30358191912133903,"score_spread":0.24223955582821058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254158391","genre_codex":"other","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":"other","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03822195,0.04599755,0.24019808,0.01689932,0.049250256,0.00048163682,0.0037571462,0.009539071,0.59565496],"genre_scores_gemma":[0.11393582,0.029845882,0.04540392,0.0013983533,0.0023267786,0.00022793245,0.010144742,0.00084391225,0.7958726],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993174,0.0000647552,0.000032070428,0.000068595065,0.00035472555,0.00016242996],"domain_scores_gemma":[0.9983632,0.00011081646,0.000022473654,0.00015925065,0.001113567,0.00023060874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016181938,0.001075635,0.0011615183,0.00143387,0.0013879706,0.0030572878,0.0010655137,0.00091373135,0.08253692],"category_scores_gemma":[0.0013098434,0.00027766963,0.0005796396,0.0011488928,0.0011387115,0.0013006254,0.0015545934,0.0013544023,0.03765958],"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":[0.00018477015,0.00015084053,0.0026553338,0.000265894,0.00005716117,0.0002622244,0.00020370552,0.003034267,0.00484261,0.015760968,0.571975,0.40060726],"study_design_scores_gemma":[0.000014009572,0.000054727116,0.0032707958,0.00023857226,0.00006652783,0.00025068055,0.00056780345,0.018937778,0.004684902,0.005254896,0.96662384,0.000035423418],"about_ca_topic_score_codex":0.086868025,"about_ca_topic_score_gemma":0.14719798,"teacher_disagreement_score":0.086868025,"about_ca_system_score_codex":0.0022469822,"about_ca_system_score_gemma":0.008364301,"threshold_uncertainty_score":0.2761135},"labels":[],"label_agreement":null},{"id":"W4292387435","doi":"10.1109/thms.2022.3194715","title":"Sonified Distance in Sensory Substitution Does Not Always Improve Localization: Comparison With a 2-D and 3-D Handheld Device","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Tactile and Sensory Interactions","field":"Neuroscience","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":"Fonds de recherche du Québec – Nature et technologies","keywords":"Substitution (logic); Sensory substitution; Mobile device; Computer science; Sensory system; Computer graphics (images); Biology; Neuroscience; Operating system","score_opus":0.05438581490685924,"score_gpt":0.30621969476112426,"score_spread":0.25183387985426503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292387435","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.98000115,0.0012015465,0.01618564,0.00007951562,0.00012116154,0.00003344084,0.000074430354,0.000312606,0.0019904918],"genre_scores_gemma":[0.9834411,0.0005715421,0.014199308,0.00010913681,0.000031163432,0.000038379043,0.00010879637,0.000055337176,0.0014452414],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997389,0.00007721934,0.00002552687,0.000052219235,0.00007109405,0.00003498679],"domain_scores_gemma":[0.998495,0.0010192717,0.00009078869,0.0001568308,0.00014488373,0.00009323465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036604583,0.00049672247,0.00040422363,0.0003487801,0.00010462606,0.0005155371,0.00044871683,0.00066683197,0.0043082684],"category_scores_gemma":[0.00260234,0.0001633195,0.0002586754,0.00023871848,0.00028050446,0.0010272895,0.00068318035,0.00020914276,0.00054747355],"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":[0.014519981,0.00075825193,0.0057330974,0.0018021928,0.00021278148,0.0004347897,0.00048634314,0.004463996,0.5659752,0.00059330056,0.0007760624,0.40424395],"study_design_scores_gemma":[0.004100656,0.091434576,0.20209168,0.0005601136,0.0024382905,0.0058859694,0.0019886575,0.08358949,0.5796911,0.0033129868,0.024487887,0.00041867833],"about_ca_topic_score_codex":0.00043367225,"about_ca_topic_score_gemma":0.00046533102,"teacher_disagreement_score":0.0043082684,"about_ca_system_score_codex":0.00010744747,"about_ca_system_score_gemma":0.00014425574,"threshold_uncertainty_score":0.014412522},"labels":[],"label_agreement":null},{"id":"W4293202779","doi":"10.1109/thms.2022.3164775","title":"Public