{"meta":{"query_hash":"deb88c3382a2","filters":{"topic":"Video Surveillance and Tracking Methods"},"cohort_total":962,"direct_labels_cover":1,"predictions_cover":962,"exported":962,"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/deb88c3382a2","api":"https://metacan.xera.ac/api/v1/cohort?topic=Video+Surveillance+and+Tracking+Methods"},"results":[{"id":"W10002024","doi":"10.1007/978-1-4020-8735-6_6","title":"A Multi-Camera Active-Vision System for Dynamic Form Recognition","year":2008,"lang":"en","type":"book-chapter","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Viewpoints; Active vision; Visibility; Computer vision; Control reconfiguration; Artificial intelligence; Computer science; Geography; Embedded system","score_opus":0.05655677562399123,"score_gpt":0.30933332371652067,"score_spread":0.2527765480925294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W10002024","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":[0.0020511006,0.000688278,0.982531,0.00007117256,0.00018903553,0.00012626007,0.00027445634,0.0055998242,0.008468896],"genre_scores_gemma":[0.03070599,0.00093995273,0.93778473,0.0002635402,0.00009771151,0.00018927768,0.00078572996,0.0003935317,0.028839584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999731,0.000019045947,0.000010751244,0.00007167042,0.00014908324,0.000018489669],"domain_scores_gemma":[0.99983907,0.000029725688,0.000007243955,0.000039453666,0.00006878644,0.00001563759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002829344,0.0007992896,0.00076901296,0.0008856049,0.00038509487,0.0010316294,0.0017072505,0.001207456,0.020917265],"category_scores_gemma":[0.00040017613,0.0005910644,0.0005396573,0.00097125216,0.00024014185,0.0012639643,0.00081233954,0.00088070653,0.012519693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011686846,0.000084112595,0.00017175586,0.0001724873,0.000034316203,0.000075845324,0.000063714186,0.002359867,0.13017535,0.005489213,0.02047756,0.8407789],"study_design_scores_gemma":[0.00010828185,0.0004707978,0.0035186997,0.00015575405,0.00018847786,0.0032356365,0.00008741036,0.39286533,0.2807457,0.011032244,0.30740917,0.00018252333],"about_ca_topic_score_codex":0.0012005137,"about_ca_topic_score_gemma":0.0023732337,"teacher_disagreement_score":0.020917265,"about_ca_system_score_codex":0.00025388345,"about_ca_system_score_gemma":0.00039029677,"threshold_uncertainty_score":0.06997526},"labels":[],"label_agreement":null},{"id":"W112792529","doi":"10.1007/978-3-642-21593-3_39","title":"Maneuvering Head Motion Tracking by Coarse-to-Fine Particle Filter","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Particle filter; Tracking (education); Computer vision; Robustness (evolution); Weighting; Artificial intelligence; Tracking system; Filter (signal processing); Algorithm; Acoustics; Physics","score_opus":0.05050819503096862,"score_gpt":0.28693180873425234,"score_spread":0.2364236137032837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W112792529","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":[0.0058118864,0.00025823253,0.99179417,0.000042795105,0.00009405694,0.000022531614,0.00005047485,0.0007561072,0.0011697367],"genre_scores_gemma":[0.36092684,0.0007561616,0.62961847,0.000168858,0.00012758291,0.000093082264,0.0005381625,0.0001975485,0.0075734193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978524,0.000022751898,0.000011077102,0.000074133364,0.000075314165,0.000031425654],"domain_scores_gemma":[0.9997565,0.000069298134,0.000020726744,0.00005984741,0.00007779074,0.000015724601],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035377606,0.0007280501,0.0010652833,0.00060169137,0.0003714982,0.000568477,0.00069099374,0.0008173294,0.0015722989],"category_scores_gemma":[0.0010118898,0.00048406888,0.00069821975,0.00092639984,0.00035301427,0.0006338333,0.00091656914,0.0008734884,0.0009565704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031255992,0.00008071223,0.0015228277,0.00015750238,0.00012940109,0.0001385869,0.00011946139,0.26031718,0.058896195,0.0040738313,0.0060636993,0.66818804],"study_design_scores_gemma":[0.000008772296,0.000031309813,0.0009609619,0.0000060762986,0.00002072261,0.00008049409,0.00000939885,0.98923546,0.0066497177,0.0016031588,0.0013809764,0.000012927574],"about_ca_topic_score_codex":0.0072962437,"about_ca_topic_score_gemma":0.0064333514,"teacher_disagreement_score":0.0072962437,"about_ca_system_score_codex":0.00031941547,"about_ca_system_score_gemma":0.00077128434,"threshold_uncertainty_score":0.014507532},"labels":[],"label_agreement":null},{"id":"W132735772","doi":"","title":"Feature-based tracking of multiple people for intelligent video surveillance.","year":2006,"lang":"en","type":"article","venue":"Scholarship at UWindsor (University of Windsor)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Feature (linguistics); Tracking (education); Artificial intelligence; Computer vision; Video tracking; Video processing","score_opus":0.025114136431372523,"score_gpt":0.2465830806768856,"score_spread":0.22146894424551308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W132735772","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":[0.011729442,0.0014443587,0.9830458,0.00018109691,0.0001476685,0.000074805204,0.00017590674,0.0012165684,0.0019843671],"genre_scores_gemma":[0.18555422,0.0018253556,0.8067639,0.00016288001,0.000115445044,0.00016333547,0.00091793353,0.00012385138,0.0043731495],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99957556,0.00008367055,0.000017375334,0.00013058499,0.00015925894,0.000033478063],"domain_scores_gemma":[0.99955887,0.00014101334,0.00007604386,0.00007229521,0.00011908615,0.00003273539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070855365,0.00051942864,0.00057868904,0.0010821038,0.00041917415,0.0006576201,0.00089343585,0.00073559314,0.0021577228],"category_scores_gemma":[0.0014544533,0.0002709869,0.00056934054,0.0009075635,0.0002680314,0.0008973069,0.00052414375,0.0005247934,0.001000645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024318468,0.00012796943,0.002345009,0.0003409135,0.00012069173,0.0001951939,0.000216802,0.018890629,0.10044245,0.008376792,0.015302219,0.8533982],"study_design_scores_gemma":[0.00007881908,0.00035632864,0.009990541,0.00019815718,0.00016951706,0.0014239491,0.00015667826,0.815533,0.09886062,0.0131258555,0.0600204,0.000086187305],"about_ca_topic_score_codex":0.0024818326,"about_ca_topic_score_gemma":0.0038945838,"teacher_disagreement_score":0.0024818326,"about_ca_system_score_codex":0.00051168277,"about_ca_system_score_gemma":0.000571749,"threshold_uncertainty_score":0.0072183013},"labels":[],"label_agreement":null},{"id":"W135359125","doi":"10.1007/978-3-642-21593-3_44","title":"Event Detection and Recognition using Histogram of Oriented Gradients and Hidden Markov Models","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Hidden Markov model; Histogram; Artificial intelligence; Event (particle physics); Histogram of oriented gradients; Computer vision; Set (abstract data type); Pattern recognition (psychology); Motion (physics); Markov chain; Object (grammar); Cognitive neuroscience of visual object recognition; Image (mathematics); Machine learning","score_opus":0.04596232009491684,"score_gpt":0.2693375622490113,"score_spread":0.22337524215409443,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W135359125","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":[0.009513024,0.0005606425,0.9871875,0.00005970465,0.00005181979,0.000032718253,0.00015138712,0.0016553915,0.00078784215],"genre_scores_gemma":[0.28148085,0.0012777486,0.71004397,0.00013111369,0.000117628566,0.00010448112,0.0011912727,0.00026951317,0.0053833616],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999666,0.00004567647,0.000020397954,0.00010386089,0.00011230576,0.000051902203],"domain_scores_gemma":[0.9995203,0.00023507581,0.000047409132,0.000063275525,0.00010641683,0.000027499362],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060757104,0.0006991646,0.0012019428,0.001266173,0.00025125218,0.001072008,0.0010217923,0.00060882635,0.0017082259],"category_scores_gemma":[0.0013010597,0.0005538049,0.00090829976,0.0011798813,0.00033108666,0.0011251235,0.0006220393,0.0007504527,0.0014630508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022424091,0.00016055974,0.0023500102,0.0001231397,0.000093232426,0.00011198854,0.00005129361,0.04826437,0.025604442,0.005693203,0.0051665995,0.912157],"study_design_scores_gemma":[0.000015513886,0.000050098122,0.0023747354,0.000016948225,0.00004084614,0.0001474537,0.000022757466,0.96835685,0.01756542,0.0095101,0.001870629,0.000028667268],"about_ca_topic_score_codex":0.0042287596,"about_ca_topic_score_gemma":0.0058913594,"teacher_disagreement_score":0.0042287596,"about_ca_system_score_codex":0.00043168972,"about_ca_system_score_gemma":0.0005251609,"threshold_uncertainty_score":0.008408308},"labels":[],"label_agreement":null},{"id":"W137855968","doi":"10.3141/2528-06","title":"Automated Region-Based Vehicle Conflict Detection Using Computer Vision Techniques","year":2015,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Cuboid; Representation (politics); Background subtraction; Computer science; Computer vision; Artificial intelligence; Subtraction; Point (geometry); Vehicle tracking system; Feature (linguistics); Set (abstract data type); Trajectory; Conflict analysis; Traffic conflict; Conflict resolution; Engineering; Mathematics; Traffic congestion; Pixel; Segmentation; Transport engineering","score_opus":0.19349789013660576,"score_gpt":0.44493917417268053,"score_spread":0.2514412840360748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W137855968","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07854368,0.0003868325,0.91799337,0.00003610639,0.000022336868,0.00008185008,0.00014392617,0.0015121314,0.0012797525],"genre_scores_gemma":[0.49903202,0.00030127683,0.49923387,0.00003376883,0.000025440531,0.00008800893,0.00040493274,0.00007096991,0.0008095466],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99942017,0.0001125819,0.00002655959,0.00016471019,0.0002043547,0.00007171219],"domain_scores_gemma":[0.9993734,0.00017439737,0.00011893525,0.0000724524,0.00022907055,0.00003183344],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004686617,0.0005211795,0.0006423827,0.0032848062,0.00033224298,0.0007623248,0.0007370987,0.00046415487,0.00065455626],"category_scores_gemma":[0.0011974524,0.00031699098,0.00055705546,0.0016731843,0.00027757965,0.0008281261,0.0005236177,0.00047431348,0.00049627834],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032897707,0.00022915368,0.0058369725,0.000112963964,0.00013015619,0.00021799078,0.00017259413,0.083615355,0.14517967,0.002716271,0.0017308691,0.7597291],"study_design_scores_gemma":[0.000019026986,0.00014707002,0.00908387,0.00001650689,0.00003234825,0.00031796435,0.00008369176,0.947909,0.03865355,0.0019079108,0.0017860713,0.000042981348],"about_ca_topic_score_codex":0.006111953,"about_ca_topic_score_gemma":0.0041657714,"teacher_disagreement_score":0.006111953,"about_ca_system_score_codex":0.00050513365,"about_ca_system_score_gemma":0.0005801368,"threshold_uncertainty_score":0.012152731},"labels":[],"label_agreement":null},{"id":"W1486005927","doi":"","title":"Integrated subsystem for Obstacle detection from a belt of micro-cameras","year":2009,"lang":"en","type":"article","venue":"International Conference on Advanced Robotics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Canadian Nautical Research Society","funders":"","keywords":"Computer vision; Occupancy grid mapping; Artificial intelligence; Computer science; Obstacle; Mobile robot; Robot; Pixel; Geography","score_opus":0.05237526766791824,"score_gpt":0.3293643129725612,"score_spread":0.27698904530464297,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1486005927","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047301076,0.0006499564,0.9415071,0.00008143464,0.0001486101,0.00034556474,0.00020746117,0.006284023,0.0034747734],"genre_scores_gemma":[0.45691392,0.00052958104,0.5271878,0.000207406,0.000095239484,0.00048600472,0.00083006715,0.00021069156,0.013539285],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994997,0.000032436743,0.000017465934,0.00011224201,0.00027543455,0.00006268897],"domain_scores_gemma":[0.99957746,0.000055836943,0.00003381076,0.000077591736,0.00020493784,0.00005026703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038304177,0.0007030579,0.00068238325,0.0007500388,0.0002731729,0.00074601796,0.0019722173,0.0006375524,0.007049051],"category_scores_gemma":[0.0005963916,0.00039979978,0.0004328992,0.00033448995,0.00020131834,0.00085502764,0.00068727875,0.00066146033,0.0021533833],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079413713,0.00041442193,0.0047766496,0.00061160675,0.00021208305,0.0002635128,0.00027448515,0.010152442,0.46567863,0.0031004036,0.0056494973,0.50807214],"study_design_scores_gemma":[0.0002439657,0.0026278784,0.025249783,0.00019331198,0.00046577863,0.0015576467,0.00024238502,0.4337196,0.47413194,0.0016192538,0.059776403,0.00017203594],"about_ca_topic_score_codex":0.0024305196,"about_ca_topic_score_gemma":0.0034324196,"teacher_disagreement_score":0.007049051,"about_ca_system_score_codex":0.0006334571,"about_ca_system_score_gemma":0.0008014143,"threshold_uncertainty_score":0.023581445},"labels":[],"label_agreement":null},{"id":"W1488075530","doi":"10.1109/fg.2015.7163097","title":"A hierarchical training and identification method using Gaussian process models for face recognition in videos","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Northern British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Facial recognition system; Pattern recognition (psychology); Classifier (UML); Noise (video); Sequence (biology); Computer vision; Machine learning; Image (mathematics)","score_opus":0.25709130074107306,"score_gpt":0.41523598379482857,"score_spread":0.1581446830537555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1488075530","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":[0.010042238,0.00014866347,0.9887327,0.000042064068,0.000015066948,0.0000343518,0.000026794676,0.0007109064,0.0002473132],"genre_scores_gemma":[0.34357995,0.00031559245,0.65210474,0.00019828252,0.000073802985,0.00019484705,0.0004412287,0.0001452464,0.002946389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991499,0.00019129424,0.000039263177,0.00031568308,0.00020035154,0.00010344943],"domain_scores_gemma":[0.9994128,0.0002085308,0.00006724068,0.000105423496,0.00016588432,0.00004022625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013626043,0.0007500398,0.0011594876,0.0010180795,0.00048705516,0.00047915333,0.001795104,0.0010667279,0.0011371727],"category_scores_gemma":[0.0024471611,0.0004911815,0.0013449827,0.0008739558,0.00045562288,0.0009424305,0.00087955507,0.0015241709,0.00081581366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018641906,0.00017093123,0.001465119,0.000060714374,0.000113304406,0.00009024494,0.00016203243,0.26835054,0.018437611,0.00445319,0.0021932293,0.7043167],"study_design_scores_gemma":[0.0000031952047,0.000034835793,0.00022611095,0.0000024964263,0.000008050505,0.000020798427,0.0000062924664,0.99714005,0.0014475537,0.00084347744,0.00026161954,0.000005643339],"about_ca_topic_score_codex":0.012432235,"about_ca_topic_score_gemma":0.0126573555,"teacher_disagreement_score":0.012432235,"about_ca_system_score_codex":0.00080312334,"about_ca_system_score_gemma":0.0011262172,"threshold_uncertainty_score":0.024719775},"labels":[],"label_agreement":null},{"id":"W1489254256","doi":"10.1007/978-3-540-74827-4_127","title":"Shadow Removal Method for Real-Time Extraction of Moving Objects","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Background subtraction; Artificial intelligence; RGB color model; Color space; Shadow (psychology); Computer graphics (images); Color image; Image processing; Image (mathematics); Pixel","score_opus":0.03639312889578386,"score_gpt":0.340562559295409,"score_spread":0.3041694303996252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1489254256","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":[0.017633656,0.0013006496,0.97675294,0.000061276776,0.00017487921,0.000044204502,0.00012934282,0.0017078847,0.0021951979],"genre_scores_gemma":[0.14743754,0.0023717822,0.82981825,0.00014902903,0.00014807998,0.00008397526,0.00096972316,0.00046231435,0.01855933],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997652,0.000012509039,0.000012053,0.000060233975,0.00011673977,0.000033152723],"domain_scores_gemma":[0.9997811,0.00004873811,0.000018185237,0.000040917133,0.00009659391,0.00001455739],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026521183,0.00073928264,0.0007234142,0.001107128,0.0004001603,0.0005587251,0.00077804446,0.0006194323,0.0042168614],"category_scores_gemma":[0.00042066444,0.00046361558,0.00072493806,0.0010291713,0.00024944887,0.0006917294,0.0004971078,0.0006047431,0.0025748953],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001990228,0.000056595323,0.00039549885,0.00021220364,0.000048257778,0.0001514909,0.00009864585,0.002456711,0.31336713,0.001110767,0.0042948606,0.67760885],"study_design_scores_gemma":[0.000055639724,0.00021470437,0.009343016,0.00005540727,0.00024799036,0.0021308756,0.00011613856,0.27593815,0.667566,0.0021725204,0.042068228,0.000091375354],"about_ca_topic_score_codex":0.0016258988,"about_ca_topic_score_gemma":0.0031147418,"teacher_disagreement_score":0.0042168614,"about_ca_system_score_codex":0.0002190434,"about_ca_system_score_gemma":0.0006191016,"threshold_uncertainty_score":0.0141067505},"labels":[],"label_agreement":null},{"id":"W1493578551","doi":"10.1109/iembs.2006.260647","title":"Video Surveillance of Medication Intake","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université de Montréal; Vale (Canada)","funders":"","keywords":"Computer science; Context (archaeology); Computer vision; Artificial intelligence; Video monitoring; Population; Multimedia; Real-time computing; Medicine; Environmental health","score_opus":0.0135051063396352,"score_gpt":0.2659063707957445,"score_spread":0.2524012644561093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1493578551","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":[0.7902187,0.0024718423,0.1856014,0.0003702601,0.0002182109,0.00034206416,0.004025872,0.0034022662,0.013349342],"genre_scores_gemma":[0.9402734,0.0008526375,0.052824136,0.00015039228,0.00011049508,0.0001002079,0.0026705493,0.000054218675,0.0029639583],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972993,0.000040209015,0.000012321472,0.000066956156,0.00012376535,0.000026930415],"domain_scores_gemma":[0.99956983,0.00010378764,0.00006471691,0.000032571174,0.00017847658,0.000050599858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002035168,0.00029244376,0.00036026398,0.00061885495,0.00015093137,0.00032317356,0.00030873253,0.0004906565,0.0010763525],"category_scores_gemma":[0.00089319964,0.00010095842,0.0001976686,0.00046255972,0.00006831204,0.0002480801,0.00021938683,0.00027175323,0.00032243587],"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":[0.0017664453,0.00056355755,0.04888276,0.0006347342,0.0001708914,0.00083691214,0.00038134318,0.012847837,0.4828855,0.0008803588,0.008673852,0.44147578],"study_design_scores_gemma":[0.00020655456,0.002641596,0.38215646,0.00011988574,0.0002470009,0.0028139749,0.0004022367,0.31037676,0.27886093,0.00077138393,0.021284258,0.00011900089],"about_ca_topic_score_codex":0.0043978263,"about_ca_topic_score_gemma":0.004209565,"teacher_disagreement_score":0.0043978263,"about_ca_system_score_codex":0.00022778884,"about_ca_system_score_gemma":0.00027917634,"threshold_uncertainty_score":0.008744478},"labels":[],"label_agreement":null},{"id":"W1496969825","doi":"10.1109/igarss.2004.1370740","title":"Detecting information under and from shadow in panchromatic Ikonos images of the city of Sherbrooke","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Panchromatic film; Shadow (psychology); Shadow mapping; Zenith; Computer vision; Specular reflection; Position (finance); Computer science; Geology; Artificial intelligence; Geography; Remote sensing; Image