Opinion About the Benefit, Risk, and Acceptance of Aerial Manipulation Systems","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; Ryerson University","keywords":"Public opinion; Business; Risk analysis (engineering); Political science; Law","score_opus":0.08642854745479074,"score_gpt":0.3577846786480007,"score_spread":0.27135613119320995,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4293202779","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99506336,0.000038200713,0.00030690964,0.00062903634,0.000012833961,0.000028502727,0.00010228307,0.000007427925,0.0038113762],"genre_scores_gemma":[0.999049,0.000053965,0.0001441462,0.00012849603,0.000020454654,0.000035323807,0.00008103238,0.0000025931129,0.00048492895],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9957209,0.0019790197,0.0002981224,0.0003014945,0.0012502532,0.00045011492],"domain_scores_gemma":[0.9621762,0.021323938,0.009435821,0.0009831628,0.004706487,0.0013744312],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007942325,0.00022335419,0.0002951622,0.00079471635,0.0006123897,0.0018359779,0.00030680417,0.00096553506,0.004509155],"category_scores_gemma":[0.026339373,0.00015482491,0.0004491326,0.00064575975,0.00084461464,0.0016700132,0.0007363699,0.000969702,0.0005636638],"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":[0.0015833706,0.0009990834,0.90860987,0.0002554824,0.00016131924,0.00050645665,0.022034997,0.0010119077,0.004959918,0.0016070188,0.0039367103,0.05433392],"study_design_scores_gemma":[0.00009801769,0.0013637972,0.9502803,0.0001437894,0.00017411596,0.00020799242,0.031743743,0.005157872,0.0024938332,0.000830584,0.0074099703,0.000095909876],"about_ca_topic_score_codex":0.004370193,"about_ca_topic_score_gemma":0.0030339917,"teacher_disagreement_score":0.007942325,"about_ca_system_score_codex":0.0011521676,"about_ca_system_score_gemma":0.0005400533,"threshold_uncertainty_score":0.042003512},"labels":[],"label_agreement":null},{"id":"W4309226869","doi":"10.1109/thms.2022.3207699","title":"Touch Semantics for Intuitive Physical Manipulation of Humanoids","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Robotic Locomotion and Control","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Humanoid robot; Computer science; Human–computer interaction; Task (project management); Semantics (computer science); Robot; Usability; Artificial intelligence; Set (abstract data type); Engineering; Programming language","score_opus":0.030188028293324107,"score_gpt":0.27688138928495637,"score_spread":0.24669336099163225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309226869","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010029438,0.00003167567,0.98733217,0.0000615534,0.000012051738,0.000052372532,0.000028143842,0.000494273,0.0019582554],"genre_scores_gemma":[0.5132163,0.00010947328,0.4830338,0.000099112076,0.000012815424,0.00038056241,0.0001228401,0.00029222164,0.0027329118],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993048,0.00023022998,0.00006839288,0.00012442975,0.00023395105,0.000038241244],"domain_scores_gemma":[0.9992506,0.00034858842,0.00008797505,0.00015312352,0.00012134468,0.000038312763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00074056035,0.0005042992,0.00024812078,0.00030599502,0.00037882943,0.0009054465,0.00073885685,0.0005698465,0.0031990265],"category_scores_gemma":[0.0025455651,0.00031173843,0.00056635943,0.00013649189,0.001684857,0.0013988954,0.0012202332,0.00086731877,0.00041568076],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037301172,0.0001700947,0.0011327314,0.0007619434,0.000047655354,0.0006744293,0.0032601936,0.24069284,0.19517782,0.42581573,0.002742701,0.1291509],"study_design_scores_gemma":[0.0000814448,0.00034441214,0.0006411178,0.00012208543,0.000028555995,0.00036504056,0.00035845948,0.7452835,0.034972932,0.1982987,0.019442547,0.00006126407],"about_ca_topic_score_codex":0.00057357154,"about_ca_topic_score_gemma":0.00078416994,"teacher_disagreement_score":0.0031990265,"about_ca_system_score_codex":0.000336983,"about_ca_system_score_gemma":0.00053452747,"threshold_uncertainty_score":0.010701835},"labels":[],"label_agreement":null},{"id":"W4376478934","doi":"10.1109/thms.2023.3265972","title":"Evaluation