resolution; Physics; Optics","score_opus":0.015567787957300527,"score_gpt":0.2456600966183946,"score_spread":0.2300923086610941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1496969825","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":[0.9964141,0.00006647614,0.000668857,0.000042304157,0.0000057727298,0.000013007634,0.0007759735,0.00006459706,0.0019487955],"genre_scores_gemma":[0.9962115,0.000090968206,0.002094518,0.000012846197,0.000008385673,0.0000047814497,0.0010405361,0.000012090972,0.00052427925],"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9999051,0.00000637636,0.0000031520628,0.0000164074,0.00003551066,0.000033421307],"domain_scores_gemma":[0.9998307,0.000023777402,0.00002709378,0.000019583564,0.00006188983,0.000037016493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000106425825,0.00025051186,0.0002270581,0.0016969928,0.00033654203,0.00054054457,0.00023043662,0.00022406895,0.00079557457],"category_scores_gemma":[0.00039622243,0.00017705414,0.00012368106,0.0012575634,0.0002715582,0.00021760998,0.00027024947,0.00023286729,0.00031805172],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021610959,0.000347707,0.5243075,0.00053089263,0.00021582383,0.006016252,0.00416227,0.010018432,0.28010264,0.0006147299,0.007129577,0.16439311],"study_design_scores_gemma":[0.000018124023,0.00002773257,0.9819271,0.000014335395,0.000039536706,0.00030328403,0.0012408268,0.0061069033,0.009166787,0.000042328193,0.0010937072,0.000019428817],"about_ca_topic_score_codex":0.10014624,"about_ca_topic_score_gemma":0.26180473,"teacher_disagreement_score":0.89985377,"about_ca_system_score_codex":0.0005336262,"about_ca_system_score_gemma":0.0004045476,"threshold_uncertainty_score":0.19912666},"labels":[],"label_agreement":null},{"id":"W1499997787","doi":"10.1109/ccece.2015.7129491","title":"Visual tracking based on compressive sensing and particle filter","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Particle filter; Robustness (evolution); Artificial intelligence; Computer science; Computer vision; Active appearance model; Tracking (education); Compressed sensing; Eye tracking; Classifier (UML); Pattern recognition (psychology); Filter (signal processing); Image (mathematics)","score_opus":0.07694932452341158,"score_gpt":0.3353905339871005,"score_spread":0.2584412094636889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1499997787","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037398727,0.00022635347,0.9947647,0.00008550091,0.000056416473,0.000020498017,0.000011649928,0.00017485236,0.0009201994],"genre_scores_gemma":[0.44074795,0.001245267,0.5536434,0.00027101833,0.00027696084,0.00017734081,0.00017939068,0.000053655727,0.003405101],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994116,0.00008633642,0.000033525735,0.00014802493,0.00028339252,0.00003715961],"domain_scores_gemma":[0.9993424,0.0002779193,0.00010211499,0.000075255964,0.00016664068,0.00003568447],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065522187,0.0005590242,0.00075399707,0.00072882377,0.00040266177,0.0006272114,0.00079178537,0.00092004356,0.0006462307],"category_scores_gemma":[0.0024634665,0.00034091764,0.0006780405,0.0008476389,0.00056291884,0.001122191,0.0007629422,0.0008293379,0.00023328196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016674219,0.0001270394,0.0015741341,0.00019227705,0.00010811924,0.00022226981,0.00018563564,0.48125556,0.04368351,0.036640994,0.0034863628,0.4323573],"study_design_scores_gemma":[0.000009238388,0.00003393339,0.00023553094,0.0000060186185,0.000007713039,0.00007380973,0.000005219082,0.99386394,0.0023170155,0.00257496,0.00086037663,0.000012208403],"about_ca_topic_score_codex":0.0051066917,"about_ca_topic_score_gemma":0.003052437,"teacher_disagreement_score":0.0051066917,"about_ca_system_score_codex":0.0005097521,"about_ca_system_score_gemma":0.0007160157,"threshold_uncertainty_score":0.010153949},"labels":[],"label_agreement":null},{"id":"W1506047331","doi":"10.1109/crv.2015.18","title":"RKLT: 8 DOF Real-Time Robust Video Tracking Combing Coarse Ransac Features and Accurate Fast Template Registration","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"RANSAC; Computer vision; Artificial intelligence; Computer science; BitTorrent tracker; Robustness (evolution); Video tracking; Outlier; Active appearance model; Motion estimation; Bundle adjustment; Eye tracking; Object (grammar); Image (mathematics)","score_opus":0.0610484374047508,"score_gpt":0.3000318064427855,"score_spread":0.2389833690380347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1506047331","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":[0.0026912326,0.000079781676,0.9919206,0.000023453735,0.000035765537,0.00005307094,0.000045690274,0.0044293976,0.0007210201],"genre_scores_gemma":[0.09806444,0.00010926506,0.89657617,0.00010137528,0.000055862824,0.0001508446,0.000510634,0.00041066782,0.0040207705],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9984837,0.00013277934,0.0000650348,0.00037856185,0.00082361937,0.00011626647],"domain_scores_gemma":[0.9991166,0.000081938866,0.00015523884,0.0003503877,0.0002302042,0.0000656691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011138099,0.0011447879,0.0014148025,0.0012131655,0.00045089977,0.0012919571,0.0024017433,0.0012615583,0.0034572706],"category_scores_gemma":[0.0018965272,0.0006133687,0.0007411859,0.0010357699,0.0005616971,0.001470471,0.0018088121,0.0012180795,0.0054384726],"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":[0.00023848718,0.0002010245,0.0012433167,0.00017558114,0.00006461234,0.00023712734,0.00011633772,0.05613096,0.10792055,0.0048430753,0.010909497,0.81791943],"study_design_scores_gemma":[0.000038511844,0.00017698492,0.0010238271,0.000017460507,0.000016810209,0.00041437926,0.000016440887,0.9448727,0.041249514,0.0015625324,0.010556655,0.000054280008],"about_ca_topic_score_codex":0.0025192196,"about_ca_topic_score_gemma":0.002807024,"teacher_disagreement_score":0.0034572706,"about_ca_system_score_codex":0.00058873586,"about_ca_system_score_gemma":0.0011166029,"threshold_uncertainty_score":0.011565745},"labels":[],"label_agreement":null},{"id":"W1507702167","doi":"10.1007/11821045_5","title":"Iterative Division and Correlograms for Detection and Tracking of Moving Objects","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Histogram; Tracking (education); Division (mathematics); Object detection; Video tracking; Object (grammar); Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.019879927569185607,"score_gpt":0.27209534646472183,"score_spread":0.2522154188955362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1507702167","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":[0.0030904564,0.00046121894,0.9954301,0.00002333701,0.00002858751,0.000015301574,0.000020736285,0.00047916203,0.00045112858],"genre_scores_gemma":[0.037129655,0.00053910696,0.9599964,0.000034514444,0.000046657264,0.000100523015,0.00010413051,0.00017404386,0.0018749024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990932,0.00023535416,0.00006595929,0.00014912176,0.0003839782,0.00007243402],"domain_scores_gemma":[0.99714017,0.0016803815,0.00016299386,0.00041910616,0.00053581264,0.00006157407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013077186,0.0009741005,0.0012119776,0.0022621707,0.00058192416,0.0010686483,0.0015948208,0.0011762853,0.002867025],"category_scores_gemma":[0.006450724,0.00091434317,0.0007086165,0.0036311704,0.0009387208,0.0015643225,0.0011574698,0.0011081405,0.0014048236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031454017,0.00006311644,0.0005538677,0.00015106764,0.000070579415,0.00009770325,0.00018012259,0.06493921,0.02256127,0.029155001,0.0045422255,0.8773714],"study_design_scores_gemma":[0.000030949876,0.00005089781,0.00063709967,0.000021788297,0.0000436085,0.0002296463,0.000030504903,0.9571359,0.020156812,0.017695205,0.0039327736,0.000034768524],"about_ca_topic_score_codex":0.005091437,"about_ca_topic_score_gemma":0.0063736974,"teacher_disagreement_score":0.005091437,"about_ca_system_score_codex":0.00071437005,"about_ca_system_score_gemma":0.0012770072,"threshold_uncertainty_score":0.0101236105},"labels":[],"label_agreement":null},{"id":"W1511840314","doi":"10.1109/icra.2015.7139526","title":"Tracking benchmark and evaluation for manipulation tasks","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"BitTorrent tracker; Computer science; Ground truth; Benchmark (surveying); Metric (unit); Artificial intelligence; Tracking (education); Computer vision; Process (computing); Video tracking; Scripting language; Face (sociological concept); Sensitivity (control systems); Object (grammar); Eye tracking; Engineering","score_opus":0.19097001699730684,"score_gpt":0.3899478782084224,"score_spread":0.19897786121111558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1511840314","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27356452,0.024529327,0.31377035,0.0016712652,0.006666051,0.006594695,0.17048019,0.14101967,0.06170394],"genre_scores_gemma":[0.23089497,0.0028134338,0.20228346,0.0008894732,0.0005492596,0.0023622294,0.53892976,0.004123982,0.017153384],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99140024,0.0015463417,0.0010808277,0.0022409575,0.003038179,0.0006935849],"domain_scores_gemma":[0.9912116,0.002077287,0.0007317798,0.002117777,0.0031582303,0.000703374],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006052153,0.0036488478,0.0021377404,0.0041713594,0.001786614,0.0025024477,0.0034177501,0.0031765457,0.0066446178],"category_scores_gemma":[0.017995477,0.0006178216,0.0016086692,0.0039045915,0.0009112122,0.00228659,0.0023586787,0.0019597383,0.007974257],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0029269843,0.0023424977,0.017489186,0.004059857,0.0011606877,0.00063624635,0.0003770827,0.07514523,0.028155804,0.0031621773,0.38542095,0.47912332],"study_design_scores_gemma":[0.0013035496,0.005321415,0.073895894,0.0010632274,0.000678207,0.0042844983,0.0007821257,0.60137737,0.079592355,0.008201209,0.22300519,0.00049502327],"about_ca_topic_score_codex":0.023196558,"about_ca_topic_score_gemma":0.028939243,"teacher_disagreement_score":0.023196558,"about_ca_system_score_codex":0.0023220354,"about_ca_system_score_gemma":0.0023468796,"threshold_uncertainty_score":0.046123087},"labels":[],"label_agreement":null},{"id":"W1512719693","doi":"10.1007/978-3-642-12297-2_18","title":"Object Detection with Multiple Motion Models","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer vision; Artificial intelligence; Motion (physics); Computer science; Tracking (education); Joint (building); Object (grammar); Object detection; Match moving; Motion detection; Video tracking; Motion estimation; Algorithm; Pattern recognition (psychology); Engineering","score_opus":0.02230703107053695,"score_gpt":0.2511055281632856,"score_spread":0.22879849709274863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1512719693","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028234154,0.0005522065,0.9949368,0.000058602538,0.000054466007,0.00001721553,0.000040734994,0.0008265453,0.00068998756],"genre_scores_gemma":[0.13036044,0.0014558545,0.85540694,0.00018685404,0.0001296284,0.00009691679,0.00067110406,0.0002987936,0.011393514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990903,0.00012354412,0.00004033826,0.0003132083,0.0003478823,0.000084716674],"domain_scores_gemma":[0.99947315,0.00020127428,0.000044702094,0.0001583366,0.00010042674,0.000022123571],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096901634,0.0012356556,0.0017141226,0.0013334741,0.00031212505,0.0011945929,0.0018418614,0.0017384936,0.0027603302],"category_scores_gemma":[0.0020234913,0.0013094777,0.0012654503,0.0013165848,0.00047479136,0.001945614,0.001908808,0.0011950259,0.0026678944],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017454877,0.00005474552,0.0004471083,0.00014806517,0.00015036244,0.00012417974,0.000042302927,0.05579933,0.03071652,0.0040695835,0.0037745708,0.90449864],"study_design_scores_gemma":[0.00001206003,0.00005866594,0.0006230177,0.000019364801,0.000047811925,0.00036222558,0.000012142823,0.96885175,0.016231006,0.008775935,0.004985402,0.000020582489],"about_ca_topic_score_codex":0.0019590969,"about_ca_topic_score_gemma":0.0023706339,"teacher_disagreement_score":0.0027603302,"about_ca_system_score_codex":0.00044605826,"about_ca_system_score_gemma":0.00039438665,"threshold_uncertainty_score":0.00923419},"labels":[],"label_agreement":null},{"id":"W1517719864","doi":"10.5565/rev/elcvia.582","title":"Autonomous UAV for Suspicious Action Detection using Pictorial Human Pose Estimation and Classiﬁcation","year":2014,"lang":"en","type":"article","venue":"ELCVIA Electronic Letters on Computer Vision and Image Analysis","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Pose; Robustness (evolution); Orientation (vector space); Action (physics); Parsing; Mathematics","score_opus":0.012659371928977202,"score_gpt":0.3161595572553594,"score_spread":0.3035001853263822,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1517719864","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":[0.23972563,0.000643627,0.7429909,0.00014199193,0.00015381786,0.00018726623,0.00040173746,0.010401953,0.005353096],"genre_scores_gemma":[0.81174886,0.00020313903,0.18465579,0.00006799713,0.0000356455,0.00006436126,0.00054874516,0.000063922,0.002611447],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999861,0.000020259078,0.000004519756,0.000050266855,0.000043583565,0.00002024641],"domain_scores_gemma":[0.9998512,0.000022651426,0.00003402646,0.000032337557,0.00004226044,0.000017448434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013712562,0.00041394384,0.00027500343,0.0005419871,0.00016921281,0.00033777312,0.00041331115,0.00027845355,0.0011668107],"category_scores_gemma":[0.00036217878,0.0001765304,0.0002120507,0.00022877195,0.00016259309,0.00025940395,0.000306765,0.00019451413,0.0005820839],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048069633,0.00016206608,0.008367347,0.000103588536,0.00006902176,0.0004224975,0.00016760878,0.02449944,0.2887938,0.0009813745,0.0057874075,0.6701652],"study_design_scores_gemma":[0.000036150745,0.00046267567,0.021655248,0.000026104592,0.000043601627,0.0009355893,0.00013379163,0.8501526,0.11879549,0.0008667107,0.0068453513,0.000046620287],"about_ca_topic_score_codex":0.0034613605,"about_ca_topic_score_gemma":0.0035749665,"teacher_disagreement_score":0.0034613605,"about_ca_system_score_codex":0.00016448068,"about_ca_system_score_gemma":0.00024526796,"threshold_uncertainty_score":0.006882429},"labels":[],"label_agreement":null},{"id":"W152581782","doi":"10.1007/978-3-642-33140-4_13","title":"Evaluating the Effects of MJPEG Compression on Motion Tracking in Metro Railway Surveillance","year":2012,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Computer science; Analytics; Ground truth; MATLAB; Video tracking; Real-time computing; Wireless; Tracking (education); Artificial intelligence; Computer vision; Data compression; Match moving; Computation; Bandwidth (computing); Motion (physics); Data mining; Video processing; Telecommunications; Algorithm","score_opus":0.049168844388256715,"score_gpt":0.34596490067054625,"score_spread":0.29679605628228956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W152581782","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":[0.96762365,0.0015581817,0.027418993,0.000091555805,0.0000807863,0.00007281443,0.00028940843,0.0005483529,0.0023163606],"genre_scores_gemma":[0.9788079,0.0007319269,0.018872777,0.00003172131,0.000029136745,0.000013058426,0.00044921704,0.00008312327,0.0009810884],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995627,0.000081381266,0.00002840663,0.00006212334,0.00020563079,0.000059782364],"domain_scores_gemma":[0.9975356,0.0016961966,0.00018523172,0.00012292969,0.00038313668,0.000076829034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007143115,0.00049615797,0.00031222476,0.00063766574,0.00021008753,0.00053496455,0.00039045402,0.00052368635,0.0010327897],"category_scores_gemma":[0.005574805,0.00016081946,0.00018179369,0.0006180957,0.00021805258,0.00061848114,0.00031859797,0.00027773742,0.00018666575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010147891,0.00076100434,0.017075399,0.0006355468,0.00021344675,0.00045547338,0.00017961982,0.24855499,0.23910806,0.0007380935,0.0017455617,0.48038495],"study_design_scores_gemma":[0.000099454504,0.0024330842,0.029581701,0.00006369211,0.00022030484,0.00046127112,0.00012565851,0.81615573,0.14944285,0.00035254404,0.0010268089,0.000036902195],"about_ca_topic_score_codex":0.0070230924,"about_ca_topic_score_gemma":0.007005288,"teacher_disagreement_score":0.0070230924,"about_ca_system_score_codex":0.00036196737,"about_ca_system_score_gemma":0.00033855214,"threshold_uncertainty_score":0.013964415},"labels":[],"label_agreement":null},{"id":"W1526081339","doi":"10.1007/978-3-642-02611-9_65","title":"Human Tracking by IP PTZ Camera Control in the Context of Video Surveillance","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Initialization; Diagonal; Feature extraction; Tracking (education); Pattern recognition (psychology); Mathematics","score_opus":0.02074441223231583,"score_gpt":0.28114794520089337,"score_spread":0.26040353296857754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1526081339","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040598143,0.0014530824,0.9449255,0.00011941717,0.00009838703,0.000042531738,0.0000608306,0.0008206573,0.011881472],"genre_scores_gemma":[0.80634767,0.001777148,0.17956525,0.00008742875,0.000120922836,0.000039911232,0.00015715457,0.00010245787,0.01180201],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980944,0.000031376705,0.000005866137,0.000077266195,0.000054006694,0.000022053817],"domain_scores_gemma":[0.99985075,0.000058833924,0.00002154488,0.000030167268,0.000029648574,0.00000892397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024114473,0.00036407515,0.0004210217,0.00030643147,0.00014695569,0.0007410319,0.0005788253,0.000486846,0.002049971],"category_scores_gemma":[0.00080296386,0.00018881996,0.00020939297,0.00043785272,0.00036491867,0.00072763494,0.00050245237,0.00042199265,0.0005292113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006869758,0.00007143125,0.0018897512,0.0002633156,0.00004094709,0.0003316251,0.00027487442,0.056636307,0.14488202,0.017529419,0.0050601065,0.77233326],"study_design_scores_gemma":[0.000029620753,0.0003591942,0.005772119,0.00004152724,0.000058773447,0.0009986813,0.00008760325,0.8951342,0.075245224,0.010678497,0.011563299,0.00003122259],"about_ca_topic_score_codex":0.0015046771,"about_ca_topic_score_gemma":0.0010385534,"teacher_disagreement_score":0.002049971,"about_ca_system_score_codex":0.00024068257,"about_ca_system_score_gemma":0.00015049335,"threshold_uncertainty_score":0.006857872},"labels":[],"label_agreement":null},{"id":"W1527472118","doi":"10.1109/ccece.2015.7129366","title":"Design and implementation of a robotic car to recognize traffic signs","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Laptop; Computer science; Microcontroller; Simple (philosophy); Fuzzy logic; Real-time computing; Controller (irrigation); Embedded system; Matching (statistics); Computer hardware; Artificial intelligence; Operating system","score_opus":0.10021336450082394,"score_gpt":0.35703331196954746,"score_spread":0.2568199474687235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1527472118","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020569336,0.00021184173,0.95838636,0.00021730537,0.00018824909,0.00086573645,0.00010028468,0.005993234,0.013467587],"genre_scores_gemma":[0.29142913,0.00022762844,0.68842846,0.00028401462,0.000047172656,0.0010289949,0.00023539149,0.00019847289,0.018120784],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953043,0.00003390885,0.000026901836,0.00010176299,0.00024035247,0.0000666316],"domain_scores_gemma":[0.9996295,0.000027239563,0.000033060584,0.000041971507,0.00021875511,0.000049505154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041221772,0.0005259068,0.0004957925,0.0005165218,0.00038592852,0.0006544636,0.0021270914,0.0009892192,0.004417441],"category_scores_gemma":[0.00048073786,0.00044032704,0.00032209425,0.00015853596,0.00026799896,0.00037171203,0.00052509137,0.0006004071,0.0025955108],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037737,0.00048143516,0.0034901535,0.0010039093,0.00015503532,0.0012632363,0.0005160922,0.06966667,0.42488164,0.016172756,0.010422909,0.47156885],"study_design_scores_gemma":[0.00033379722,0.0026791615,0.005251082,0.00017456654,0.00021015797,0.0028128203,0.0002028239,0.44087365,0.34092647,0.0023711475,0.2039576,0.0002066929],"about_ca_topic_score_codex":0.001830006,"about_ca_topic_score_gemma":0.0012357074,"teacher_disagreement_score":0.004417441,"about_ca_system_score_codex":0.0003343895,"about_ca_system_score_gemma":0.0011285583,"threshold_uncertainty_score":0.014777839},"labels":[],"label_agreement":null},{"id":"W1528650981","doi":"10.1109/ccece.2015.7129171","title":"Enhancement of Gaussian background modelling algorithm for moving object detection &amp;amp; its implementation on FPGA","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Field-programmable gate array; Computer science; Object detection; Pipeline (software); Gaussian; Object (grammar); Algorithm; Artificial intelligence; Computer vision; Gaussian process; Real-time computing; Computer hardware; Pattern recognition (psychology)","score_opus":0.14573574620321567,"score_gpt":0.38283267629982065,"score_spread":0.23709693009660499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1528650981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019645372,0.0004311688,0.9684591,0.00012133535,0.0001185064,0.000070042646,0.000063900094,0.0046945284,0.006396162],"genre_scores_gemma":[0.37786838,0.0006413703,0.6056726,0.0002035556,0.00007776388,0.0001116985,0.0003216259,0.00024872317,0.014854203],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970704,0.000036891288,0.000018290382,0.00005164634,0.00014220465,0.00004403291],"domain_scores_gemma":[0.9997464,0.0000477194,0.000020290321,0.000029914656,0.00014239414,0.0000132997775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027482648,0.00061738567,0.0003256208,0.00060388,0.00021903361,0.0005931827,0.00088717876,0.0004445914,0.005201696],"category_scores_gemma":[0.0005618385,0.00023900908,0.00025232887,0.00056525925,0.00015213527,0.0006949118,0.00026612572,0.00036292485,0.0016825574],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053092843,0.00011878211,0.0012611347,0.0003233362,0.00006576483,0.00038963,0.00015053203,0.022215765,0.27515855,0.009363564,0.010761641,0.6796604],"study_design_scores_gemma":[0.00011190069,0.00056344445,0.0020298234,0.00004553077,0.000095138945,0.0012784062,0.000039631377,0.4990393,0.43484214,0.0014941351,0.060393527,0.00006705247],"about_ca_topic_score_codex":0.0017073286,"about_ca_topic_score_gemma":0.0013641092,"teacher_disagreement_score":0.005201696,"about_ca_system_score_codex":0.00034533584,"about_ca_system_score_gemma":0.00041576833,"threshold_uncertainty_score":0.017401397},"labels":[],"label_agreement":null},{"id":"W1531695270","doi":"10.1007/978-3-642-02611-9_71","title":"Region Classification for Robust Floor Detection in Indoor Environments","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Computer vision; Obstacle; Homography; Pixel; Classifier (UML); Ground truth; Pattern recognition (psychology); Mathematics; Geography","score_opus":0.04683207120025216,"score_gpt":0.2728261870323058,"score_spread":0.22599411583205367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1531695270","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":[0.020561777,0.0008182935,0.9726902,0.000049625076,0.00010263126,0.00006084814,0.0003623006,0.004205986,0.0011481806],"genre_scores_gemma":[0.1986222,0.0007122744,0.7931011,0.00010731104,0.00009647597,0.00012600515,0.0014372697,0.0006364995,0.005160831],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992834,0.00007568623,0.00003356097,0.000204808,0.00025231778,0.0001502947],"domain_scores_gemma":[0.99940574,0.00017954507,0.000053706925,0.00013560492,0.0001932368,0.000032147862],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005917876,0.0010362595,0.0016877338,0.0014617293,0.00042394965,0.00095002557,0.0020841327,0.0010842294,0.0038447897],"category_scores_gemma":[0.001305746,0.0006483578,0.0012948422,0.0014240858,0.00036331877,0.0008856319,0.0010402516,0.00097242673,0.0030451007],"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":[0.00039159952,0.00012687927,0.0010673802,0.00016030204,0.00007030976,0.00007982693,0.00005844325,0.027594414,0.089867555,0.0011421476,0.0052117133,0.87422943],"study_design_scores_gemma":[0.000018730787,0.00011275587,0.0034286603,0.000033073586,0.00006307243,0.00028638818,0.00005392988,0.9200331,0.06988268,0.0019228687,0.0041267774,0.000038036582],"about_ca_topic_score_codex":0.0056929397,"about_ca_topic_score_gemma":0.0068771434,"teacher_disagreement_score":0.0056929397,"about_ca_system_score_codex":0.00048647213,"about_ca_system_score_gemma":0.0007086604,"threshold_uncertainty_score":0.012862146},"labels":[],"label_agreement":null},{"id":"W1532681415","doi":"10.1109/icme.2015.7177407","title":"Object tracking using structure-aware binary features","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Artificial intelligence; Robustness (evolution); Computer science; Binary number; Pattern recognition (psychology); Rectangle; Video tracking; Computer vision; Local binary patterns; Domain (mathematical analysis); Object (grammar); Histogram; Mathematics; Image (mathematics)","score_opus":0.08326346975951512,"score_gpt":0.3400145971581055,"score_spread":0.2567511273985904,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1532681415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07231153,0.0006284907,0.9238622,0.000082188875,0.000076172444,0.000047014215,0.00015134229,0.0009443832,0.0018966917],"genre_scores_gemma":[0.74521434,0.00055731065,0.25039282,0.00011666592,0.000074061674,0.000053470376,0.0007799949,0.0001060578,0.0027052232],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969125,0.000027514192,0.000013837037,0.00010569874,0.000117787786,0.00004400967],"domain_scores_gemma":[0.9995819,0.00009309931,0.00010995021,0.00007301026,0.000115565126,0.000026495347],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044727218,0.00048019996,0.0007874716,0.0019893926,0.00028805528,0.00082427944,0.0007758559,0.00063481845,0.00069374853],"category_scores_gemma":[0.0011701799,0.00030134464,0.00043209878,0.0019448133,0.00029904646,0.0012728027,0.0007156258,0.00050083356,0.0005644296],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032213764,0.00017716583,0.0029036633,0.00012464946,0.00007109543,0.00007896122,0.00005535023,0.061306246,0.07677205,0.0029993346,0.0022148169,0.85297453],"study_design_scores_gemma":[0.000021434089,0.00010901064,0.004722318,0.000017305227,0.000043461667,0.00019627962,0.00001906771,0.9685185,0.02129509,0.003259983,0.0017755417,0.000021976437],"about_ca_topic_score_codex":0.002220654,"about_ca_topic_score_gemma":0.002135463,"teacher_disagreement_score":0.002220654,"about_ca_system_score_codex":0.0004436458,"about_ca_system_score_gemma":0.00037641212,"threshold_uncertainty_score":0.004415393},"labels":[],"label_agreement":null},{"id":"W1533704302","doi":"10.1007/978-3-642-10467-1_32","title":"Automatic Detection of Object of Interest and Tracking in Active Video","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Initialization; Computer vision; Video tracking; AdaBoost; Classifier (UML); Pattern recognition (psychology); Object detection; Salient; Object (grammar)","score_opus":0.03684155327446538,"score_gpt":0.29622404265613417,"score_spread":0.2593824893816688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1533704302","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":[0.025142448,0.0011657586,0.96968573,0.00004911372,0.0001274551,0.00004415813,0.00009923043,0.0010966511,0.0025893769],"genre_scores_gemma":[0.29212692,0.0017114049,0.6929374,0.00012612954,0.00015389209,0.00008234376,0.0006792647,0.00028493168,0.011897695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962676,0.000032094063,0.000015086664,0.00011739521,0.0001586492,0.000049941755],"domain_scores_gemma":[0.999361,0.00028706057,0.000055769935,0.00009373516,0.00016700015,0.000035471643],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005900833,0.00060113455,0.00076451787,0.0014035692,0.00028011587,0.0010581692,0.0015407519,0.0011046602,0.0014983066],"category_scores_gemma":[0.0013252016,0.0005628845,0.0004949664,0.0009817288,0.00040487014,0.0011329105,0.0006853643,0.00063194684,0.0012003726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021616646,0.000114474635,0.00091759366,0.00017178354,0.000035862857,0.00012838622,0.00009769923,0.0071705296,0.1790914,0.004247495,0.0028910206,0.8049177],"study_design_scores_gemma":[0.000033890974,0.0002507569,0.0072456403,0.00006290782,0.000121966965,0.0014242389,0.00007759682,0.68905175,0.27799672,0.009462325,0.01421592,0.00005628661],"about_ca_topic_score_codex":0.0010157847,"about_ca_topic_score_gemma":0.0014769442,"teacher_disagreement_score":0.0015407519,"about_ca_system_score_codex":0.0002369091,"about_ca_system_score_gemma":0.0002396743,"threshold_uncertainty_score":0.0050123334},"labels":[],"label_agreement":null},{"id":"W1539081329","doi":"10.1007/978-3-642-13681-8_27","title":"Human Detection with a Multi-sensors Stereovision System","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Artificial intelligence; Daylight; Computer vision; Process (computing); Cascade; Object detection; Pattern recognition (psychology); Optics; Engineering","score_opus":0.025106295036767964,"score_gpt":0.27881905786219585,"score_spread":0.2537127628254279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1539081329","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":[0.03633877,0.0005950791,0.9526807,0.00011933849,0.0001353207,0.000120830664,0.00031502638,0.0022997349,0.0073952395],"genre_scores_gemma":[0.40232396,0.0006956413,0.5856651,0.0002632424,0.00013601323,0.000122053294,0.00047399593,0.00011173333,0.010208332],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996032,0.000050252074,0.000012909038,0.00009636586,0.00019944062,0.000037846614],"domain_scores_gemma":[0.99978846,0.000047490776,0.000013900814,0.000037612106,0.00008727255,0.000025313731],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003704581,0.0005073995,0.0005836202,0.0009177274,0.00029787212,0.000612404,0.0008064032,0.0009589759,0.0038722996],"category_scores_gemma":[0.0004896281,0.0005699326,0.00046971266,0.00071424514,0.00021819671,0.0006341617,0.0007063639,0.00043578507,0.0015150907],"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":[0.000594886,0.00014420804,0.001421466,0.00018467159,0.000095550145,0.0001421807,0.00010132379,0.008992128,0.3603552,0.0018301889,0.004851468,0.62128675],"study_design_scores_gemma":[0.00020126684,0.00095913385,0.023299653,0.00008737529,0.00029212385,0.002910746,0.00012082641,0.6349672,0.30303177,0.0052507455,0.02869009,0.0001890471],"about_ca_topic_score_codex":0.0017848272,"about_ca_topic_score_gemma":0.003475911,"teacher_disagreement_score":0.0038722996,"about_ca_system_score_codex":0.00031446794,"about_ca_system_score_gemma":0.0005281513,"threshold_uncertainty_score":0.0129541755},"labels":[],"label_agreement":null},{"id":"W1543581404","doi":"10.1007/978-3-540-76386-4_50","title":"Spatiotemporal Oriented Energy Features for Visual Tracking","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"BitTorrent tracker; Computer science; Artificial intelligence; Clutter; Tracking (education); Computer vision; Eye tracking; Set (abstract data type); Feature (linguistics); Orientation (vector space); Video tracking; Energy (signal processing); Estimator; Pattern recognition (psychology); Mathematics; Object (grammar)","score_opus":0.034631170678768274,"score_gpt":0.32310466603994215,"score_spread":0.2884734953611739,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1543581404","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":[0.007355747,0.0008893756,0.9895438,0.000048482434,0.00006256091,0.000018016837,0.00018585678,0.0004988645,0.0013972686],"genre_scores_gemma":[0.312305,0.00252823,0.67113,0.00009475123,0.00016294022,0.0001392871,0.0013570935,0.00054528273,0.011737348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998869,0.000014855414,0.000008331599,0.00003093829,0.00004715218,0.000011764472],"domain_scores_gemma":[0.9998056,0.00007208543,0.000023187155,0.0000470271,0.000044535493,0.0000074178124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022287275,0.00046820487,0.0006224496,0.00074360264,0.00016574567,0.00069690606,0.00064369664,0.0005056657,0.0029917008],"category_scores_gemma":[0.0010649342,0.00026401549,0.00034045565,0.0016871266,0.00021231652,0.0012074094,0.00062149117,0.00053494214,0.0010530725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017977774,0.00005956028,0.00039581725,0.00013713248,0.000042755153,0.00007507328,0.000035588135,0.054676022,0.04648992,0.02725301,0.0059305644,0.8647247],"study_design_scores_gemma":[0.000014435883,0.00005672277,0.0017594767,0.000029567756,0.000032529624,0.00020210212,0.000026948772,0.93715936,0.018853322,0.03248433,0.009356424,0.000024809142],"about_ca_topic_score_codex":0.0008856019,"about_ca_topic_score_gemma":0.0011696228,"teacher_disagreement_score":0.0029917008,"about_ca_system_score_codex":0.00024361232,"about_ca_system_score_gemma":0.00013827109,"threshold_uncertainty_score":0.0100082755},"labels":[],"label_agreement":null},{"id":"W1550219943","doi":"10.1007/978-3-642-16355-5_9","title":"Support Vector Machines for Inhabitant Identification in Smart Houses","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Support vector machine; Password; Identification (biology); Feature selection; Classifier (UML); Machine learning; Artificial intelligence; Data mining; Feature vector; Smart environment; Process (computing); Computer security; Internet of Things","score_opus":0.023927445871866224,"score_gpt":0.2921565550762632,"score_spread":0.26822910920439696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1550219943","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04118231,0.0013336778,0.9548885,0.000115651295,0.00009916691,0.000030235347,0.000317926,0.0010396395,0.0009928695],"genre_scores_gemma":[0.7031059,0.001145472,0.28721058,0.00007781447,0.0001289307,0.00013216416,0.0010138556,0.00009763584,0.0070876703],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962854,0.00009686365,0.000024347186,0.00010430058,0.00008952462,0.000056510144],"domain_scores_gemma":[0.9995023,0.00030535072,0.00004389758,0.000047375237,0.00008233316,0.00001883702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041343176,0.0006417983,0.001109026,0.0005860005,0.00027738625,0.0006118147,0.0010311041,0.00097647123,0.0018734585],"category_scores_gemma":[0.0016007121,0.00031948704,0.00057142595,0.0009979678,0.0002552499,0.0010197927,0.0005934165,0.0009203597,0.0011002786],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018384212,0.00011166369,0.0030494134,0.00016471487,0.000063524196,0.00017769393,0.00011903373,0.23668972,0.0086433785,0.003947495,0.0047674947,0.7420821],"study_design_scores_gemma":[0.0000025616787,0.000019161485,0.00076573645,0.000007849524,0.0000058221,0.000035180266,0.000042941698,0.99417555,0.0014914823,0.0028243768,0.00062212185,0.000007252461],"about_ca_topic_score_codex":0.0041436907,"about_ca_topic_score_gemma":0.003773117,"teacher_disagreement_score":0.0041436907,"about_ca_system_score_codex":0.00029931054,"about_ca_system_score_gemma":0.0002831063,"threshold_uncertainty_score":0.00823915},"labels":[],"label_agreement":null},{"id":"W1558051366","doi":"10.1371/journal.pone.0151984","title":"Expectation-Maximization Binary Clustering for Behavioural Annotation","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":172,"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":"Fundación Biodiversidad; Universitat de Barcelona; Ministerio de Ciencia e Innovación; Institució Catalana de Recerca i Estudis Avançats; University of Toronto; Emory University; Lunds Universitet; Consejo Superior de Investigaciones Científicas; Vetenskapsrådet","keywords":"Cluster analysis; Expectation–maximization algorithm; Computer science; Annotation; Binary number; Computational biology; Artificial intelligence; Biology; Mathematics; Statistics; Maximum likelihood","score_opus":0.10875646124983834,"score_gpt":0.28499391092356624,"score_spread":0.1762374496737279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1558051366","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":[0.0019527698,0.00017566724,0.99453366,0.00014787912,0.000032724154,0.00006490627,0.00045169133,0.002027739,0.00061296095],"genre_scores_gemma":[0.05273473,0.00018915893,0.9386875,0.00021816995,0.00006522381,0.00043915806,0.0039487756,0.0010361734,0.0026812176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9962214,0.0013300721,0.00024430745,0.0012277394,0.0007723288,0.00020415474],"domain_scores_gemma":[0.99456465,0.002524832,0.00045450553,0.0009212286,0.0013926941,0.00014191476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039711343,0.0017660436,0.001995026,0.0029169593,0.0014440749,0.0018361019,0.0044236123,0.0027173571,0.0062116887],"category_scores_gemma":[0.016267987,0.0011428604,0.0021680384,0.0040286286,0.0012064995,0.0020912336,0.0022189599,0.0033852565,0.0063123154],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004441294,0.0002212861,0.0023874023,0.0007699702,0.00027710557,0.00016770895,0.00050044147,0.4150798,0.008756005,0.037725665,0.037934646,0.4957359],"study_design_scores_gemma":[0.000012861903,0.000014760229,0.0005889903,0.0000331938,0.000011280628,0.000043294032,0.000036054556,0.9594619,0.0016874124,0.034370363,0.0037085423,0.000031332213],"about_ca_topic_score_codex":0.012841435,"about_ca_topic_score_gemma":0.016740378,"teacher_disagreement_score":0.012841435,"about_ca_system_score_codex":0.0022059535,"about_ca_system_score_gemma":0.0026109647,"threshold_uncertainty_score":0.025533378},"labels":[],"label_agreement":null},{"id":"W1559347819","doi":"10.1007/978-3-642-12297-2_22","title":"Human Action Recognition Using Non-separable Oriented 3D Dual-Tree Complex Wavelets","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Complex wavelet transform; Computer science; Artificial intelligence; Pattern recognition (psychology); Classifier (UML); Computer vision; Wavelet transform; Pixel; Categorization; Wavelet; Discrete wavelet transform","score_opus":0.07964072100341703,"score_gpt":0.3331938573939116,"score_spread":0.2535531363904946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1559347819","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03954294,0.00042718512,0.95796704,0.00006839455,0.000058904192,0.000028320415,0.0001671806,0.0003726707,0.0013673614],"genre_scores_gemma":[0.44529736,0.001427536,0.54893506,0.00010197319,0.000097123,0.00006711647,0.0006915784,0.00014324121,0.0032390284],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999795,0.00002947872,0.000012615211,0.000054554934,0.000080419784,0.000027846401],"domain_scores_gemma":[0.9997286,0.00008762978,0.000028902166,0.000053664513,0.00007947069,0.000021643666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032649242,0.00047278748,0.00065555354,0.0008695178,0.00013517619,0.00075900415,0.0004423753,0.0005723919,0.0016256968],"category_scores_gemma":[0.0008712258,0.00028907982,0.0006490515,0.0010646087,0.00030305833,0.0007279068,0.0004716325,0.0005009321,0.0009862916],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038412792,0.00011489523,0.0017584502,0.00017506804,0.00007919908,0.00016957095,0.0001036602,0.027799167,0.12733893,0.006107067,0.0031017116,0.83286816],"study_design_scores_gemma":[0.000021338377,0.000120573175,0.0062925355,0.00003199368,0.00005016636,0.00050921994,0.0000622713,0.9596052,0.024553077,0.0061193286,0.00260695,0.000027300635],"about_ca_topic_score_codex":0.0006832031,"about_ca_topic_score_gemma":0.00103773,"teacher_disagreement_score":0.0016256968,"about_ca_system_score_codex":0.0001433003,"about_ca_system_score_gemma":0.00021913658,"threshold_uncertainty_score":0.005438447},"labels":[],"label_agreement":null},{"id":"W1561770131","doi":"","title":"Automated person segmentation in videos","year":2012,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Conditional random field; Computer vision; Computer science; Segmentation; Pose; Detector; Frame (networking); Image segmentation; Optical flow; 3D pose estimation; Pattern recognition (psychology); Image (mathematics)","score_opus":0.021259946553243432,"score_gpt":0.2797878230200291,"score_spread":0.25852787646678566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1561770131","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":[0.07889759,0.0010897826,0.9047434,0.00023177797,0.00020376612,0.00023333644,0.0011859876,0.006232253,0.007182109],"genre_scores_gemma":[0.37255132,0.0011851772,0.60934585,0.0002799317,0.0002554102,0.00013100269,0.004732713,0.0008371081,0.010681478],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898726,0.00017874515,0.000030910953,0.00043859874,0.00018865391,0.00017578107],"domain_scores_gemma":[0.99949706,0.00013424251,0.00006957837,0.00009634102,0.00015376146,0.000049074253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000493252,0.0011976109,0.00092575664,0.0026668326,0.0007319657,0.0008704504,0.0009350142,0.0011081425,0.0035652318],"category_scores_gemma":[0.001397426,0.00039465612,0.00078641565,0.0012468225,0.00041346898,0.0009133709,0.00081921794,0.0005760575,0.0030567688],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049014325,0.00016009882,0.004450183,0.00017516968,0.00009598425,0.00046310975,0.00028509198,0.024642458,0.0784219,0.003606842,0.015167237,0.8720418],"study_design_scores_gemma":[0.000053708867,0.00030273292,0.023291487,0.00014254531,0.00010017319,0.0021084247,0.00076891313,0.7785629,0.14812928,0.0140240975,0.03242037,0.00009538995],"about_ca_topic_score_codex":0.008741032,"about_ca_topic_score_gemma":0.012642217,"teacher_disagreement_score":0.008741032,"about_ca_system_score_codex":0.00055612496,"about_ca_system_score_gemma":0.00060040504,"threshold_uncertainty_score":0.017380297},"labels":[],"label_agreement":null},{"id":"W1563021453","doi":"10.1109/crv.2015.53","title":"An