of Short-Range Depth Sonifications for Visual-to-Auditory Sensory Substitution","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Tactile and Sensory Interactions","field":"Neuroscience","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Sensory substitution; Sonification; Repetition (rhetorical device); Memorization; Azimuth; Auditory feedback; Noise (video); Range (aeronautics); Speech recognition; Reverberation; Task (project management); Acoustics; Binaural recording; Sensory system; Artificial intelligence; Mathematics; Human–computer interaction; Audiology; Psychology; Engineering","score_opus":0.20880716149997464,"score_gpt":0.41433443309065676,"score_spread":0.20552727159068213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376478934","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.986453,0.0004748132,0.012300758,0.000020562002,0.000034201483,0.00011678677,0.000044263936,0.00010335995,0.00045220874],"genre_scores_gemma":[0.96614957,0.00041086006,0.032460973,0.000026207304,0.00002311274,0.00010515985,0.00007462919,0.000030628693,0.0007187757],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99928707,0.00028536454,0.00008858883,0.00009055181,0.0001890663,0.000059392067],"domain_scores_gemma":[0.9948362,0.0036000262,0.00028444812,0.00024953898,0.000733195,0.00029655485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015337543,0.00066556054,0.00045561502,0.0005178514,0.00016972248,0.00039350582,0.0005317658,0.00060541346,0.0019004443],"category_scores_gemma":[0.009094116,0.00023343724,0.00030926426,0.00018107005,0.0004457348,0.00070786086,0.00063017855,0.000278414,0.00028251417],"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":[0.017431093,0.0021804296,0.010829011,0.0024723758,0.00024253868,0.00030795002,0.0016086842,0.009923092,0.634959,0.0005027052,0.0003476238,0.31919554],"study_design_scores_gemma":[0.0013453163,0.13652581,0.07198412,0.000281731,0.0010551938,0.0018355818,0.0019663782,0.05594746,0.72345066,0.0008354386,0.004530192,0.00024215659],"about_ca_topic_score_codex":0.000481277,"about_ca_topic_score_gemma":0.0005308216,"teacher_disagreement_score":0.0019004443,"about_ca_system_score_codex":0.00014303888,"about_ca_system_score_gemma":0.00025421943,"threshold_uncertainty_score":0.008111417},"labels":[],"label_agreement":null},{"id":"W4385977576","doi":"10.1109/thms.2023.3298309","title":"Gamification of Driver Distraction Feedback: A Simulator Study With Younger Drivers","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Toyota Collaborative Safety Research Center","keywords":"Distraction; Driving simulator; SAFER; Simulation; Human–computer interaction; Computer science; Distracted driving; Visual feedback; Video feedback; Psychology; Cognitive psychology; Computer security; Artificial intelligence","score_opus":0.0454609942191945,"score_gpt":0.3688490174998084,"score_spread":0.3233880232806139,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385977576","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.99970895,0.000013645874,0.00010200554,0.000008434628,0.0000014506014,0.00009087663,0.000011629867,0.000002156683,0.000060755916],"genre_scores_gemma":[0.99615866,0.00015868124,0.002037411,0.00006078747,0.000012736364,0.00055120146,0.00008591994,0.0000057016096,0.0009289544],"study_design_codex":"nonrandomized_trial","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991417,0.0003369865,0.00006059105,0.00012758026,0.00014905234,0.0001840951],"domain_scores_gemma":[0.9978257,0.0009139427,0.00018788941,0.00016313173,0.00038353348,0.0005257321],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003035957,0.00092231855,0.0009135568,0.0007087607,0.0005666996,0.000729922,0.0008061942,0.0008904634,0.0015621381],"category_scores_gemma":[0.005125577,0.00043007365,0.00088606705,0.00021313128,0.00047096153,0.00071494764,0.0006381494,0.0008240584,0.0004474355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.017093