Online Unsupervised Feature Selection and its Application for Background Suppression","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Feature selection; Artificial intelligence; Machine learning; Streaming data; Feature (linguistics); Set (abstract data type); Feature extraction; Data mining; Pattern recognition (psychology)","score_opus":0.0925735562986253,"score_gpt":0.3582278964884754,"score_spread":0.2656543401898501,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1563021453","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":[0.010291966,0.000099575795,0.98888576,0.000046625435,0.000014835313,0.000021507683,0.000025389078,0.00044550147,0.00016887992],"genre_scores_gemma":[0.3471599,0.0002884977,0.6488943,0.00016776388,0.000112370406,0.00019700511,0.00048567058,0.00020805803,0.0024862988],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939513,0.0001314646,0.00003395079,0.00019960057,0.0001674342,0.00007247969],"domain_scores_gemma":[0.9991843,0.00031556748,0.000097331635,0.000118580596,0.00023711493,0.000047071982],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007820827,0.0009465037,0.0012808754,0.0012465763,0.00049015164,0.0004351738,0.0012234313,0.0007376658,0.0008115155],"category_scores_gemma":[0.0022664,0.00037738224,0.0009878761,0.0013212651,0.00045619087,0.0009185025,0.0008232692,0.0007988102,0.00032038594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002614564,0.00028710612,0.002387498,0.00008732821,0.00013196142,0.00025895514,0.00013523105,0.20676544,0.047989197,0.003894305,0.00339963,0.7344019],"study_design_scores_gemma":[0.000010819349,0.000043885404,0.0005534915,0.0000031182792,0.000013825021,0.00008056473,0.000008826723,0.99199957,0.005509169,0.0010839779,0.00068225106,0.000010529906],"about_ca_topic_score_codex":0.0030479725,"about_ca_topic_score_gemma":0.003291698,"teacher_disagreement_score":0.0030479725,"about_ca_system_score_codex":0.00033550936,"about_ca_system_score_gemma":0.00078600814,"threshold_uncertainty_score":0.006060481},"labels":[],"label_agreement":null},{"id":"W1572051620","doi":"10.1007/11550518_55","title":"Robust Head Detection and Tracking in Cluttered Workshop Environments Using GMM","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Mixture model; Artificial intelligence; Computer vision; Computer science; Tracking (education); Head (geology); Robustness (evolution); Pattern recognition (psychology)","score_opus":0.06337742730003773,"score_gpt":0.29753083688292525,"score_spread":0.23415340958288752,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1572051620","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":[0.013590224,0.00034336,0.9828981,0.000047000296,0.00007234583,0.000018168714,0.000068481095,0.002087932,0.0008744368],"genre_scores_gemma":[0.33140877,0.00096127746,0.65835905,0.00014392613,0.00014263322,0.00007058891,0.00046756174,0.00070725323,0.007738969],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995554,0.00009877216,0.000020006935,0.00012117594,0.00012719467,0.00007743693],"domain_scores_gemma":[0.9996126,0.00015992174,0.000028083565,0.00007811606,0.00009894545,0.00002236537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055565865,0.00079860166,0.0011755623,0.00078901026,0.00031166206,0.0007358522,0.00089171575,0.00081419665,0.0015702045],"category_scores_gemma":[0.0015442792,0.00070042507,0.00069315446,0.00082946074,0.0003586708,0.0008439529,0.0011838446,0.00065962377,0.001812954],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059970934,0.000079439444,0.0014777778,0.00013240015,0.00018239395,0.0002156945,0.00018715377,0.055462543,0.15458237,0.002805615,0.0064894552,0.7777855],"study_design_scores_gemma":[0.000031876123,0.00013428337,0.005710269,0.000018182,0.000121367,0.0007621367,0.00009237474,0.88740253,0.09588127,0.004867252,0.0049109356,0.00006752534],"about_ca_topic_score_codex":0.003621961,"about_ca_topic_score_gemma":0.0047458373,"teacher_disagreement_score":0.003621961,"about_ca_system_score_codex":0.00030880937,"about_ca_system_score_gemma":0.0004088951,"threshold_uncertainty_score":0.007201791},"labels":[],"label_agreement":null},{"id":"W1579937857","doi":"10.1109/bmsb.2015.7177267","title":"Online pedestrian tracking via saliency-based H-S histogram","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Natural Science Foundation of China","keywords":"Histogram; Artificial intelligence; Minimum bounding box; Computer science; Computer vision; Pixel; Tracking (education); Pedestrian detection; Bounding overwatch; Ground truth; Feature (linguistics); Pattern recognition (psychology); Pedestrian; Image (mathematics); Engineering","score_opus":0.09583626817791878,"score_gpt":0.33253368485702994,"score_spread":0.23669741667911115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1579937857","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":[0.06285839,0.0005511079,0.93184316,0.00008769848,0.000108508604,0.000051320454,0.00013995812,0.0023417228,0.0020181069],"genre_scores_gemma":[0.7912952,0.0003751446,0.20514645,0.00009534013,0.00011423111,0.00004075227,0.00041171128,0.00013950566,0.0023816663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997652,0.00002066493,0.000008412714,0.00007674344,0.00008770304,0.00004115121],"domain_scores_gemma":[0.9996307,0.000083621875,0.000053943095,0.000043586537,0.000137391,0.00005074952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034559748,0.00050181145,0.0008121212,0.0018714579,0.00034587207,0.0004759677,0.00072236947,0.00034615508,0.0012543149],"category_scores_gemma":[0.0009827769,0.0003142117,0.0005078224,0.0011166433,0.00029379744,0.0007376236,0.00055183185,0.00029891788,0.00056212826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070138683,0.00016649891,0.0063367225,0.00015300949,0.00009866945,0.00023418119,0.0001500732,0.060792897,0.06560627,0.00315799,0.0059681507,0.85663414],"study_design_scores_gemma":[0.000037739213,0.000117431635,0.004928519,0.000010229414,0.000042145406,0.00028427906,0.00003441192,0.9706354,0.018712241,0.002712898,0.002455241,0.000029366729],"about_ca_topic_score_codex":0.0053871376,"about_ca_topic_score_gemma":0.00560423,"teacher_disagreement_score":0.0053871376,"about_ca_system_score_codex":0.000485665,"about_ca_system_score_gemma":0.00058597064,"threshold_uncertainty_score":0.010711551},"labels":[],"label_agreement":null},{"id":"W1585059879","doi":"10.1007/978-3-642-15561-1_37","title":"Visual Tracking Using a Pixelwise Spatiotemporal Oriented Energy Representation","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Representation (politics); Image warping; Artificial intelligence; Pattern recognition (psychology); Inter frame; Computer vision; Tracking (education); Motion estimation; Frame (networking); Reference frame","score_opus":0.038147052986569716,"score_gpt":0.32599792736978356,"score_spread":0.28785087438321383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1585059879","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":[0.0027016439,0.00008783924,0.9964395,0.000031226573,0.000027466522,0.000010399199,0.00002889066,0.00008935773,0.0005836825],"genre_scores_gemma":[0.21309842,0.000686611,0.77877057,0.00012711168,0.00009842853,0.00010027151,0.0004052991,0.0002356649,0.0064775944],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982387,0.0000292841,0.000013758389,0.000050141083,0.000067976296,0.000015107104],"domain_scores_gemma":[0.9998455,0.000038573253,0.00002055224,0.000037834427,0.0000477909,0.000009760242],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041363577,0.0005019976,0.0005641564,0.00086862297,0.00021634428,0.0010555584,0.0008132244,0.0007852094,0.001759112],"category_scores_gemma":[0.0009213177,0.00034785928,0.0007364117,0.0014303385,0.00026909506,0.0013711936,0.0007790958,0.00062843587,0.0006507942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020980694,0.00011857661,0.00066879764,0.0001387105,0.00014005823,0.00014081615,0.00007784524,0.23792139,0.09550933,0.065483384,0.003272087,0.5963192],"study_design_scores_gemma":[0.0000052097075,0.000030090874,0.0003917708,0.000008771052,0.00002416111,0.00010104469,0.000007537427,0.9832624,0.005487819,0.009045607,0.0016221112,0.0000135371765],"about_ca_topic_score_codex":0.0011517715,"about_ca_topic_score_gemma":0.001438699,"teacher_disagreement_score":0.001759112,"about_ca_system_score_codex":0.000334689,"about_ca_system_score_gemma":0.00029177868,"threshold_uncertainty_score":0.005884826},"labels":[],"label_agreement":null},{"id":"W1585116509","doi":"10.1109/ccece.2015.7129345","title":"Moving objects tracking from most probable regions and eliminating camera motion","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Tracking (education); Focus (optics); Match moving; Object (grammar); Position (finance); Video tracking; Residual; Object detection; Pyramid (geometry); Gaussian; Motion (physics); Pattern recognition (psychology); Mathematics; Algorithm","score_opus":0.0583908186737613,"score_gpt":0.29137869202145555,"score_spread":0.23298787334769425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1585116509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0327991,0.00081689324,0.9643939,0.00006006588,0.000055289125,0.000052326177,0.00007845147,0.00085057796,0.0008934223],"genre_scores_gemma":[0.26389417,0.0009954383,0.731143,0.00009410012,0.00008822867,0.00007554958,0.00047475143,0.00028104897,0.0029537126],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992194,0.00006178007,0.000043743672,0.00030703243,0.00031220703,0.000055835895],"domain_scores_gemma":[0.9992817,0.0001997975,0.00013453294,0.00015696934,0.0001976111,0.000029477975],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008742436,0.0007576199,0.0009947494,0.0021723765,0.0004471707,0.0010423379,0.0011715904,0.00073587714,0.0007452562],"category_scores_gemma":[0.002577966,0.00065330975,0.0011941412,0.001347364,0.00034943526,0.0013119564,0.0006876977,0.00068147754,0.00068371964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032191188,0.00011202606,0.0039790026,0.00021596924,0.00019299301,0.00031168142,0.00030595815,0.040718786,0.10106863,0.0027619528,0.001725025,0.8482859],"study_design_scores_gemma":[0.000050972783,0.00021707678,0.014134701,0.000053186457,0.00033043453,0.0018136529,0.00013980608,0.8503689,0.11855371,0.0038537642,0.010386545,0.00009711349],"about_ca_topic_score_codex":0.0040484234,"about_ca_topic_score_gemma":0.0052964254,"teacher_disagreement_score":0.0040484234,"about_ca_system_score_codex":0.00039592793,"about_ca_system_score_gemma":0.0009007972,"threshold_uncertainty_score":0.0080497265},"labels":[],"label_agreement":null},{"id":"W1590413836","doi":"10.1007/978-3-642-10268-4_95","title":"Fuzzy Feature-Based Upper Body Tracking with IP PTZ Camera Control","year":2009,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Diagonal; Fuzzy logic; Frame (networking); Frame rate; Classifier (UML); Feature (linguistics); Mathematics","score_opus":0.01435036323617115,"score_gpt":0.2581544733446371,"score_spread":0.24380411010846595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1590413836","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018969906,0.00022883754,0.9769372,0.00003120552,0.000052121093,0.00003694395,0.000024700741,0.0003298329,0.0033892435],"genre_scores_gemma":[0.8850652,0.00022878486,0.11138449,0.000045219098,0.000033740427,0.00008683586,0.00005699018,0.000028231027,0.0030705119],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99976665,0.000021937338,0.000014635201,0.00007573013,0.00009753763,0.00002350899],"domain_scores_gemma":[0.9998456,0.000038242317,0.00002512469,0.00002065995,0.00006205554,0.00000819442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003008136,0.00037837666,0.0006137696,0.00032816196,0.00025645862,0.0005742211,0.0005656416,0.00045553167,0.0014469995],"category_scores_gemma":[0.0006019822,0.00019187025,0.00039873712,0.00038683394,0.00027357895,0.000411066,0.00038762263,0.00036176763,0.00035755205],"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":[0.000721531,0.00010852313,0.0010202893,0.00032610248,0.00007308334,0.00015572968,0.00018161157,0.18061443,0.12150622,0.0061603417,0.0018961892,0.6872359],"study_design_scores_gemma":[0.00002478716,0.00023138295,0.0020123934,0.000021649184,0.00004127202,0.0001441559,0.000016464162,0.9801513,0.014627418,0.001334178,0.0013760037,0.000019000678],"about_ca_topic_score_codex":0.0025322433,"about_ca_topic_score_gemma":0.0019990155,"teacher_disagreement_score":0.0025322433,"about_ca_system_score_codex":0.00030848978,"about_ca_system_score_gemma":0.00022614849,"threshold_uncertainty_score":0.005034983},"labels":[],"label_agreement":null},{"id":"W1601669981","doi":"","title":"Object detection using a moving camera under sudden illumination change","year":2013,"lang":"en","type":"article","venue":"Chinese Control Conference","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Background subtraction; Computer vision; Change detection; Artificial intelligence; Computer science; Object detection; Foreground detection; Tracking (education); Video tracking; Object (grammar); Subtraction; Pattern recognition (psychology); Pixel; Mathematics","score_opus":0.04576470541031745,"score_gpt":0.2960345553245552,"score_spread":0.25026984991423773,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1601669981","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16368638,0.0007186389,0.83095264,0.00008324738,0.000096175565,0.000057848843,0.00009417802,0.0024005927,0.0019102413],"genre_scores_gemma":[0.6517596,0.00064648705,0.3448617,0.00012549365,0.00006319745,0.000037150872,0.00037148484,0.00011639686,0.0020185446],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995129,0.000054043325,0.000014591857,0.00016762665,0.00018702478,0.00006377474],"domain_scores_gemma":[0.9997178,0.000068442685,0.00003279841,0.000043748376,0.000103704544,0.00003352782],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043563536,0.0006087782,0.0010434972,0.0015244198,0.0003212224,0.0007689159,0.0008717369,0.0007904518,0.0004867142],"category_scores_gemma":[0.0009784246,0.0003857371,0.0006043216,0.0010715828,0.0003226748,0.00073086267,0.00064497517,0.00057510426,0.00038177747],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044998396,0.00012910366,0.0044745407,0.00021456061,0.000167415,0.0011156541,0.00024252739,0.05197961,0.43061316,0.0024709639,0.0015265975,0.506616],"study_design_scores_gemma":[0.000023469485,0.00024143085,0.01009091,0.000019947607,0.000077813675,0.0011486475,0.00009800613,0.8357909,0.14746596,0.0014283068,0.0035679198,0.000046848832],"about_ca_topic_score_codex":0.002727294,"about_ca_topic_score_gemma":0.0021561997,"teacher_disagreement_score":0.002727294,"about_ca_system_score_codex":0.00032306174,"about_ca_system_score_gemma":0.00038376308,"threshold_uncertainty_score":0.005422771},"labels":[],"label_agreement":null},{"id":"W164317220","doi":"10.1007/978-3-642-24028-7_71","title":"Real-Time Object Tracking on iPhone","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Tracking (education); Video tracking; Object (grammar); Matching (statistics); Tracking system; Computer graphics (images); Mathematics","score_opus":0.03347473561539138,"score_gpt":0.2774255847903342,"score_spread":0.2439508491749428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W164317220","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":[0.11167857,0.0016981767,0.8575646,0.000157044,0.00043079743,0.000104743245,0.00067061983,0.013221576,0.014473865],"genre_scores_gemma":[0.5583965,0.00085925974,0.4112298,0.0002413111,0.00011474598,0.000065300024,0.0014115942,0.00069117115,0.026990239],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998054,0.000018678578,0.000007141072,0.000061612605,0.00008140274,0.000025778823],"domain_scores_gemma":[0.99979514,0.000060417,0.000008852858,0.00004880201,0.000068284855,0.000018602286],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033551225,0.00046718173,0.0006072095,0.0005684407,0.00028040688,0.0005284459,0.0005979286,0.0006423169,0.006484308],"category_scores_gemma":[0.00053202716,0.00027822453,0.00022675694,0.0005235216,0.0001413476,0.0007122749,0.00048148562,0.00027220833,0.00216052],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006746236,0.00005845286,0.0011474832,0.00012631202,0.000045775723,0.0002066165,0.00011424625,0.0076962486,0.15224312,0.0011473896,0.011843182,0.82469666],"study_design_scores_gemma":[0.000059862934,0.00029842838,0.01244151,0.00005630187,0.000101889054,0.001447798,0.00008748375,0.7847755,0.16322827,0.0026847941,0.03474537,0.00007282446],"about_ca_topic_score_codex":0.0023278731,"about_ca_topic_score_gemma":0.0033995414,"teacher_disagreement_score":0.006484308,"about_ca_system_score_codex":0.00020389457,"about_ca_system_score_gemma":0.00018409887,"threshold_uncertainty_score":0.021692157},"labels":[],"label_agreement":null},{"id":"W1719277475","doi":"10.1109/crv.2005.60","title":"People Tracking using Robust Motion Detection and Estimation","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Robustness (evolution); Video tracking; Monocular; Motion estimation; Video processing","score_opus":0.045215288751262385,"score_gpt":0.29469007082561033,"score_spread":0.24947478207434795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1719277475","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02655157,0.00044892324,0.97038066,0.000055945613,0.000035346427,0.000028683653,0.000044323442,0.0014955819,0.0009589618],"genre_scores_gemma":[0.41369408,0.0006367745,0.5814275,0.00010379407,0.000094576266,0.00007615565,0.00033812164,0.00012211935,0.003506941],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99947315,0.000076610544,0.000019090545,0.00018716702,0.00018905048,0.000054982575],"domain_scores_gemma":[0.9996251,0.00011551183,0.00007226926,0.000072352974,0.00009581831,0.000018991113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000543607,0.00051012397,0.00076058944,0.0014497787,0.0002652761,0.0007329496,0.0008079631,0.000873925,0.0009461282],"category_scores_gemma":[0.001718774,0.00036754823,0.0004928577,0.00075803086,0.00030140442,0.00093371095,0.0005836558,0.00038797938,0.0008234528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034432154,0.00010064335,0.0021350142,0.00011772056,0.00013346002,0.00012021837,0.000097244265,0.06518593,0.115296945,0.003046826,0.0022081714,0.81121343],"study_design_scores_gemma":[0.000055374076,0.00014699498,0.0051031727,0.000023783954,0.00007291888,0.00034065905,0.000035046975,0.9229019,0.062261865,0.003851046,0.005155054,0.00005218081],"about_ca_topic_score_codex":0.003275337,"about_ca_topic_score_gemma":0.002684369,"teacher_disagreement_score":0.003275337,"about_ca_system_score_codex":0.00039874908,"about_ca_system_score_gemma":0.0004020381,"threshold_uncertainty_score":0.0065125227},"labels":[],"label_agreement":null},{"id":"W1776642447","doi":"10.1007/978-3-319-23222-5_65","title":"Nonlinear Background Filter to Improve Pedestrian Detection","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Filter (signal processing); Detector; Frame (networking); False positive paradox; Nonlinear system; Pedestrian detection; Pedestrian; Motion (physics); Image (mathematics); Frame rate; Motion detection; Physics; Geography; Telecommunications","score_opus":0.04950859430179317,"score_gpt":0.30827015369565247,"score_spread":0.2587615593938593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1776642447","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":[0.022177117,0.0015565429,0.9697688,0.00016552075,0.0003351322,0.00004355219,0.0002152515,0.0019486098,0.003789521],"genre_scores_gemma":[0.25896415,0.0034897553,0.7022573,0.0004316645,0.0002889236,0.00007407007,0.0018259714,0.00081999635,0.0318482],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996991,0.000032432577,0.000011385589,0.00009115844,0.00011508406,0.00005071719],"domain_scores_gemma":[0.99963117,0.00006887268,0.000016078713,0.00004298588,0.00021419606,0.000026761552],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056655565,0.0011223818,0.0010644849,0.0012067495,0.00042928642,0.00080454774,0.00085289613,0.00077596714,0.003967792],"category_scores_gemma":[0.0009678709,0.00039749304,0.0008681042,0.00119563,0.00025408927,0.0009563121,0.00083093933,0.0010492896,0.0034440854],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040563405,0.00023294536,0.0010669029,0.00018776362,0.00010788417,0.00012169844,0.000057880367,0.022855844,0.08987892,0.0032799314,0.01003861,0.871766],"study_design_scores_gemma":[0.000023809194,0.000113826,0.002288274,0.00002938773,0.00017367369,0.0003392013,0.000025780437,0.91249454,0.07192082,0.0021927042,0.010370708,0.00002730373],"about_ca_topic_score_codex":0.0069825402,"about_ca_topic_score_gemma":0.010144245,"teacher_disagreement_score":0.0069825402,"about_ca_system_score_codex":0.00054935034,"about_ca_system_score_gemma":0.000695089,"threshold_uncertainty_score":0.0138837695},"labels":[],"label_agreement":null},{"id":"W1796589289","doi":"10.1109/mva.2015.7153248","title":"Object