182,0.45508122,0.20876963,0.0014305431,0.0008766855,0.003066041,0.08835758,0.003573414,0.060692474,0.0005848283,0.0012886416,0.15918578],"study_design_scores_gemma":[0.0062937974,0.5824974,0.34726113,0.00014514868,0.0008938553,0.0012957397,0.025010109,0.00969418,0.018746391,0.00075503386,0.007145229,0.00026205793],"about_ca_topic_score_codex":0.0043569654,"about_ca_topic_score_gemma":0.0046620946,"teacher_disagreement_score":0.0043569654,"about_ca_system_score_codex":0.00048653732,"about_ca_system_score_gemma":0.00082305196,"threshold_uncertainty_score":0.016055882},"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":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044181094,0.0003600747,0.9505493,0.00021554153,0.00008301138,0.00010259769,0.00022850969,0.0029075334,0.0013722943],"genre_scores_gemma":[0.69299734,0.000236319,0.29884702,0.00045007208,0.00015675678,0.00034309956,0.0015665352,0.00018577356,0.0052170157],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992993,0.00014924697,0.000030070743,0.0003426386,0.0001096167,0.000069155154],"domain_scores_gemma":[0.9991678,0.00028482592,0.0001116088,0.00021761519,0.00017162338,0.00004657608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091571757,0.0010301931,0.0010550749,0.0006410433,0.0002078833,0.0005924826,0.0018392167,0.0010774051,0.001253978],"category_scores_gemma":[0.0025074184,0.00037690188,0.0008870993,0.0007073791,0.0005099932,0.00124574,0.001009448,0.0016530376,0.00096575724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003073457,0.0009234359,0.0051838825,0.000168858,0.00021550994,0.00014769634,0.00019540815,0.17980538,0.026887272,0.0026785256,0.0071178842,0.77636886],"study_design_scores_gemma":[0.000012070884,0.00007984536,0.001154122,0.000006903497,0.0000110471165,0.000043086326,0.000016377915,0.9915827,0.0046153767,0.0018108995,0.0006582043,0.000009383472],"about_ca_topic_score_codex":0.0017144172,"about_ca_topic_score_gemma":0.0032858944,"teacher_disagreement_score":0.0018392167,"about_ca_system_score_codex":0.00047302936,"about_ca_system_score_gemma":0.0005206289,"threshold_uncertainty_score":0.0048428774},"labels":[],"label_agreement":null},{"id":"W4399995429","doi":"10.1109/thms.2024.3408841","title":"Modeling Brake Perception Response Time in On-Road and Roadside Hazards Using an Integrated Cognitive Architecture","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Safety Warnings and Signage","field":"Psychology","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; Western University","funders":"","keywords":"Brake; Cognitive architecture; Perception; Cognition; Architecture; Automotive engineering; Response time; Computer science; Engineering; Psychology; Neuroscience; Geography","score_opus":0.05104347476449961,"score_gpt":0.3587444118349861,"score_spread":0.3077009370704865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399995429","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6879216,0.00006266296,0.30887544,0.0001524732,0.000048786056,0.00007467659,0.00008034598,0.0002388589,0.002545125],"genre_scores_gemma":[0.9873046,0.000025069941,0.011958274,0.000016622038,0.0000046701653,0.00003885472,0.000029766692,0.00000867895,0.00061344216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997689,0.000055176784,0.000010725394,0.00007439129,0.000037763904,0.0000530114],"domain_scores_gemma":[0.9993812,0.00030947232,0.00008032389,0.000046497877,0.00013073649,0.000051830786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000622044,0.00049118383,0.00029701146,0.00037896886,0.00033561495,0.000699055,0.00094205304,0.00064633385,0.0010248384],"category_scores_gemma":[0.0022868535,0.00030863305,0.0006115667,0.00024954163,0.0005730729,0.0007819903,0.000597619,0.0008346571,0.00009795096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00004987594,0.000054135067,0.0019155483,0.000008408238,0.00002183784,0.000021595983,0.000056921992,0.9920398,0.0017152118,0.0017425431,0.000037569178,0.0023365417],"study_design_scores_gemma":[0.0000037416685,0.0000135019345,0.0003403061,4.7217205e-7,0.0000040890277,0.0000027028912,0.0000049582763,0.9989262,0.0001498086,0.00052489963,0.000026495927,0.0000027893684],"about_ca_topic_score_codex":0.041642036,"about_ca_topic_score_gemma":0.024487274,"teacher_disagreement_score":0.041642036,"about_ca_system_score_codex":0.0014490044,"about_ca_system_score_gemma":0.001685456,"threshold_uncertainty_score":0.082799315},"labels":[],"label_agreement":null},{"id":"W4407098514","doi":"10.1109/thms.2025.3527397","title":"Global-Local Image Perceptual Score (GLIPS): Evaluating Photorealistic Quality of AI-Generated Images","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Visual Attention and Saliency Detection","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Perception; Artificial intelligence; Image quality; Computer vision; Computer science; Image (mathematics); Quality (philosophy); Computer graphics (images); Psychology","score_opus":0.0740158551259147,"score_gpt":0.408762644697055,"score_spread":0.3347467895711403,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407098514","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5371079,0.0016623883,0.43843767,0.00030952974,0.0002418899,0.0005875717,0.0009456253,0.001941379,0.018766124],"genre_scores_gemma":[0.91086596,0.00032527684,0.08639636,0.00009378002,0.000046428948,0.00011627131,0.0005475101,0.00018822134,0.0014201846],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99924755,0.00014183125,0.000043688535,0.000107856205,0.00041482717,0.00004433964],"domain_scores_gemma":[0.9967818,0.001324933,0.00045733518,0.00031989557,0.00089135475,0.00022461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013799183,0.00059352966,0.00032481004,0.0017108129,0.00021697044,0.0009919192,0.00045229107,0.00053788535,0.0025709635],"category_scores_gemma":[0.009334057,0.00012606772,0.0004038883,0.00060405093,0.00058957987,0.0012819367,0.0010182924,0.00047502926,0.0004705904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017453403,0.00029730986,0.03459472,0.0014193007,0.00041032437,0.00043908088,0.00095781236,0.035164483,0.2866148,0.010310356,0.0060593393,0.62198716],"study_design_scores_gemma":[0.00023088648,0.005523574,0.23218802,0.00025021782,0.00057709485,0.003621179,0.0015309629,0.4659621,0.2473281,0.024705991,0.017649755,0.00043210742],"about_ca_topic_score_codex":0.0008328016,"about_ca_topic_score_gemma":0.0010421015,"teacher_disagreement_score":0.0025709635,"about_ca_system_score_codex":0.0004672875,"about_ca_system_score_gemma":0.00026859448,"threshold_uncertainty_score":0.008600712},"labels":[],"label_agreement":null},{"id":"W4407937729","doi":"10.1109/thms.2025.3538098","title":"Time Series Signal Analysis With Information Granulation Based on Permutation Entropy: An Application to Electroencephalography Signals","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Neural Networks and Applications","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 Alberta","funders":"","keywords":"Electroencephalography; SIGNAL (programming language); Pattern recognition (psychology); Series (stratigraphy); Computer science; Entropy (arrow of time); Signal processing; Permutation (music); Artificial intelligence; Speech recognition; Data mining; Mathematics; Psychology; Neuroscience; Acoustics; Physics; Biology; Telecommunications","score_opus":0.0065784996221192,"score_gpt":0.2515574771403906,"score_spread":0.2449789775182714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407937729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0329767,0.00060258235,0.96483654,0.00017446853,0.00006427992,0.000054735276,0.00010973348,0.0004245936,0.0007564747],"genre_scores_gemma":[0.5460291,0.0008240527,0.45161906,0.0000946523,0.0002152697,0.000099424135,0.00029678986,0.00009915144,0.00072250323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993436,0.00013004306,0.000092005896,0.00013382119,0.0002569025,0.000043709402],"domain_scores_gemma":[0.9983309,0.00091174216,0.00024214237,0.00023756492,0.00021612138,0.0000614608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008929775,0.0005169676,0.0006647346,0.00214568,0.00027897756,0