detection in surveillance video from dense trajectories","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Object detection; Object (grammar); Video tracking; Trajectory; Motion (physics); Object-class detection; Pattern recognition (psychology); Face detection","score_opus":0.0368037791736426,"score_gpt":0.28551650189908834,"score_spread":0.24871272272544573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1796589289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08635769,0.0005946356,0.910691,0.0001335236,0.000035645346,0.00007236583,0.0004986899,0.00075030676,0.00086604245],"genre_scores_gemma":[0.6925822,0.0014063375,0.3010557,0.000081506325,0.00009557915,0.00010871287,0.0027330972,0.00010703887,0.0018298567],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996201,0.000067189045,0.000024188355,0.00009786863,0.00014603189,0.000044722525],"domain_scores_gemma":[0.998793,0.0005016884,0.00021036757,0.00012259472,0.00029324563,0.00007902171],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056625414,0.00083446904,0.00062376185,0.0023776079,0.00036253926,0.00078049913,0.000741702,0.0006349785,0.0005586985],"category_scores_gemma":[0.0038391866,0.00043920797,0.00038456894,0.0019967516,0.000512038,0.0012501285,0.00098588,0.00065778184,0.00033357504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00073446357,0.00018869332,0.017114924,0.00040858868,0.00013165131,0.0013298092,0.00069235044,0.45055762,0.06959102,0.017485952,0.003809509,0.43795547],"study_design_scores_gemma":[0.000010337983,0.00007786931,0.003931777,0.000029468463,0.000011590487,0.00026622653,0.0001307546,0.97687244,0.007843837,0.009313525,0.0014965071,0.000015634561],"about_ca_topic_score_codex":0.010559064,"about_ca_topic_score_gemma":0.010149542,"teacher_disagreement_score":0.010559064,"about_ca_system_score_codex":0.00072279887,"about_ca_system_score_gemma":0.0004955681,"threshold_uncertainty_score":0.0209952},"labels":[],"label_agreement":null},{"id":"W1863704471","doi":"10.1109/iccv.2015.496","title":"FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computation; Computer science; Bounded function; Online algorithm; Tracking (education); Inference; Minimum-cost flow problem; Algorithm; Mathematical optimization; Flow network; Artificial intelligence; Mathematics","score_opus":0.06428688319598616,"score_gpt":0.3213269967043053,"score_spread":0.2570401135083191,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1863704471","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00730519,0.0003371752,0.9848422,0.00024526496,0.00009468189,0.00008702322,0.00035027825,0.0045727766,0.0021653613],"genre_scores_gemma":[0.15778202,0.00028719718,0.83195657,0.0002471594,0.0001544641,0.00027771553,0.002042782,0.0005397222,0.0067124697],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992004,0.00013184425,0.00004117709,0.00024801868,0.00025668446,0.00012178805],"domain_scores_gemma":[0.9986765,0.00069821277,0.00010395063,0.00026240773,0.0001964318,0.00006251309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013495152,0.0016813979,0.0015923649,0.0011034255,0.00073595566,0.0015234301,0.003243892,0.0020365773,0.007947695],"category_scores_gemma":[0.005440663,0.00080477924,0.0007765749,0.0016529693,0.0007469022,0.0032769844,0.0018311153,0.001886787,0.0020288678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053850445,0.00027936415,0.0009914072,0.00020485796,0.00006936564,0.00010546497,0.000098845085,0.5178844,0.0039487183,0.018590808,0.023655249,0.43363297],"study_design_scores_gemma":[0.000029600193,0.000023932136,0.00008945571,0.0000058111846,0.0000045519123,0.000023483104,0.0000070683773,0.9913298,0.00081779744,0.0063667903,0.0012957025,0.0000059800277],"about_ca_topic_score_codex":0.012772565,"about_ca_topic_score_gemma":0.012460351,"teacher_disagreement_score":0.012772565,"about_ca_system_score_codex":0.0012141285,"about_ca_system_score_gemma":0.0028899424,"threshold_uncertainty_score":0.026587665},"labels":[],"label_agreement":null},{"id":"W1873421284","doi":"10.1109/pacrim.1999.799484","title":"Real-time image segmentation for action recognition","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"McGill University","keywords":"Artificial intelligence; Computer vision; Computer science; Ghosting; Image segmentation; Image (mathematics); Image processing; Noise (video); Feature detection (computer vision); Segmentation; Background image","score_opus":0.06261352675828731,"score_gpt":0.3474825181733123,"score_spread":0.28486899141502503,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1873421284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011269019,0.001392703,0.9667876,0.00026503883,0.00018936812,0.00014828972,0.00044742116,0.012132836,0.007367708],"genre_scores_gemma":[0.1444208,0.00110915,0.83879536,0.0002178445,0.00010978789,0.00016910228,0.00172116,0.0006677469,0.012788984],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995289,0.00006124067,0.000027457774,0.000127853,0.00019803431,0.00005660661],"domain_scores_gemma":[0.99961567,0.00008474602,0.00004659189,0.00009903609,0.00012021198,0.000033636356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040737074,0.00069747056,0.0006063634,0.0017244682,0.0003923852,0.0008649011,0.0011346817,0.0008814738,0.013849612],"category_scores_gemma":[0.000793689,0.000366403,0.00052266184,0.001281524,0.0004231885,0.0009464023,0.00045967908,0.00052811025,0.0071989973],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003067575,0.00008290698,0.00039735812,0.0002261383,0.000030688225,0.00009516552,0.00008602622,0.0054176752,0.24919125,0.004209525,0.011757418,0.728199],"study_design_scores_gemma":[0.00006400462,0.00033328065,0.007548488,0.000111989626,0.00007185548,0.0013112107,0.00017429564,0.44737992,0.4027778,0.013726808,0.1263934,0.00010690661],"about_ca_topic_score_codex":0.002819537,"about_ca_topic_score_gemma":0.0040533217,"teacher_disagreement_score":0.013849612,"about_ca_system_score_codex":0.0007226814,"about_ca_system_score_gemma":0.0006022924,"threshold_uncertainty_score":0.046331584},"labels":[],"label_agreement":null},{"id":"W1909638996","doi":"10.1117/1.jei.24.5.053020","title":"Automatic parsing of lane and road boundaries in challenging traffic scenes","year":2015,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Cluster analysis; Parsing; Precision and recall; Set (abstract data type); Enhanced Data Rates for GSM Evolution; Frame (networking); Visualization; Pattern recognition (psychology)","score_opus":0.019185270501920915,"score_gpt":0.286365852667976,"score_spread":0.26718058216605506,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1909638996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27634034,0.0010905602,0.6920224,0.00022077087,0.00011465189,0.00038419568,0.0030433466,0.018988147,0.007795604],"genre_scores_gemma":[0.43945605,0.0005986101,0.54566044,0.00013740445,0.000070010734,0.000114943316,0.010052155,0.0010007495,0.0029096003],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995395,0.000041479623,0.000019234156,0.00020644539,0.00010788504,0.00008553223],"domain_scores_gemma":[0.9994035,0.000116068215,0.000095252384,0.00012198043,0.00021612323,0.000047085272],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037005078,0.00081888115,0.0007896884,0.0038696995,0.00081052247,0.0012543,0.001110871,0.0009824922,0.0018509033],"category_scores_gemma":[0.001296775,0.00046157767,0.0006345674,0.0013698373,0.0003240788,0.0012508121,0.000773919,0.00070302584,0.0020543006],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029804124,0.00045609986,0.016427806,0.00034194198,0.0001211033,0.0005196578,0.00047145347,0.031748824,0.13316353,0.003089859,0.017283551,0.79607815],"study_design_scores_gemma":[0.000031802767,0.00020621474,0.04487387,0.00011448206,0.00011689966,0.0012782406,0.0012043201,0.7932665,0.12732461,0.009464201,0.02201949,0.00009937953],"about_ca_topic_score_codex":0.0058535673,"about_ca_topic_score_gemma":0.01812564,"teacher_disagreement_score":0.0058535673,"about_ca_system_score_codex":0.00040581686,"about_ca_system_score_gemma":0.00075143774,"threshold_uncertainty_score":0.011638999},"labels":[],"label_agreement":null},{"id":"W1910583789","doi":"10.1109/crv.2005.24","title":"Body Tracking in HumanWalk from Monocular Video Sequences","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Silhouette; Computer vision; Artificial intelligence; Computer science; Minimum bounding box; Initialization; Tracking (education); Monocular; Segmentation; Context (archaeology); Frame (networking); Optical flow; Image (mathematics); Geography","score_opus":0.03263537499838576,"score_gpt":0.31605545228270215,"score_spread":0.2834200772843164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1910583789","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24310724,0.0012948962,0.7503959,0.000087029664,0.0001163227,0.00010777087,0.0004765586,0.0015910721,0.0028232525],"genre_scores_gemma":[0.659009,0.00086726755,0.3335846,0.000098443124,0.00005116991,0.00006980616,0.0011106026,0.00014562902,0.0050634807],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980026,0.000026261296,0.0000068867353,0.00007125162,0.000065915454,0.00002937532],"domain_scores_gemma":[0.9998617,0.000027353675,0.000028823539,0.000017086864,0.000048074362,0.0000168993],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023966927,0.00037561494,0.0005526127,0.0009325444,0.00026785236,0.00040681483,0.00040841947,0.0003752273,0.00091441715],"category_scores_gemma":[0.0006547528,0.00029362945,0.00019446312,0.00078176934,0.00018770952,0.0004737807,0.00042453787,0.00024013405,0.0005655956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006960099,0.00012267602,0.006995261,0.00036840697,0.00008810779,0.0008018041,0.00032626058,0.031926326,0.17069235,0.0012351993,0.0033343763,0.7834132],"study_design_scores_gemma":[0.000039939623,0.00030265155,0.05173398,0.000082467835,0.000055686283,0.0017921663,0.00028867729,0.8613232,0.07534715,0.0026957642,0.0062975935,0.0000408525],"about_ca_topic_score_codex":0.003839431,"about_ca_topic_score_gemma":0.010021099,"teacher_disagreement_score":0.003839431,"about_ca_system_score_codex":0.0002486529,"about_ca_system_score_gemma":0.00029712587,"threshold_uncertainty_score":0.007634163},"labels":[],"label_agreement":null},{"id":"W1923950849","doi":"10.1007/978-3-540-74260-9_52","title":"Tracking Multiple People in the Context of Video Surveillance","year":2007,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Minimum bounding box; Feature (linguistics); Tracking (education); Video tracking; Context (archaeology); Feature extraction; Pixel; Cluster analysis; Object detection; Tracking system; Pattern recognition (psychology); Matching (statistics); Image (mathematics); Video processing; Kalman filter; Mathematics","score_opus":0.0382191993295249,"score_gpt":0.29477175311917675,"score_spread":0.25655255378965186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1923950849","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12409919,0.002723234,0.8681216,0.00021086994,0.00019569651,0.00005236768,0.00011875187,0.00033535055,0.004142972],"genre_scores_gemma":[0.7659925,0.0024139874,0.22463113,0.00009850969,0.00027718657,0.00004321831,0.00038843157,0.00006975279,0.0060851937],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995122,0.00009311679,0.000018329105,0.00018189634,0.00013267306,0.00006184864],"domain_scores_gemma":[0.99931824,0.00040233036,0.00008604158,0.000055694632,0.00008462132,0.000053112264],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006058262,0.00051994354,0.00097016554,0.0008820485,0.00035961336,0.0009198915,0.0009317185,0.0013388091,0.00076020934],"category_scores_gemma":[0.0021527624,0.0004724447,0.0004269486,0.0011367357,0.00036771272,0.0013172012,0.0009952423,0.00084156357,0.00028796165],"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":[0.00092237804,0.00024104297,0.012213565,0.00038417985,0.00022940765,0.0015474528,0.0009339025,0.14263205,0.10104055,0.009797702,0.0049779164,0.7250799],"study_design_scores_gemma":[0.000023453042,0.00019133465,0.008182381,0.00005170025,0.00010206831,0.0013714433,0.00030525893,0.95320326,0.021718344,0.010566695,0.004244767,0.000039347124],"about_ca_topic_score_codex":0.002662757,"about_ca_topic_score_gemma":0.003328103,"teacher_disagreement_score":0.002662757,"about_ca_system_score_codex":0.00026117289,"about_ca_system_score_gemma":0.00020586506,"threshold_uncertainty_score":0.0052945614},"labels":[],"label_agreement":null},{"id":"W1954025331","doi":"","title":"Fusion of spatial and visual information for object tracking on iPhone","year":2013,"lang":"en","type":"article","venue":"International Conference on Information Fusion","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Computer science; Video tracking; Artificial intelligence; Tracking (education); Object (grammar); Tracking system; Matching (statistics); Motion (physics); Eye tracking; Sensor fusion; Visualization; Match moving; Computer graphics (images); Mathematics","score_opus":0.031502281182184066,"score_gpt":0.3121970779035453,"score_spread":0.2806947967213612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1954025331","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.056608226,0.0019715428,0.93744534,0.000115720504,0.00019954058,0.00005273592,0.000099584024,0.0009404763,0.0025668002],"genre_scores_gemma":[0.7171776,0.00138627,0.27754715,0.00020830044,0.0001372948,0.00007186961,0.00034505178,0.000064398686,0.0030620531],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995945,0.000049553866,0.000025277463,0.000101238664,0.00018161282,0.000047897753],"domain_scores_gemma":[0.99973005,0.00005407182,0.00003169193,0.00005393524,0.00011484806,0.00001539892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004885871,0.00039867192,0.00053426164,0.0010207581,0.00024507885,0.000519864,0.00042797747,0.0005524318,0.0008887969],"category_scores_gemma":[0.0010507782,0.00021545582,0.00040773785,0.0007624977,0.00018516571,0.0010771372,0.00061141665,0.0002780543,0.0004934462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004782178,0.000077091245,0.0017904937,0.00018714897,0.00008931627,0.00019481026,0.00010571044,0.015079247,0.15133847,0.001461937,0.0015483372,0.82764924],"study_design_scores_gemma":[0.000052925712,0.0006262617,0.016185846,0.00007880285,0.00034705046,0.0011132357,0.00016498523,0.79166764,0.17020139,0.0052102967,0.014260924,0.00009056125],"about_ca_topic_score_codex":0.0012971523,"about_ca_topic_score_gemma":0.0018092343,"teacher_disagreement_score":0.0012971523,"about_ca_system_score_codex":0.00024413597,"about_ca_system_score_gemma":0.0003111879,"threshold_uncertainty_score":0.002973318},"labels":[],"label_agreement":null},{"id":"W1957641636","doi":"10.1371/journal.pone.0133036","title":"A Deep-Structured Conditional Random Field Model for Object Silhouette Tracking","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Ministero dello Sviluppo Economico; Shiraz University; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Ontario Ministry of Economic Development and Innovation","keywords":"Silhouette; Conditional random field; Artificial intelligence; Computer science; Video tracking; Context (archaeology); Computer vision; Probabilistic logic; Tracking (education); Object (grammar); Active appearance model; Pattern recognition (psychology); Markov random field; Image segmentation; Image (mathematics); Geography","score_opus":0.1201658419493417,"score_gpt":0.3015773961163503,"score_spread":0.1814115541670086,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1957641636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003439459,0.00020625745,0.99480647,0.000095178446,0.000032488868,0.000017475244,0.0002071848,0.0008018544,0.00039359197],"genre_scores_gemma":[0.42572445,0.0008295913,0.563725,0.00040151764,0.00018410923,0.0002383473,0.0026806735,0.00039882562,0.005817504],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940157,0.00011879962,0.00002622395,0.00025548125,0.00013598768,0.000061972096],"domain_scores_gemma":[0.9989924,0.0004942081,0.000121997015,0.0001334173,0.00020782319,0.000050249535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012315313,0.00089890923,0.0010943329,0.0010537374,0.00037515306,0.00069120084,0.0026043197,0.0012971491,0.0021730908],"category_scores_gemma":[0.0033509252,0.00076477573,0.0012548012,0.001312737,0.00068284734,0.0016760197,0.0008485294,0.0018596435,0.0009028818],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014623952,0.00005296949,0.0010735308,0.00010096748,0.0000653579,0.00011462194,0.00008099491,0.8561233,0.0050977417,0.01919402,0.004372756,0.11357752],"study_design_scores_gemma":[0.0000030054125,0.000007532109,0.000095389965,0.0000039836477,0.000004985509,0.000016940103,0.0000013038311,0.9960594,0.00037755808,0.0029970394,0.0004269164,0.0000058408305],"about_ca_topic_score_codex":0.020055799,"about_ca_topic_score_gemma":0.022803226,"teacher_disagreement_score":0.020055799,"about_ca_system_score_codex":0.0013743261,"about_ca_system_score_gemma":0.0014213029,"threshold_uncertainty_score":0.03987813},"labels":[],"label_agreement":null},{"id":"W195830232","doi":"10.1007/978-3-642-19730-7_4","title":"An Improved Mean-shift Tracking Algorithm Based on Adaptive Multiple Feature Fusion","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in electrical engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Mean-shift; Weighting; Feature (linguistics); Artificial intelligence; Tracking (education); Pattern recognition (psychology); Enhanced Data Rates for GSM Evolution; Computer science; Video tracking; Fusion; Computer vision; Algorithm; Variance (accounting); Measure (data warehouse); Scheme (mathematics); Object (grammar); Mathematics; Data mining","score_opus":0.01619930769530423,"score_gpt":0.23373884946265988,"score_spread":0.21753954176735565,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W195830232","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":[0.004689699,0.00037593071,0.9935056,0.000054987268,0.00016635022,0.000021913638,0.000022623142,0.0004918019,0.0006711344],"genre_scores_gemma":[0.07833856,0.00041941289,0.91673106,0.00012346436,0.00014353458,0.000081571816,0.0001824184,0.00008481545,0.0038952257],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993198,0.000070001675,0.000038617476,0.00018970662,0.0003406198,0.000041360545],"domain_scores_gemma":[0.9994282,0.00011851546,0.0000367911,0.00009015046,0.00030036145,0.000025967418],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078496506,0.0007286512,0.0014692582,0.00086105365,0.0005995787,0.0007674551,0.0013740963,0.0013300165,0.001602793],"category_scores_gemma":[0.0013874823,0.0005396483,0.00091363036,0.0017092533,0.0003677392,0.0013209066,0.0010256852,0.001177201,0.0014001356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026538508,0.00008146858,0.0006872434,0.00011526873,0.0001241087,0.00007438151,0.00007722535,0.03462955,0.0806406,0.00475445,0.0032669192,0.87528336],"study_design_scores_gemma":[0.000049952072,0.00014282473,0.0013940502,0.000014248577,0.000097295044,0.00038697073,0.000014554916,0.9611085,0.025637973,0.002751515,0.008344295,0.000057762296],"about_ca_topic_score_codex":0.0032694484,"about_ca_topic_score_gemma":0.003617412,"teacher_disagreement_score":0.0032694484,"about_ca_system_score_codex":0.00038039443,"about_ca_system_score_gemma":0.00097482646,"threshold_uncertainty_score":0.0065007806},"labels":[],"label_agreement":null},{"id":"W1961030615","doi":"10.1109/crv.2005.65","title":"Real-Time Video Surveillance with Self-Organizing Maps","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Optical flow; Background subtraction; Motion detection; Pattern recognition (psychology); Trajectory; Tracking (education); Object detection; Matching (statistics); Motion (physics); Pixel; Image (mathematics); Mathematics","score_opus":0.009547286091440371,"score_gpt":0.24107904665248087,"score_spread":0.2315317605610405,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1961030615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05671034,0.0005030072,0.9387145,0.00012936616,0.00007962458,0.000051723786,0.00006231647,0.0019378341,0.001811289],"genre_scores_gemma":[0.66913944,0.00021738975,0.3284591,0.00007292596,0.00006103027,0.00009684191,0.00013081836,0.00006299208,0.0017594638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974054,0.00008052076,0.000010556579,0.000048965583,0.000099481564,0.000019795436],"domain_scores_gemma":[0.99974304,0.000117595555,0.000022576234,0.000036494363,0.00006705627,0.000013228201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038438183,0.0002752012,0.00027148082,0.0005119665,0.00016714518,0.00044170997,0.0004940425,0.0003113063,0.0006282709],"category_scores_gemma":[0.00089578377,0.00017437799,0.000246625,0.00042760762,0.00020303788,0.0006444068,0.00039250642,0.0002578043,0.00022982444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037269067,0.00019530603,0.001965771,0.0001699355,0.00013989343,0.00018676012,0.00026155254,0.14038993,0.047900356,0.0063857036,0.0033971611,0.798635],"study_design_scores_gemma":[0.000011784302,0.000046631336,0.0011925929,0.0000059849363,0.000012740168,0.0000756226,0.000032847973,0.98263484,0.010917554,0.0033570097,0.0017004225,0.000011980865],"about_ca_topic_score_codex":0.0012364202,"about_ca_topic_score_gemma":0.001216841,"teacher_disagreement_score":0.0012364202,"about_ca_system_score_codex":0.00023493996,"about_ca_system_score_gemma":0.00014711621,"threshold_uncertainty_score":0.0024585128},"labels":[],"label_agreement":null},{"id":"W196186286","doi":"","title":"An Object Assignment Algorithm for Tracking Performance Evaluation","year":2009,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Benchmark (surveying); Tracking (education); Set (abstract data type); Ground truth; Artificial intelligence; Object (grammar); Algorithm; Video tracking; Object detection; Data mining; Machine learning; Pattern recognition (psychology)","score_opus":0.026232836171501267,"score_gpt":0.3020318938269015,"score_spread":0.27579905765540025,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W196186286","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":[0.0038675137,0.0002632559,0.99084306,0.000056587724,0.000060984006,0.00023407565,0.00013251384,0.0033823203,0.0011596512],"genre_scores_gemma":[0.07221433,0.00022123872,0.92348284,0.00006897844,0.00009964167,0.0007326325,0.0007766961,0.00033846535,0.0020652092],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99209374,0.0020562294,0.00072753563,0.0011214791,0.00366716,0.00033379908],"domain_scores_gemma":[0.99026674,0.003643762,0.000657571,0.0013530061,0.0038370392,0.00024189461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006790328,0.0023904855,0.0023426586,0.004325288,0.0011684,0.0024969308,0.00294579,0.0026747622,0.0055603352],"category_scores_gemma":[0.023366395,0.00056516443,0.0011719279,0.0037245103,0.0007233087,0.0028524487,0.001730162,0.0019974902,0.003491249],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004203461,0.00042803763,0.0022016268,0.00030948067,0.00016707048,0.00007957586,0.000118411306,0.16847424,0.012920203,0.009741618,0.010590943,0.7945485],"study_design_scores_gemma":[0.000038375067,0.0002294035,0.0010179189,0.00003173278,0.00003290304,0.00015669715,0.000021692378,0.98286575,0.008175602,0.0034835262,0.0039093494,0.000037013502],"about_ca_topic_score_codex":0.0025026512,"about_ca_topic_score_gemma":0.0014706685,"teacher_disagreement_score":0.006790328,"about_ca_system_score_codex":0.0017275014,"about_ca_system_score_gemma":0.001874263,"threshold_uncertainty_score":0.035911083},"labels":[],"label_agreement":null},{"id":"W1963590456","doi":"10.1145/2659021.2659050","title":"A Negotiation Protocol with Conditional Offers for Camera Handoffs","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Handover; Negotiation; Task (project management); Protocol (science); State (computer science); Scheme (mathematics); Camera auto-calibration; Computer vision; Artificial intelligence; Real-time computing; Computer network; Camera resectioning; Engineering; Algorithm","score_opus":0.016836429248691883,"score_gpt":0.29865505367482154,"score_spread":0.28181862442612965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1963590456","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02013027,0.00031470665,0.9688552,0.0005053115,0.0001661682,0.00022830095,0.0000657014,0.00029312828,0.009441159],"genre_scores_gemma":[0.7606875,0.0005338333,0.22656915,0.00021859701,0.00015409598,0.0006325845,0.00016813868,0.00012028483,0.0109159015],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975303,0.0009783414,0.00019844498,0.000387848,0.00064450386,0.00026054622],"domain_scores_gemma":[0.99639636,0.002108119,0.00038791457,0.0004434769,0.00031770155,0.00034641183],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033461605,0.00063272606,0.0007978144,0.0005456895,0.001298194,0.0023370886,0.0023981838,0.0015665822,0.006163448],"category_scores_gemma":[0.00921825,0.0004765693,0.0009354571,0.00077754195,0.0015347522,0.005715156,0.002834075,0.0024457518,0.0007580015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007405405,0.00027078047,0.000909791,0.00027120547,0.00011128428,0.00082186406,0.0014011245,0.18343423,0.008916989,0.71159905,0.004886498,0.08663667],"study_design_scores_gemma":[0.00010442082,0.00014576653,0.00014376141,0.000036346984,0.000044725042,0.00027728267,0.00018896397,0.8894587,0.0025072033,0.09498062,0.012050556,0.000061668245],"about_ca_topic_score_codex":0.0016621216,"about_ca_topic_score_gemma":0.0009576803,"teacher_disagreement_score":0.006163448,"about_ca_system_score_codex":0.0011977257,"about_ca_system_score_gemma":0.001806294,"threshold_uncertainty_score":0.020618796},"labels":[],"label_agreement":null},{"id":"W1964309827","doi":"10.1117/12.631536","title":"Multi-information fusion for human motion tracking by particle filter","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Particle filter; Fuse (electrical); Tracking (education); Boundary (topology); Filter (signal processing); Property (philosophy); Curse of dimensionality; Mathematics; Physics","score_opus":0.04953396741575158,"score_gpt":0.32639186768095907,"score_spread":0.2768579002652075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1964309827","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037065397,0.00013308073,0.99562967,0.000047892067,0.00002701083,0.000009893885,0.0000104103065,0.00016675596,0.00026867952],"genre_scores_gemma":[0.37097022,0.0004766356,0.62632364,0.00009593836,0.00009108686,0.00011776348,0.0001582233,0.00008354528,0.0016829737],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924785,0.0001888866,0.00004365589,0.00016666179,0.0002950706,0.000057957237],"domain_scores_gemma":[0.99930894,0.0003238526,0.00006796092,0.000097795346,0.0001712206,0.00003030041],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018642109,0.00073064887,0.0014243224,0.0013295606,0.0005784555,0.00079528114,0.00089531275,0.0012135636,0.001167212],"category_scores_gemma":[0.0034558037,0.00053206773,0.0010912155,0.0011594291,0.00060611556,0.0022019362,0.0012544648,0.0011407553,0.0005516378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030235227,0.00010811827,0.0010806071,0.0001576193,0.00014267188,0.00014453949,0.0002078999,0.53854835,0.024762249,0.02442367,0.0022397381,0.40788212],"study_design_scores_gemma":[0.0000083786645,0.00002070214,0.00021456408,0.000003960013,0.000010598111,0.000014975607,0.0000046715436,0.9944794,0.0019283954,0.002816611,0.00048752935,0.000010159767],"about_ca_topic_score_codex":0.0050304895,"about_ca_topic_score_gemma":0.0035861982,"teacher_disagreement_score":0.0050304895,"about_ca_system_score_codex":0.0010074486,"about_ca_system_score_gemma":0.0007926766,"threshold_uncertainty_score":0.010002434},"labels":[],"label_agreement":null},{"id":"W1965594729","doi":"10.1109/wacv.2014.6836010","title":"Urban Tracker: Multiple object tracking in urban mixed traffic","year":2014,"lang":"en","type":"article","venue":"IEEE Winter Conference on Applications of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":117,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada","keywords":"Background subtraction; Computer vision; Computer science; Artificial intelligence; Feature (linguistics); Object detection; Tracking (education); Video tracking; Object (grammar); Pattern recognition (psychology); Pixel","score_opus":0.027266957231979205,"score_gpt":0.3038622739867174,"score_spread":0.2765953167547382,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965594729","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10599332,0.0005539081,0.8869172,0.00007764808,0.000100697536,0.00008504137,0.00019724235,0.0040726326,0.0020023757],"genre_scores_gemma":[0.6561749,0.00034080696,0.3387556,0.00007930052,0.00008291124,0.00008390739,0.00069137895,0.00022962916,0.0035615554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995431,0.00007620521,0.000014254403,0.00016590657,0.0001383254,0.00006217867],"domain_scores_gemma":[0.9995635,0.0001238425,0.00008652669,0.0000530131,0.0001218588,0.000051174706],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078216166,0.00050842494,0.00065277395,0.0012366287,0.0004622139,0.00084405707,0.00083591265,0.0006736542,0.0008594049],"category_scores_gemma":[0.0011544559,0.00034458487,0.0002648687,0.0013126038,0.00025257294,0.00081249897,0.0007449268,0.000389777,0.0005020382],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084318104,0.00038872592,0.028425219,0.0003116856,0.0002607173,0.0012268142,0.0005401322,0.15746386,0.073739424,0.0067734034,0.0099204695,0.72010636],"study_design_scores_gemma":[0.000026224328,0.00013485624,0.0057830634,0.000013618723,0.000030325627,0.0004403972,0.000074852716,0.9777516,0.009843296,0.0011850961,0.0046945876,0.000022090555],"about_ca_topic_score_codex":0.0040020547,"about_ca_topic_score_gemma":0.006169138,"teacher_disagreement_score":0.0040020547,"about_ca_system_score_codex":0.00035187593,"about_ca_system_score_gemma":0.00046580544,"threshold_uncertainty_score":0.007957518},"labels":[],"label_agreement":null},{"id":"W1965678663","doi":"10.1109/newcas.2014.6934064","title":"Vehicle detection using TD2DHOG features","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Discrete cosine transform; Histogram; Object detection; Pyramid (geometry); Pattern recognition (psychology); Classifier (UML); Computer vision; Pedestrian detection; Histogram of oriented gradients; Feature extraction; Image (mathematics); Mathematics; Engineering","score_opus":0.02224505232704343,"score_gpt":0.28528247268696044,"score_spread":0.263037420359917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1965678663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.24189885,0.000889356,0.74336493,0.00022729505,0.00021044142,0.00021753667,0.0015634825,0.0038025207,0.007825595],"genre_scores_gemma":[0.7200099,0.00062518823,0.268907,0.00018767064,0.000082341554,0.00009203516,0.0038982555,0.0001583009,0.006039314],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974567,0.000012775877,0.000008708013,0.00006315637,0.0001087643,0.000060937222],"domain_scores_gemma":[0.9997863,0.000028318465,0.00002934481,0.000034493478,0.00010157551,0.0000200511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016179183,0.0005003509,0.0005861058,0.0022114802,0.00020032706,0.0007310528,0.00050063356,0.00043580565,0.0015766803],"category_scores_gemma":[0.0004588797,0.00023011981,0.00043977608,0.0012303917,0.00020940536,0.0007009382,0.0007446748,0.0003253409,0.0012481029],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046130034,0.00019843988,0.010002485,0.00015359037,0.00009983914,0.00034038132,0.00006502731,0.014813669,0.1518591,0.0013414271,0.006837279,0.81382746],"study_design_scores_gemma":[0.00007596922,0.0004313569,0.040503386,0.000042702948,0.00013294263,0.0014713744,0.00024699926,0.7027507,0.22223301,0.004014403,0.028005086,0.000092015376],"about_ca_topic_score_codex":0.0036052007,"about_ca_topic_score_gemma":0.0053693764,"teacher_disagreement_score":0.0036052007,"about_ca_system_score_codex":0.00033259444,"about_ca_system_score_gemma":0.0004650619,"threshold_uncertainty_score":0.007168412},"labels":[],"label_agreement":null},{"id":"W1969397097","doi":"10.1109/fskd.2010.5569091","title":"Road vehicle detection using fuzzy logic rule-based method","year":2010,"lang":"en","type":"article","venue":"2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Fuzzy logic; Object detection; Orthophoto; Preprocessor; Image segmentation; Detector; Fuzzy rule; Segmentation; Set (abstract data type); Fuzzy set; Pattern recognition (psychology)","score_opus":0.06239997226372771,"score_gpt":0.3468651914125755,"score_spread":0.28446521914884776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969397097","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":[0.041631803,0.00024053387,0.9517089,0.00007224395,0.000061574225,0.00027258293,0.0001353616,0.0017870306,0.004089959],"genre_scores_gemma":[0.44178343,0.00019247411,0.5545727,0.00011695289,0.000032609663,0.00022748503,0.00025829292,0.000037537713,0.002778528],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916816,0.000113824455,0.00008235329,0.00021190102,0.00036538392,0.000058393445],"domain_scores_gemma":[0.99897516,0.00033175386,0.00008634992,0.000090743866,0.00049061707,0.000025407442],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008259494,0.00049873575,0.00087593414,0.0014507931,0.00037823274,0.0010016779,0.0012130857,0.0007471706,0.0018149717],"category_scores_gemma":[0.002112796,0.00029965147,0.00058950373,0.0006544636,0.0003062425,0.0005733791,0.00022501308,0.00041972572,0.0007703464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004609736,0.0004080869,0.0028729034,0.00033842374,0.00016973786,0.00040341332,0.00019689262,0.12201033,0.08873079,0.0038178226,0.0026568274,0.7779339],"study_design_scores_gemma":[0.000052365915,0.000122522,0.001320955,0.000026325035,0.000068848734,0.00019668324,0.000029707808,0.9704305,0.025163865,0.0011101253,0.0014369726,0.000041062674],"about_ca_topic_score_codex":0.005138646,"about_ca_topic_score_gemma":0.0042340937,"teacher_disagreement_score":0.005138646,"about_ca_system_score_codex":0.0005474871,"about_ca_system_score_gemma":0.00056312006,"threshold_uncertainty_score":0.010217428},"labels":[],"label_agreement":null},{"id":"W1969652359","doi":"10.1109/acvmot.2005.42","title":"Detecting Motion Patterns via Direction Maps with Application to Surveillance","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Computer vision; Traverse; Motion (physics); False positive paradox; Motion capture; Representation (politics); Motion detection; Pattern recognition (psychology); Geography","score_opus":0.010968688416281581,"score_gpt":0.2601156892918445,"score_spread":0.2491470008755629,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1969652359","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12747009,0.00097382284,0.86403435,0.00017711087,0.00008352946,0.00010415961,0.00035466504,0.0032952044,0.0035071315],"genre_scores_gemma":[0.5713067,0.0008247212,0.42554015,0.00004410225,0.00006690152,0.00008061571,0.0004813061,0.00009073442,0.0015647756],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99974006,0.0000736184,0.000011795581,0.000044397726,0.0001048576,0.000025246978],"domain_scores_gemma":[0.9992681,0.0002989175,0.00008778391,0.00008826964,0.00021866748,0.00003816319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003421321,0.00038407304,0.00034523854,0.0016202666,0.00023030637,0.0006526965,0.00042368073,0.0003578765,0.0008482824],"category_scores_gemma":[0.0026993656,0.00021099266,0.00024323145,0.0014633678,0.00026351606,0.00061542937,0.00043417932,0.0003209411,0.00032602245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039257985,0.00008990146,0.00809837,0.00012361606,0.00004631665,0.00033589097,0.0001900102,0.059648823,0.061237462,0.005610591,0.002894751,0.86133164],"study_design_scores_gemma":[0.000049162518,0.0002169665,0.014841287,0.000033259406,0.00004064409,0.0013872387,0.0001885408,0.91619706,0.047705125,0.009422811,0.009833761,0.000084186795],"about_ca_topic_score_codex":0.002769107,"about_ca_topic_score_gemma":0.002113856,"teacher_disagreement_score":0.002769107,"about_ca_system_score_codex":0.00019924655,"about_ca_system_score_gemma":0.00024365506,"threshold_uncertainty_score":0.005505979},"labels":[],"label_agreement":null},{"id":"W1970428255","doi":"10.1109/acvmot.2005.95","title":"Pre-Attentive Face Detection for Foveated Wide-Field Surveillance","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Image resolution; Face (sociological concept); Probabilistic logic; Parametric statistics; Field of view; Field (mathematics); Object detection; Pattern recognition (psychology); Mathematics","score_opus":0.01843738379682973,"score_gpt":0.2955665255908486,"score_spread":0.2771291417940189,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1970428255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06826101,0.00036922822,0.9280836,0.00009688782,0.000039789735,0.000051846273,0.000040996845,0.00074986415,0.0023067347],"genre_scores_gemma":[0.4927049,0.00037214495,0.5041356,0.000130252,0.000060082977,0.000056902616,0.00008245376,0.0000681878,0.0023895164],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997701,0.000045093384,0.0000055698324,0.00006977515,0.000086212836,0.000023312381],"domain_scores_gemma":[0.9994666,0.00025515453,0.000054234584,0.000076430595,0.000110070025,0.00003748353],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041761884,0.00032409024,0.00037812837,0.0003246797,0.00022228365,0.00039938913,0.0006548526,0.00039217653,0.0023214081],"category_scores_gemma":[0.0013501407,0.0002864062,0.00026369726,0.00015700542,0.00039211242,0.0006899389,0.0005352905,0.00056796195,0.0006222842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004214468,0.0001316342,0.0023725408,0.00015056327,0.00004217884,0.00020296028,0.00014590545,0.013622548,0.60371006,0.0043909713,0.0019239336,0.3728853],"study_design_scores_gemma":[0.000053059164,0.0007037732,0.018216165,0.00007251207,0.0000829691,0.002243948,0.000094196235,0.6016344,0.3606329,0.0072016255,0.008975462,0.00008902965],"about_ca_topic_score_codex":0.000634324,"about_ca_topic_score_gemma":0.0018797644,"teacher_disagreement_score":0.0023214081,"about_ca_system_score_codex":0.00032802744,"about_ca_system_score_gemma":0.0003401578,"threshold_uncertainty_score":0.007765949},"labels":[],"label_agreement":null},{"id":"W1971322191","doi":"10.1016/j.imavis.2014.01.007","title":"Adaptive on-line similarity measure for direct visual tracking","year":2014,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Measure (data warehouse); Artificial intelligence; Similarity measure; Eye tracking; Computer vision; Line (geometry); Similarity (geometry); Computer science; Structural similarity; Tracking (education); Pattern recognition (psychology); Image (mathematics); Mathematics; Data mining; Psychology","score_opus":0.05290856011846713,"score_gpt":0.3842307644521508,"score_spread":0.3313222043336837,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1971322191","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":[0.008604093,0.00011325489,0.99033326,0.000026270936,0.00003704898,0.00002684261,0.000025382291,0.00021543389,0.00061845075],"genre_scores_gemma":[0.4578278,0.00033070237,0.53560823,0.00016041045,0.00014815111,0.00018086872,0.00038434236,0.0001590544,0.0052004224],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987632,0.00025185256,0.00007051625,0.00031269575,0.0005156824,0.00008608175],"domain_scores_gemma":[0.99877936,0.0003064316,0.00012420978,0.00028781538,0.00043040057,0.00007185201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007400471,0.00052029255,0.0011159924,0.0011513612,0.00038566816,0.0010576713,0.0015778526,0.0010166866,0.0017954927],"category_scores_gemma":[0.0037038217,0.0003008503,0.0006121466,0.0014281383,0.00044162534,0.0014561012,0.0015734258,0.0009928046,0.00090104743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040724917,0.00032579544,0.0013339224,0.00012809495,0.00010228011,0.00009764365,0.000108707485,0.111267745,0.050761078,0.015242282,0.0040876106,0.81613755],"study_design_scores_gemma":[0.000015408694,0.000117871496,0.0008350509,0.00000639855,0.00001677617,0.00012842422,0.00001562924,0.9859633,0.007677641,0.0036297285,0.0015803643,0.000013508848],"about_ca_topic_score_codex":0.0025035026,"about_ca_topic_score_gemma":0.0023717484,"teacher_disagreement_score":0.0025035026,"about_ca_system_score_codex":0.0006556538,"about_ca_system_score_gemma":0.00088389986,"threshold_uncertainty_score":0.006006539},"labels":[],"label_agreement":null},{"id":"W1974698510","doi":"10.1109/smc.2014.6973953","title":"Dual Gaussian mixture model with pixel history for background suppression","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Mixture model; Computer science; Pixel; Artificial intelligence; Dual (grammatical number); Object detection; Computer vision; Gaussian; Pattern recognition (psychology); Assimilation (phonology); Object (grammar); Gaussian