.0010386984,0.0003653451,0.0004165207,0.00073609914],"category_scores_gemma":[0.0036824725,0.00019306951,0.00074953894,0.0022460362,0.00068191125,0.0015838327,0.00077248446,0.0007192523,0.00015797144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004888211,0.00012500252,0.004720424,0.0004259087,0.00019856298,0.00047545825,0.00036212683,0.1268765,0.07691124,0.027003607,0.0022491284,0.7601633],"study_design_scores_gemma":[0.000019165687,0.00014471015,0.0057928762,0.000023589377,0.00006583612,0.00027976892,0.000060657778,0.9516055,0.017925985,0.020931536,0.0030958902,0.00005443762],"about_ca_topic_score_codex":0.0008102314,"about_ca_topic_score_gemma":0.00051764265,"teacher_disagreement_score":0.00214568,"about_ca_system_score_codex":0.00036374593,"about_ca_system_score_gemma":0.00028557618,"threshold_uncertainty_score":0.004722595},"labels":[],"label_agreement":null},{"id":"W4408859987","doi":"10.1109/thms.2025.3546515","title":"EEG Features to Quantify the NASA-TLX Factors of Cognitive Workload","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Electroencephalography; Workload; Cognition; Psychology; Computer science; Cognitive psychology; Artificial intelligence; Human–computer interaction; Cognitive science; Neuroscience","score_opus":0.04737216503554875,"score_gpt":0.40676266301146574,"score_spread":0.359390497975917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408859987","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.57450384,0.0024151977,0.40621665,0.00032953633,0.00027958423,0.00074132805,0.0044210884,0.0010461063,0.010046702],"genre_scores_gemma":[0.92523605,0.0011271776,0.070578136,0.00008871132,0.00014618506,0.00029589195,0.0012257611,0.00006124727,0.0012407862],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997346,0.000049227307,0.000027242531,0.000053528413,0.0001097738,0.00002554561],"domain_scores_gemma":[0.9991992,0.0002648663,0.00016400956,0.000071596995,0.00025957866,0.000040785748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047427003,0.0008848242,0.00027223403,0.0013465142,0.000111176385,0.00068268925,0.00020556142,0.00039337584,0.0015046269],"category_scores_gemma":[0.002682072,0.000093935334,0.00029238855,0.001215507,0.00020915632,0.0005434093,0.0003353762,0.00043515762,0.00036450024],"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":[0.0007044998,0.00041478337,0.12759565,0.0015416768,0.00041115473,0.000622078,0.0009581092,0.009103851,0.20246316,0.0027896985,0.004973154,0.6484222],"study_design_scores_gemma":[0.00007834818,0.0014499378,0.88283265,0.00025709157,0.00026988122,0.0016973288,0.0007954477,0.050942477,0.044744864,0.005957932,0.0108311465,0.00014290864],"about_ca_topic_score_codex":0.00081398996,"about_ca_topic_score_gemma":0.0012846048,"teacher_disagreement_score":0.0015046269,"about_ca_system_score_codex":0.00013076232,"about_ca_system_score_gemma":0.00019301522,"threshold_uncertainty_score":0.005033493},"labels":[],"label_agreement":null},{"id":"W4409154300","doi":"10.1109/thms.2025.3552231","title":"Cybersecurity Challenge Analysis of Work-From-Anywhere (WFA) and Recommendations Guided by a User Study","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Information and Cyber Security","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":"Work (physics); Computer science; Computer security; Engineering","score_opus":0.028050095648286373,"score_gpt":0.3125078055633413,"score_spread":0.2844577099150549,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409154300","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9955373,0.000045133183,0.00241752,0.00029462238,0.0000120167115,0.00021337946,0.00020804055,0.000087892375,0.0011841953],"genre_scores_gemma":[0.9915916,0.00009926832,0.0061792484,0.00016700097,0.000009090624,0.00042665735,0.000343676,0.000027530745,0.0011559312],"study_design_codex":"observational","study_design_gemma":"qualitative","domain_scores_codex":[0.9971378,0.0018199742,0.00020515699,0.0002418223,0.0003591899,0.0002360982],"domain_scores_gemma":[0.9808157,0.01229286,0.0012116185,0.001277466,0.0030523248,0.0013499449],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0048887194,0.0005731775,0.0005688534,0.001632583,0.0019236818,0.0022095123,0.0008152988,0.0012495702,0.0023265204],"category_scores_gemma":[0.019557375,0.0002924828,0.0005782308,0.00092466694,0.00081025873,0.003179886,0.0017897256,0.0012396796,0.0007491622],"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":[0.0013920681,0.0054733614,0.45827046,0.002137251,0.00015791492,0.0035585049,0.36453566,0.0032535743,0.013178415,0.0037758213,0.013704984,0.13056198],"study_design_scores_gemma":[0.00012443238,0.0059235366,0.3694001,0.0007441582,0.00017902223,0.0028010427,0.5214851,0.044664044,0.010884769,0.0030928229,0.040106818,0.0005940514],"about_ca_topic_score_codex":0.003029034,"about_ca_topic_score_gemma":0.005478321,"teacher_disagreement_score":0.0048887194,"about_ca_system_score_codex":0.0012277722,"about_ca_system_score_gemma":0.0005936564,"threshold_uncertainty_score":0.02585429},"labels":[],"label_agreement":null},{"id":"W4413155970","doi":"10.1109/thms.2025.3591550","title":"Dynamic Estimation of Mental Workload and Operator Accuracy for Time-Constrained Binary Classification Tasks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Human-Automation Interaction and Safety","field":"Psychology","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":"Polytechnique Montréal; McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Ministère de la Défense Nationale; Innovation for Defence Excellence and Security","keywords":"Workload; Operator (biology); Computer science; Binary number; Estimation; Binary classification; Bitwise operation; Artificial intelligence; Mathematics; Arithmetic; Programming language; Engineering; Support vector machine","score_opus":0.02869783594966975,"score_gpt":0.3842877530317126,"score_spread":0.35558991708204285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413155970","genre_codex":"empirical","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":"empirical","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9544629,0.00013011937,0.04400296,0.00006078418,0.000021217433,0.00004657166,0.0001861579,0.0001157384,0.00097375334],"genre_scores_gemma":[0.99008125,0.00004524344,0.009327079,0.0000130364115,0.000011726568,0.000030569277,0.000290715,0.000010949602,0.00018938252],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9983581,0.0004170347,0.00016649306,0.00033440944,0.00059455156,0.00012935343],"domain_scores_gemma":[0.983677,0.010191699,0.0025222588,0.0012497541,0.0020136624,0.00034568837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002059256,0.00058529526,0.0003492291,0.00090467,0.00024553447,0.0009062932,0.0004952302,0.0005370759,0.0007718303],"category_scores_gemma":[0.027251553,0.00017277505,0.0002820354,0.0007397668,0.00031765806,0.0009577454,0.0006311544,0.00046386602,0.00016818062],"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":[0.0031247924,0.0011304412,0.45480973,0.00047540822,0.00040740872,0.00020871343,0.0022873585,0.14968678,0.05556804,0.0016561804,0.0013473894,0.32929775],"study_design_scores_gemma":[0.00004031222,0.0010949584,0.5470821,0.00005706695,0.000087305976,0.00019867138,0.0005809743,0.4358679,0.013006411,0.0012971115,0.00057210407,0.00011506368],"about_ca_topic_score_codex":0.0037838393,"about_ca_topic_score_gemma":0.0035499714,"teacher_disagreement_score":0.0037838393,"about_ca_system_score_codex":0.000422495,"about_ca_system_score_gemma":0.00037452666,"threshold_uncertainty_score":0.010890484},"labels":[],"label_agreement":null},{"id":"W4413344168","doi":"10.1109/thms.2025.3591603","title":"Role-Based Human-Machine Collaboration Task-Allocation Strategy in Multiagent Environment","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Collaboration in agile enterprises","field":"Business, Management and Accounting","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":"Nipissing University","funders":"National Natural Science Foundation of China","keywords":"Task (project management); Computer science; Human–computer interaction; Process management; Knowledge management; Distributed computing; Business; Systems engineering; Engineering","score_opus":0.017943066818514927,"score_gpt":0.27489252771407613,"score_spread":0.2569494608955612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413344168","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06598276,0.0004329337,0.927493,0.0004872797,0.000064835695,0.00014951562,0.000060943752,0.00038351963,0.004945291],"genre_scores_gemma":[0.898093,0.00017946454,0.098894835,0.00010921072,0.000031274067,0.00020351383,0.000070184884,0.000042353615,0.002376203],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984206,0.00065663015,0.00006876462,0.000378513,0.00022307374,0.0002523946],"domain_scores_gemma":[0.9983925,0.00067095435,0.00019327742,0.00018974853,0.0002158513,0.0003376007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017612659,0.0011432916,0.000996227,0.00046652506,0.00088133663,0.0012738609,0.0020252494,0.0012685409,0.0024078202],"category_scores_gemma":[0.003158869,0.00040364757,0.0007006246,0.00049562927,0.000867116,0.0018497953,0.0021188396,0.0010649312,0.00037444866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023817964,0.00015511095,0.0011526915,0.00019513228,0.00006733315,0.00030673592,0.00033724162,0.90865064,0.006218219,0.033627518,0.0025357378,0.04651556],"study_design_scores_gemma":[0.000015753405,0.000041186413,0.00014957778,0.0000047670283,0.000009949971,0.00003109522,0.000051558538,0.9892508,0.0005415681,0.009293859,0.0006018318,0.000008136013],"about_ca_topic_score_codex":0.0033227452,"about_ca_topic_score_gemma":0.0025081723,"teacher_disagreement_score":0.0033227452,"about_ca_system_score_codex":0.0009047458,"about_ca_system_score_gemma":0.0014019262,"threshold_uncertainty_score":0.009314597},"labels":[],"label_agreement":null},{"id":"W4416429257","doi":"10.1109/thms.2025.3627893","title":"Adaptive Weighted Federated Domain Adaptation Methods for Nonintrusive Load Monitoring","year":2025,"lang":"","type":"article","venue":"IEEE Transactions on Human-Machine Systems","topic":"Smart Grid Energy Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Weighting; Scalability; Adaptation (eye); Domain (mathematical analysis); Context (archaeology); Similarity (geometry); Raw data; Coding (social sciences); Similarity measure","score_opus":0.03830626402564625,"score_gpt":0.3343307092553955,"score_spread":0.29602444522974924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416429257","genre_codex":"methods","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":"methods","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014499125,0.0001791888,0.9836105,0.000096120806,0.00003099971,0.000017460185,0.000034142275,0.0010328749,0.0004996048],"genre_scores_gemma":[0.66090596,0.00019979489,0.33550754,0.00025020444,0.00007170535,0.000108091204,0.00027323523,0.00023260046,0.0024507684],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911517,0.00025848474,0.0000429673,0.00024143388,0.000259638,0.00008221944],"domain_scores_gemma":[0.99827886,0.0006998887,0.00019532283,0.00046112243,0.00029326353,0.00007168189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001464762,0.0006913959,0.0009678952,0.00046716057,0.00035685734,0.00081419904,0.0017783856,0.0007691313,0.001240051],"category_scores_gemma":[0.0048609287,0.00032255828,0.0006046961,0.0006554086,0.0007460557,0.0019976231,0.0015091836,0.0017050722,0.00048078437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002915836,0.00021767977,0.0022832495,0.000077379795,0.00010039698,0.00011188425,0.00018720028,0.613415,0.011761821,0.010641819,0.003598241,0.35731375],"study_design_scores_gemma":[0.0000054311517,0.000013747229,0.00012201314,0.0000029314238,0.0000032786334,0.00001897847,0.0000092409555,0.9939401,0.0017900604,0.0036148785,0.00047357706,0.000005771647],"about_ca_topic_score_codex":0.0030540614,"about_ca_topic_score_gemma":0.003280065,"teacher_disagreement_score":0.0030540614,"about_ca_system_score_codex":0.0006748424,"about_ca_system_score_gemma":0.00094431854,"threshold_uncertainty_score":0.0077465177},"labels":[],"label_agreement":null}]}