process","score_opus":0.040590081014604415,"score_gpt":0.27833366154294525,"score_spread":0.23774358052834083,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1974698510","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":[0.007996183,0.00074490765,0.98912024,0.00009435063,0.00011822738,0.00002714853,0.00006961207,0.0008497203,0.0009797189],"genre_scores_gemma":[0.3697032,0.0022152918,0.61554265,0.00028599572,0.000263679,0.00014167547,0.00094803656,0.00038857135,0.010510796],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916375,0.00015350452,0.000035322944,0.00022600744,0.0003225358,0.000098788085],"domain_scores_gemma":[0.9995035,0.00014129198,0.00004683465,0.00008054575,0.00018953353,0.000038318074],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010807064,0.0011144149,0.0013118958,0.0012293555,0.0004206033,0.0009544525,0.0015417455,0.0010302038,0.0015724322],"category_scores_gemma":[0.0017950756,0.00052320387,0.0013932748,0.0012646188,0.00051325763,0.0014056078,0.0011622178,0.0015277171,0.0015491202],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006487018,0.00020792981,0.004058805,0.00030320478,0.00027070066,0.00027618615,0.00024915935,0.2098385,0.055505004,0.015480867,0.006152912,0.707008],"study_design_scores_gemma":[0.000009032379,0.000052582225,0.00091016415,0.000010846507,0.00005820777,0.00015896244,0.0000181566,0.9829784,0.009359115,0.0023641083,0.0040476494,0.000032754517],"about_ca_topic_score_codex":0.00624441,"about_ca_topic_score_gemma":0.0053198095,"teacher_disagreement_score":0.00624441,"about_ca_system_score_codex":0.0006597014,"about_ca_system_score_gemma":0.00095427886,"threshold_uncertainty_score":0.012416124},"labels":[],"label_agreement":null},{"id":"W1976220907","doi":"10.1007/s11042-012-1342-3","title":"Guest editorial: Advances in multimedia surveillance","year":2013,"lang":"en","type":"editorial","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Multimedia","score_opus":0.013575123445665211,"score_gpt":0.29981699778473503,"score_spread":0.28624187433906984,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1976220907","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":[0.00002289277,0.009262623,0.00026216457,0.016203174,0.9726752,0.0000126278865,0.00003736495,0.00005453819,0.0014693778],"genre_scores_gemma":[0.00028837987,0.0051696543,0.000110979396,0.007156804,0.97967577,0.000013076137,0.000025236712,0.000043398402,0.0075167366],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9952728,0.0007602839,0.0004903073,0.0005947844,0.002571686,0.00031009555],"domain_scores_gemma":[0.9819495,0.0051613916,0.0014133039,0.00045994102,0.008110204,0.0029056554],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0055304314,0.0036301939,0.0038738432,0.005680685,0.0024097655,0.0080462005,0.0027971044,0.012388771,0.01769912],"category_scores_gemma":[0.02039983,0.00096959446,0.0021924847,0.0019041571,0.0021169954,0.0035748936,0.0019472389,0.013743025,0.01299657],"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":[0.00002632241,0.000009108797,0.00001480575,0.00015226754,0.000013894344,0.00008723135,0.000004935252,0.00001955942,0.000055076805,0.0002108887,0.99309075,0.0063150628],"study_design_scores_gemma":[0.0000385754,0.000024671757,0.00018465707,0.00033419667,0.000052203915,0.0003090233,0.00001827587,0.00018181607,0.00013481689,0.0008818267,0.99782276,0.000017124441],"about_ca_topic_score_codex":0.001023234,"about_ca_topic_score_gemma":0.0036738615,"teacher_disagreement_score":0.01769912,"about_ca_system_score_codex":0.002315448,"about_ca_system_score_gemma":0.0022674322,"threshold_uncertainty_score":0.059209466},"labels":[],"label_agreement":null},{"id":"W1977018441","doi":"10.1007/s00530-006-0059-4","title":"Surveillance camera scheduling: a virtual vision approach","year":2006,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":70,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"University of Pennsylvania","keywords":"Computer science; Smart camera; Computer vision; Artificial intelligence; Zoom; Pedestrian; Field of view; Real-time computing; Computer graphics (images)","score_opus":0.01784174888210121,"score_gpt":0.26879248223465263,"score_spread":0.2509507333525514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1977018441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055486597,0.00022853988,0.9921969,0.000113485665,0.00010742865,0.00004121271,0.000018769877,0.0002711479,0.0014737776],"genre_scores_gemma":[0.50338846,0.0005821839,0.4899057,0.00020139327,0.0004411355,0.00014156273,0.00012589098,0.00032924963,0.0048844405],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985215,0.00050037506,0.00006058059,0.0003304922,0.00035300662,0.00023400903],"domain_scores_gemma":[0.99826837,0.0007657602,0.00017912929,0.00019383186,0.00037945475,0.00021337553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020458857,0.0014317005,0.002202568,0.0017122151,0.0012370799,0.002193068,0.0029392862,0.0012453479,0.0044498113],"category_scores_gemma":[0.0040510083,0.001079487,0.0010667655,0.0017239755,0.0008117503,0.0020524443,0.0013967549,0.0011159887,0.0004677267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045787834,0.00030788436,0.0005548561,0.00014318485,0.00010162404,0.00008923041,0.000113924725,0.7290669,0.0076638344,0.031829376,0.004851475,0.22481985],"study_design_scores_gemma":[0.000015538124,0.000039939565,0.00007090426,0.000003774421,0.000014257251,0.000017685574,0.000015151646,0.9917915,0.0009865302,0.006354614,0.0006813216,0.000008737462],"about_ca_topic_score_codex":0.007607125,"about_ca_topic_score_gemma":0.006582072,"teacher_disagreement_score":0.007607125,"about_ca_system_score_codex":0.0018269871,"about_ca_system_score_gemma":0.0026725854,"threshold_uncertainty_score":0.015125692},"labels":[],"label_agreement":null},{"id":"W1980508392","doi":"10.1109/tip.2012.2214049","title":"Video Object Tracking in the Compressed Domain Using Spatio-Temporal Markov Random Fields","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; Video tracking; Motion compensation; Computer science; Motion estimation; Markov random field; Block-matching algorithm; Block (permutation group theory); Markov chain; Pixel; Pattern recognition (psychology); Object (grammar); Mathematics; Image segmentation; Image (mathematics); Machine learning","score_opus":0.03635832405567782,"score_gpt":0.3198126644037912,"score_spread":0.28345434034811334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1980508392","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011933677,0.00033606004,0.9867982,0.00006941164,0.00002230588,0.000018474102,0.000030080317,0.00033182788,0.00045983924],"genre_scores_gemma":[0.41931623,0.0012526133,0.576186,0.00014802766,0.00010665643,0.00007516071,0.00032771227,0.00007169956,0.0025158597],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997402,0.00006081903,0.000012266411,0.000049158054,0.00011838469,0.0000192155],"domain_scores_gemma":[0.99951625,0.00026795722,0.00006308283,0.000050054623,0.00008886421,0.000013860921],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078342686,0.00035924194,0.00052741833,0.00066694885,0.00018405798,0.0003823495,0.0005636009,0.00054909725,0.00054368493],"category_scores_gemma":[0.0018426575,0.00021825268,0.00052832195,0.0005743631,0.00026066316,0.0008555501,0.0002968249,0.00052157685,0.0001920031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029783585,0.00008082906,0.0015049898,0.00014013374,0.00007905642,0.00017983936,0.00011170975,0.5013778,0.032334223,0.013199023,0.0016117147,0.44908288],"study_design_scores_gemma":[0.000007348712,0.000021185444,0.00030315985,0.000006732428,0.000010298192,0.00004864399,0.0000040655996,0.9954266,0.002499667,0.001164161,0.0005013622,0.0000067974006],"about_ca_topic_score_codex":0.0073983027,"about_ca_topic_score_gemma":0.006444832,"teacher_disagreement_score":0.0073983027,"about_ca_system_score_codex":0.0005418494,"about_ca_system_score_gemma":0.0006150447,"threshold_uncertainty_score":0.014710486},"labels":[],"label_agreement":null},{"id":"W1982807263","doi":"10.5539/mas.v3n11p80","title":"Moving Objects Segmentation Based on Histogram for Video Surveillance","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Background subtraction; Computer science; Histogram; Color histogram; Histogram matching; Segmentation; Feature (linguistics); Background image; Pattern recognition (psychology); Image histogram; Image segmentation; Image (mathematics); Color image; Image processing; Pixel; Image texture","score_opus":0.020874350886607378,"score_gpt":0.2919378153553886,"score_spread":0.2710634644687812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1982807263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020316694,0.0010288751,0.97486204,0.00007061084,0.00005745238,0.000036922553,0.000090624795,0.0016574658,0.0018792872],"genre_scores_gemma":[0.3599904,0.001624163,0.63502085,0.00008890132,0.00012248402,0.00007887073,0.00041152936,0.00017337545,0.0024894108],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998259,0.000028964774,0.000007821805,0.000041775755,0.00007657957,0.000018947365],"domain_scores_gemma":[0.99984384,0.000052732645,0.000019202287,0.000017453678,0.0000542355,0.000012568117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021635479,0.00023499865,0.00034404884,0.0013486588,0.00015592124,0.00044711423,0.00042777843,0.0002723651,0.0013808609],"category_scores_gemma":[0.0006329646,0.00016024726,0.00030420846,0.0012075055,0.00024439587,0.000666614,0.00023506343,0.00027486004,0.0005470265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021725358,0.00006881583,0.0009246403,0.00015995414,0.000047932546,0.00009773801,0.00007309872,0.027687665,0.13315776,0.007289507,0.0025144734,0.8277613],"study_design_scores_gemma":[0.00003284813,0.00015590368,0.0067091878,0.00003117669,0.000067151115,0.0004187318,0.00007337973,0.8806779,0.09268165,0.006873317,0.012222413,0.00005641585],"about_ca_topic_score_codex":0.002473522,"about_ca_topic_score_gemma":0.001936503,"teacher_disagreement_score":0.002473522,"about_ca_system_score_codex":0.00040211552,"about_ca_system_score_gemma":0.00027108093,"threshold_uncertainty_score":0.0049182773},"labels":[],"label_agreement":null},{"id":"W1984837130","doi":"10.1007/s11042-012-1247-1","title":"Bus surveillance: how many and where cameras should be placed","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"Transport Canada","keywords":"Computer science; Doors; Intuition; Cover (algebra); Key (lock); Artificial intelligence; Computer vision; Real-time computing; Computer security","score_opus":0.05862694165929638,"score_gpt":0.31043669660903106,"score_spread":0.2518097549497347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1984837130","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15544035,0.021947473,0.73918825,0.023147225,0.0013033728,0.00048526426,0.0035271135,0.0051788036,0.049782164],"genre_scores_gemma":[0.6939304,0.006142607,0.2838659,0.0010609764,0.0006185558,0.0001471319,0.0015873705,0.0005214871,0.012125678],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99893755,0.00037812383,0.000057034995,0.0003122059,0.00019337943,0.00012167824],"domain_scores_gemma":[0.99822336,0.00041158393,0.0002533454,0.00016160324,0.0006870977,0.0002629556],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013119383,0.0010891161,0.00074290845,0.0011115103,0.0012277617,0.0029113505,0.0014159045,0.0017769269,0.0052047344],"category_scores_gemma":[0.0055531366,0.0006424341,0.00034990747,0.0009171042,0.0008731555,0.0060887313,0.00079983583,0.0012466505,0.0019858973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011091346,0.0003513983,0.049325816,0.0010511365,0.00021439922,0.00028395426,0.000861383,0.02458478,0.021938508,0.021876078,0.08796556,0.7904379],"study_design_scores_gemma":[0.0003071356,0.0008604824,0.06795501,0.0016461631,0.00061648956,0.0021331394,0.019086413,0.5187449,0.0606117,0.14906773,0.17846619,0.00050457445],"about_ca_topic_score_codex":0.01637165,"about_ca_topic_score_gemma":0.03838629,"teacher_disagreement_score":0.01637165,"about_ca_system_score_codex":0.0013953316,"about_ca_system_score_gemma":0.0016709651,"threshold_uncertainty_score":0.03255266},"labels":[],"label_agreement":null},{"id":"W1985145931","doi":"10.5244/c.28.92","title":"Multiple Object Tracking Using Local Motion Patterns","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Object (grammar); Markov chain; Motion (physics); Video tracking; Tracking (education); Set (abstract data type); Data association; Association (psychology); Pattern recognition (psychology); Algorithm; Machine learning","score_opus":0.04469162574395924,"score_gpt":0.2971600427281662,"score_spread":0.25246841698420697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1985145931","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022339548,0.000060730537,0.99699354,0.000022423868,0.000012543575,0.00002251511,0.00003079641,0.0003590626,0.00026447323],"genre_scores_gemma":[0.077782266,0.00017298221,0.9195784,0.00006304066,0.000034524128,0.00015811643,0.0003608778,0.00013068895,0.0017190923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989183,0.0001134843,0.000055995548,0.0004020978,0.0004395789,0.00007067667],"domain_scores_gemma":[0.9987356,0.0005051313,0.00017494833,0.00028335294,0.00024060353,0.000060437877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013449199,0.00076059916,0.0012084352,0.0021366125,0.0007814234,0.0012039269,0.0019743089,0.001198633,0.0014125757],"category_scores_gemma":[0.004168981,0.0007682102,0.0010995284,0.0022734131,0.000576401,0.0019054317,0.0019298834,0.0015252024,0.0010754357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019491295,0.00013948703,0.0034216596,0.0001307885,0.0001385587,0.00015771712,0.0002294808,0.19405827,0.019235794,0.0152970385,0.0023960362,0.7646002],"study_design_scores_gemma":[0.0000198761,0.00004688233,0.0010646377,0.00002120266,0.000026543972,0.00014360722,0.000018553243,0.97521836,0.0085236905,0.010112402,0.004781806,0.000022378117],"about_ca_topic_score_codex":0.003794753,"about_ca_topic_score_gemma":0.004627466,"teacher_disagreement_score":0.003794753,"about_ca_system_score_codex":0.00092122296,"about_ca_system_score_gemma":0.001065188,"threshold_uncertainty_score":0.007545352},"labels":[],"label_agreement":null},{"id":"W1986401722","doi":"10.1016/j.procs.2013.09.114","title":"Tracking Method in Consideration of Existence of Similar Object around Target Object","year":2013,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Object (grammar); Computer vision; Tracking (education); Artificial intelligence; Video tracking","score_opus":0.03204396204073693,"score_gpt":0.3107415780829116,"score_spread":0.27869761604217463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986401722","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":[0.0048562046,0.00028624246,0.99307185,0.000042548694,0.000060191494,0.000048907812,0.000016640759,0.0004098023,0.0012075531],"genre_scores_gemma":[0.24186833,0.00095644034,0.7490921,0.00019022494,0.00012744805,0.00022276865,0.00018975201,0.00013162993,0.0072213844],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915504,0.00009363893,0.000042626292,0.00028432885,0.0003786165,0.000045760746],"domain_scores_gemma":[0.9993505,0.0001798129,0.00007298048,0.00007946116,0.00028274488,0.00003445543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009242385,0.0005069572,0.0008022006,0.0014518132,0.0006649864,0.0008924208,0.0010212832,0.0010833576,0.0014113298],"category_scores_gemma":[0.0018623424,0.00031731935,0.0006867484,0.0010721275,0.0004414995,0.0012711326,0.00082789105,0.00069527153,0.0006413381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018612182,0.00013412336,0.0053142495,0.00043520602,0.000220913,0.0004353312,0.00041491922,0.046023607,0.06434036,0.021129845,0.0043017804,0.85706365],"study_design_scores_gemma":[0.00007261565,0.00022786931,0.0051545785,0.000065240034,0.00020361679,0.0021794306,0.00010765345,0.92297703,0.04011163,0.009634656,0.019148922,0.000116789866],"about_ca_topic_score_codex":0.0030251315,"about_ca_topic_score_gemma":0.0022894691,"teacher_disagreement_score":0.0030251315,"about_ca_system_score_codex":0.0005032473,"about_ca_system_score_gemma":0.0010801891,"threshold_uncertainty_score":0.0060150623},"labels":[],"label_agreement":null},{"id":"W1986429310","doi":"10.1007/s11265-010-0540-3","title":"Automatic Detection of Object of Interest and Tracking in Active Video","year":2010,"lang":"en","type":"article","venue":"Journal of Signal Processing Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Initialization; Video tracking; AdaBoost; Pattern recognition (psychology); Classifier (UML); Outlier; Salient; Object detection; Tracking (education); Object (grammar)","score_opus":0.03947942196863002,"score_gpt":0.31007701230803086,"score_spread":0.27059759033940084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986429310","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":[0.14876533,0.0007213942,0.8479865,0.000092417475,0.00013235769,0.000054636977,0.00010134724,0.0005925273,0.0015534272],"genre_scores_gemma":[0.6905215,0.00057331217,0.30466005,0.00011244005,0.00012402197,0.00006543626,0.00033932444,0.000107237945,0.003496743],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995455,0.000061234095,0.000020943338,0.00013920497,0.00016658683,0.00006641177],"domain_scores_gemma":[0.9986985,0.00059466244,0.00012594377,0.0001418757,0.000349459,0.00008955433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090590125,0.00048572707,0.00069986156,0.0018042104,0.00036114047,0.0010402913,0.001030165,0.0011126476,0.00064952706],"category_scores_gemma":[0.00247981,0.0004308747,0.00040023678,0.000823815,0.00039751898,0.0009681051,0.0006880981,0.00067075697,0.00045551342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094526797,0.00031702046,0.0052404744,0.00015957415,0.0000766429,0.00026477253,0.00021315008,0.010859511,0.31627536,0.0037430753,0.0015075413,0.6603977],"study_design_scores_gemma":[0.00005109947,0.00031661143,0.016650911,0.00002933769,0.0001206434,0.0010161689,0.000099088094,0.80281514,0.17248368,0.0032838327,0.003095243,0.000038206203],"about_ca_topic_score_codex":0.0011900064,"about_ca_topic_score_gemma":0.0017680578,"teacher_disagreement_score":0.0018042104,"about_ca_system_score_codex":0.0002449331,"about_ca_system_score_gemma":0.0003467625,"threshold_uncertainty_score":0.004790902},"labels":[],"label_agreement":null},{"id":"W1986844135","doi":"10.1109/tip.2012.2221726","title":"Catching a Rat by Its Edglets","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Tracking (education); Frame (networking); Sliding window protocol; Window (computing); Robustness (evolution); Image processing; Boundary (topology); Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.02500305423925454,"score_gpt":0.3041002851527391,"score_spread":0.2790972309134846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1986844135","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":[0.59051365,0.0021463116,0.3897546,0.0006050683,0.00050212897,0.00018630724,0.0002599454,0.0028248,0.01320716],"genre_scores_gemma":[0.80027264,0.0011329493,0.18562987,0.0006390282,0.00011541962,0.00008347452,0.00036846183,0.00016965406,0.011588539],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997676,0.000019072999,0.0000106313655,0.00010636665,0.000063795436,0.000032615695],"domain_scores_gemma":[0.9996214,0.00006463056,0.00011118299,0.00007536101,0.000072344155,0.000055096505],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029852087,0.00055653456,0.00034211125,0.0006192346,0.00029405663,0.00042376152,0.0005717967,0.0004991519,0.0014063644],"category_scores_gemma":[0.0009101549,0.00024891194,0.0003891373,0.00025626284,0.00051360705,0.0006829762,0.0010401094,0.00051218004,0.0007593836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000342948,0.00007870522,0.02787668,0.00021593002,0.00011814726,0.0015733066,0.0003970375,0.0051049404,0.5108545,0.002380343,0.0025031974,0.44855422],"study_design_scores_gemma":[0.00015878816,0.004150701,0.22726442,0.00046608213,0.0006881713,0.015945254,0.0014379481,0.17039753,0.4920089,0.0075691203,0.079512045,0.00040113393],"about_ca_topic_score_codex":0.0014340373,"about_ca_topic_score_gemma":0.0028406775,"teacher_disagreement_score":0.0014340373,"about_ca_system_score_codex":0.00017437716,"about_ca_system_score_gemma":0.00026132533,"threshold_uncertainty_score":0.0047047734},"labels":[],"label_agreement":null},{"id":"W1987107691","doi":"10.1016/j.inffus.2014.11.002","title":"Intelligent video surveillance in crowded scenes","year":2014,"lang":"en","type":"article","venue":"Information Fusion","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Computer graphics (images)","score_opus":0.01427287372608142,"score_gpt":0.26233032412529145,"score_spread":0.24805745039921004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987107691","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07462717,0.0012416067,0.9216228,0.0001449193,0.00008727046,0.000034430504,0.00006330771,0.00037513318,0.0018033733],"genre_scores_gemma":[0.7667494,0.000977277,0.22960494,0.000093514835,0.00017211054,0.000040098825,0.00024501787,0.000057225676,0.002060321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993568,0.00015792262,0.000028542241,0.00017127699,0.00020132284,0.000084040534],"domain_scores_gemma":[0.999335,0.0002644187,0.00010739338,0.00007052797,0.00017464432,0.00004805536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011151747,0.0007802827,0.0010396879,0.0014276719,0.0005338582,0.0011310154,0.0006210627,0.0008781905,0.00033487537],"category_scores_gemma":[0.0019581292,0.0004389284,0.0005166833,0.0010259692,0.0005545706,0.001295992,0.0009328497,0.00061466935,0.00017356368],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00080813403,0.00024586866,0.005327104,0.00027170937,0.00026971757,0.00038780298,0.0004915172,0.3455367,0.089942,0.009454583,0.003683262,0.5435816],"study_design_scores_gemma":[0.000007797717,0.000055057182,0.0018380701,0.000010699606,0.000032125234,0.00008301242,0.00007089055,0.9833445,0.0106139835,0.0030373703,0.00089162454,0.000014833866],"about_ca_topic_score_codex":0.0040425924,"about_ca_topic_score_gemma":0.003930104,"teacher_disagreement_score":0.0040425924,"about_ca_system_score_codex":0.00051868946,"about_ca_system_score_gemma":0.00043886562,"threshold_uncertainty_score":0.008038104},"labels":[],"label_agreement":null},{"id":"W1987355247","doi":"10.1109/iembs.2011.6090506","title":"Towards a single sensor passive solution for automated fall detection","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University Health Network; University of Toronto; Toronto Rehabilitation Institute","funders":"Canadian Institutes of Health Research","keywords":"Computer science; Artificial intelligence; Field (mathematics); Falling (accident); Population; Computer vision; Real-time computing; Machine learning; Human–computer interaction","score_opus":0.07249349244288815,"score_gpt":0.29415606864106575,"score_spread":0.22166257619817759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1987355247","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024930984,0.00038020554,0.9695202,0.00020136278,0.00008172169,0.0001164111,0.00007325317,0.0031907442,0.0015050184],"genre_scores_gemma":[0.20927754,0.00029795145,0.78557694,0.00027377869,0.000075451695,0.00017257169,0.00023993046,0.00011390444,0.0039719776],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992223,0.000115178016,0.000027333994,0.00025643196,0.00033702046,0.00004189288],"domain_scores_gemma":[0.99887866,0.00024136936,0.000117312164,0.00018819084,0.00051576906,0.000058752405],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007986713,0.0008213816,0.0008502436,0.0008112569,0.00039909303,0.001021971,0.0017974868,0.0013098532,0.0017398803],"category_scores_gemma":[0.0016793517,0.00043551993,0.0003763721,0.0005534168,0.00040442,0.0014117702,0.00068611326,0.0008137379,0.0018009662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004818399,0.0007269739,0.0042908546,0.000275306,0.00009118031,0.00012277611,0.00020203034,0.010232465,0.19042574,0.0033345725,0.0059546744,0.78386164],"study_design_scores_gemma":[0.00014936802,0.0014364986,0.008156408,0.000089980975,0.00015360648,0.0009721301,0.00018668278,0.84843975,0.11612028,0.006569728,0.017640874,0.00008467122],"about_ca_topic_score_codex":0.001284091,"about_ca_topic_score_gemma":0.0017811015,"teacher_disagreement_score":0.0017974868,"about_ca_system_score_codex":0.0003773646,"about_ca_system_score_gemma":0.00059324235,"threshold_uncertainty_score":0.005820513},"labels":[],"label_agreement":null},{"id":"W1988630042","doi":"10.1016/j.imavis.2012.03.003","title":"Shape based appearance model for kernel tracking","year":2012,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Kernel (algebra); Silhouette; Heat kernel signature; Artificial intelligence; Computer vision; Computer science; Radial basis function kernel; Tracking (education); Active appearance model; Computation; Kernel method; Pattern recognition (psychology); Mathematics; Algorithm; Active shape model; Support vector machine; Image (mathematics)","score_opus":0.04821628321845593,"score_gpt":0.37087355913652087,"score_spread":0.3226572759180649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988630042","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":[0.0049955533,0.00012860278,0.9940252,0.00004394361,0.00002790236,0.000010114919,0.000031066418,0.00043001238,0.00030772216],"genre_scores_gemma":[0.57976866,0.0008710336,0.40733415,0.00016383523,0.000067840854,0.00011988682,0.000681842,0.00047451557,0.010518241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999532,0.000086102096,0.000025239173,0.00013627196,0.00016469312,0.000055707922],"domain_scores_gemma":[0.99919814,0.00016718343,0.000086807435,0.00022718562,0.00027432348,0.000046299283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058777235,0.0005607623,0.0010831748,0.00079134223,0.00032445998,0.0010192047,0.0016250715,0.0013332893,0.001851076],"category_scores_gemma":[0.0026342436,0.0005100853,0.0011879026,0.0011233231,0.00051536,0.0017275105,0.0009511332,0.0017370187,0.0022119873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038988786,0.00017433554,0.001859619,0.00016455229,0.00013737429,0.0001716987,0.00012936912,0.42044607,0.038571037,0.026409479,0.004650467,0.5068962],"study_design_scores_gemma":[0.0000032223252,0.000013864052,0.00022717321,0.000003456048,0.000009174352,0.00003902401,0.0000044022713,0.99536395,0.0017702469,0.0019395774,0.00061914563,0.0000067386654],"about_ca_topic_score_codex":0.0056241048,"about_ca_topic_score_gemma":0.0036163277,"teacher_disagreement_score":0.0056241048,"about_ca_system_score_codex":0.0007408218,"about_ca_system_score_gemma":0.0007335638,"threshold_uncertainty_score":0.011182785},"labels":[],"label_agreement":null},{"id":"W1988661984","doi":"10.5539/cis.v2n1p126","title":"Tracking High Speed Skater by Using Multiple Model","year":2009,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Tracking (education)","score_opus":0.03309069761525933,"score_gpt":0.29116540317035955,"score_spread":0.2580747055551002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1988661984","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07862379,0.00038011232,0.91256475,0.00016752646,0.00023133738,0.00006414708,0.000309699,0.0023696832,0.0052889483],"genre_scores_gemma":[0.76230407,0.00036421284,0.22385736,0.00016446352,0.00009521151,0.00012822682,0.0009895999,0.0002724986,0.011824384],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999688,0.000040095216,0.000010132265,0.00012836025,0.00008614166,0.00004729794],"domain_scores_gemma":[0.9997392,0.00005464051,0.00003106791,0.000060760423,0.00008392058,0.000030392077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044811735,0.00086714653,0.0013938786,0.001496461,0.0006194761,0.0011351277,0.0010614318,0.0013529068,0.002511026],"category_scores_gemma":[0.0009085048,0.0008162627,0.0011717895,0.0012425677,0.00033010237,0.0011685131,0.0009422469,0.0008830324,0.001850071],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008225551,0.00026570007,0.007158973,0.00012221702,0.0004738864,0.0002105532,0.00017038992,0.47385913,0.036116417,0.0025533384,0.004879298,0.47336748],"study_design_scores_gemma":[0.000006911128,0.000023419721,0.00090956636,0.000004103609,0.00001856395,0.000035731216,0.000011554419,0.9961622,0.0017794032,0.0004894813,0.00055020116,0.000008790792],"about_ca_topic_score_codex":0.0097870175,"about_ca_topic_score_gemma":0.011177299,"teacher_disagreement_score":0.0097870175,"about_ca_system_score_codex":0.00059060345,"about_ca_system_score_gemma":0.0005389752,"threshold_uncertainty_score":0.019460142},"labels":[],"label_agreement":null},{"id":"W1989925366","doi":"10.1109/syscon.2012.6189534","title":"Intelligent people surveillance with a cooperative camera network","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Homography; Computer science; Computer vision; Artificial intelligence; Object (grammar); Field (mathematics); Ground plane; Object detection; Calibration; Operator (biology); Field of view; Real-time computing; Telecommunications; Pattern recognition (psychology)","score_opus":0.022252268976743563,"score_gpt":0.2777253702340063,"score_spread":0.25547310125726275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1989925366","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2216298,0.0006206346,0.7709834,0.00020152648,0.00006369866,0.00008661406,0.000038253747,0.0010351475,0.0053408653],"genre_scores_gemma":[0.90252745,0.00021428491,0.09517187,0.000059411155,0.00004975789,0.00007174347,0.00006261146,0.000018988456,0.0018239389],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925715,0.00021916699,0.0000172424,0.00019181131,0.00023002413,0.00008455646],"domain_scores_gemma":[0.9994935,0.00012004046,0.00008009897,0.0000863603,0.00012977092,0.00009028189],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057219056,0.00046979956,0.0004243849,0.00059979275,0.00046820735,0.0005706164,0.00070377457,0.00061849883,0.0005591186],"category_scores_gemma":[0.0007538602,0.0002694758,0.00031459928,0.00039717014,0.0003468736,0.0013858561,0.0011885733,0.00051271415,0.00021060437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018969262,0.0006370721,0.014521246,0.00021894005,0.00023576568,0.0014967432,0.0016493894,0.18815982,0.17776497,0.015672589,0.0060043666,0.5917421],"study_design_scores_gemma":[0.00012244735,0.00063414994,0.0035345494,0.000023615843,0.00008161654,0.0004971658,0.0002371543,0.9623387,0.020782784,0.0035716463,0.008131732,0.000044565415],"about_ca_topic_score_codex":0.0036168518,"about_ca_topic_score_gemma":0.002589586,"teacher_disagreement_score":0.0036168518,"about_ca_system_score_codex":0.00033548506,"about_ca_system_score_gemma":0.00033200264,"threshold_uncertainty_score":0.0071915984},"labels":[],"label_agreement":null},{"id":"W1991127174","doi":"10.1109/tip.2013.2281423","title":"Multiresolution Based Gaussian Mixture Model for Background Suppression","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Mixture model; Robustness (evolution); Multiresolution analysis; Computer science; Artificial intelligence; Pattern recognition (psychology); Wavelet; Gaussian; Wavelet transform; Discrete wavelet transform","score_opus":0.039512363698150386,"score_gpt":0.3119189349818175,"score_spread":0.27240657128366713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1991127174","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":[0.0019110441,0.00050000066,0.9963637,0.000052576317,0.00004462401,0.000015881253,0.000039696835,0.00045160635,0.00062089803],"genre_scores_gemma":[0.20187318,0.0031951175,0.7872491,0.00022866823,0.00019340069,0.00014476603,0.00068308134,0.00038656828,0.0060460945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916244,0.00020656441,0.00003254371,0.00020721117,0.0003136934,0.000077542216],"domain_scores_gemma":[0.9996006,0.00013657837,0.00004633737,0.00007470215,0.00012147288,0.000020416315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011573485,0.0012152871,0.0013395515,0.0012936174,0.00040199325,0.000983305,0.0015535547,0.0011416047,0.0017645891],"category_scores_gemma":[0.0020494743,0.0005048814,0.0015946188,0.0016239114,0.0004634658,0.0014178251,0.0009454042,0.0016675172,0.0017622212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003042643,0.0001264517,0.0017470455,0.00039295052,0.0002416513,0.0003358261,0.00035279698,0.40725702,0.05278276,0.046822775,0.006509833,0.48312664],"study_design_scores_gemma":[0.0000055306136,0.000027002983,0.0004002081,0.000015043504,0.00003569922,0.00010458258,0.00001755806,0.9851403,0.005495695,0.0042744447,0.004460237,0.000023604407],"about_ca_topic_score_codex":0.00416816,"about_ca_topic_score_gemma":0.003831639,"teacher_disagreement_score":0.00416816,"about_ca_system_score_codex":0.00063939707,"about_ca_system_score_gemma":0.00073046715,"threshold_uncertainty_score":0.008287787},"labels":[],"label_agreement":null},{"id":"W1992148734","doi":"10.1117/12.2002185","title":"Joint histogram between color and local extrema patterns for object tracking","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Histogram; Computer vision; Local binary patterns; Maxima and minima; Pixel; Computer science; RGB color model; Pattern recognition (psychology); Video tracking; Color histogram; Feature (linguistics); Benchmark (surveying); Tracking (education); Joint (building); Object (grammar); Mathematics; Color image; Image (mathematics); Image processing; Geography; Engineering","score_opus":0.024733872425114642,"score_gpt":0.2538111936710884,"score_spread":0.22907732124597374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1992148734","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027958686,0.00048472956,0.9690304,0.000050837483,0.00007735182,0.00003530768,0.00011961921,0.0010694487,0.0011735637],"genre_scores_gemma":[0.52700573,0.00072477554,0.4676942,0.000080542435,0.000079674275,0.000099163975,0.00062870875,0.0001797821,0.003507375],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996228,0.00004078055,0.000019882022,0.00008759101,0.00019066155,0.000038214002],"domain_scores_gemma":[0.9996587,0.000077855046,0.000050208688,0.00005840451,0.00013162941,0.000023169268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040227466,0.0002811862,0.0004657331,0.0012906011,0.00019337444,0.00064686075,0.00057218596,0.0003389848,0.001076712],"category_scores_gemma":[0.0011863698,0.00019396035,0.0003949297,0.001355667,0.00023050848,0.00091764436,0.000491604,0.00043428637,0.00064230093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026091767,0.00010855444,0.0032838597,0.00013304566,0.00007448218,0.00009577593,0.000057848174,0.029824084,0.080552906,0.0038297647,0.0020657044,0.879713],"study_design_scores_gemma":[0.000025507741,0.00020312831,0.012639087,0.000022275635,0.00009628871,0.0004859455,0.00006124967,0.91367406,0.061819308,0.004165973,0.0067564147,0.000050683666],"about_ca_topic_score_codex":0.001552912,"about_ca_topic_score_gemma":0.0014651094,"teacher_disagreement_score":0.001552912,"about_ca_system_score_codex":0.00029099363,"about_ca_system_score_gemma":0.00042964055,"threshold_uncertainty_score":0.0036019087},"labels":[],"label_agreement":null},{"id":"W1994634851","doi":"10.1109/tip.2014.2378053","title":"SuBSENSE: A Universal Change Detection Method With Local Adaptive Sensitivity","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":651,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé; Fonds de recherche du Québec – Nature et technologies; Université Laval","keywords":"Computer science; Artificial intelligence; Pixel; Segmentation; Background subtraction; Change detection; Computer vision; Noise (video); Sensitivity (control systems); Image segmentation; Frame (networking); Adaptation (eye); Frame rate; Analytics; Fidelity; Object detection; Pattern recognition (psychology); Image (mathematics); Data mining","score_opus":0.027079027732611685,"score_gpt":0.27956061371006874,"score_spread":0.25248158597745707,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1994634851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026701683,0.0006718761,0.95938736,0.00011422553,0.00015903171,0.0001468762,0.00024549983,0.011178133,0.0013952227],"genre_scores_gemma":[0.24331762,0.0003878147,0.74799716,0.0003956866,0.00015962093,0.00017730054,0.001511662,0.0015807402,0.004472383],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904126,0.00009428857,0.00004842708,0.0003921219,0.00033712268,0.000086725704],"domain_scores_gemma":[0.9992982,0.00016300814,0.00007023286,0.0001821523,0.00021565228,0.00007070828],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000956114,0.0011328587,0.0014578084,0.002512345,0.00047214952,0.0012092884,0.0018411279,0.0010370861,0.0022375975],"category_scores_gemma":[0.0019891805,0.0005516993,0.0011480012,0.0012014209,0.00086386746,0.0015571794,0.0017481851,0.0012329463,0.0013058791],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034903732,0.00016009777,0.002053015,0.00017699267,0.00019650489,0.00020182841,0.00016595445,0.038722683,0.086655825,0.002948168,0.0071014264,0.8612685],"study_design_scores_gemma":[0.000034696008,0.00015902094,0.0028235056,0.000019746727,0.000074908574,0.0004561002,0.00004904189,0.9330405,0.052300215,0.0022889422,0.008693116,0.00006017202],"about_ca_topic_score_codex":0.0038705766,"about_ca_topic_score_gemma":0.0061500184,"teacher_disagreement_score":0.0038705766,"about_ca_system_score_codex":0.0006860874,"about_ca_system_score_gemma":0.0007798423,"threshold_uncertainty_score":0.007696092},"labels":[],"label_agreement":null},{"id":"W1995043765","doi":"10.1109/iccvw.2009.5457459","title":"People detection and tracking using the Explorative Particle Filtering","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Particle filter; Tracking (education); Computer science; Computer vision; Artificial intelligence; Task (project management); Tracking system; Function (biology); Video tracking; Pattern recognition (psychology); Filter (signal processing); Video processing; Engineering","score_opus":0.0688763402009989,"score_gpt":0.31730302690817824,"score_spread":0.24842668670717932,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1995043765","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014066804,0.00012651668,0.9850529,0.00005637289,0.00001695948,0.0000190163,0.000017479684,0.00022674767,0.00041726683],"genre_scores_gemma":[0.38279155,0.00038714692,0.61449224,0.000084120016,0.00006127938,0.00011810637,0.00007744091,0.000058957045,0.0019291575],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994134,0.00014067985,0.00002827512,0.00016256707,0.00020427672,0.000050783026],"domain_scores_gemma":[0.99911386,0.00046550762,0.00013458655,0.00012362789,0.00012594751,0.000036467147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013436351,0.00072484615,0.00091919105,0.0015481544,0.00041372626,0.0007626052,0.00079673185,0.0009273483,0.0004928748],"category_scores_gemma":[0.0024817737,0.0005744661,0.00092418387,0.0008917184,0.0006014778,0.0011799987,0.0010796722,0.00062704674,0.00019655749],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042085254,0.00016664602,0.0076197176,0.00018556943,0.00033153465,0.0005690242,0.00045059907,0.3806613,0.049774338,0.013050332,0.0018795197,0.5448905],"study_design_scores_gemma":[0.000020147088,0.00006684807,0.0025511123,0.000013878155,0.000029418748,0.00025370572,0.00002207007,0.9842839,0.007190952,0.0041872533,0.0013454923,0.000035131146],"about_ca_topic_score_codex":0.0027050546,"about_ca_topic_score_gemma":0.0020106474,"teacher_disagreement_score":0.0027050546,"about_ca_system_score_codex":0.00044755405,"about_ca_system_score_gemma":0.0004576251,"threshold_uncertainty_score":0.0071059465},"labels":[],"label_agreement":null},{"id":"W1996204076","doi":"10.1007/s00138-011-0381-5","title":"A computer vision framework for the analysis and interpretation of the cephalo-ocular behavior of drivers","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University; Université Laval","funders":"","keywords":"Computer science; Overtaking; Software; Identification (biology); Intersection (aeronautics); Simulation; Computer vision; Robustness (evolution); Artificial intelligence; Human–computer interaction; Engineering","score_opus":0.01561043928368079,"score_gpt":0.3184597311985256,"score_spread":0.30284929191484483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1996204076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0017260285,0.00021245937,0.99692696,0.00006555825,0.00002357112,0.000034164284,0.00009595519,0.0004411214,0.00047417465],"genre_scores_gemma":[0.10738518,0.00065169565,0.8883198,0.00010934253,0.000116817086,0.00019098504,0.00051735924,0.00012241694,0.0025864018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996038,0.00006945731,0.000026444633,0.00012241818,0.0001278443,0.00004999603],"domain_scores_gemma":[0.99960405,0.00010972409,0.000040312992,0.00005463281,0.00016070499,0.00003063387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007526704,0.0008647463,0.0009052093,0.0018266453,0.00074310886,0.0017677364,0.001378893,0.0012710721,0.0019247769],"category_scores_gemma":[0.0013221584,0.00039765393,0.0015315601,0.0011164667,0.00067773944,0.0008341272,0.001065566,0.0010410745,0.00082942756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017366948,0.00026691396,0.0026294154,0.00029699632,0.00020505246,0.00051611284,0.0002983513,0.21204203,0.062306143,0.08644743,0.010831073,0.6239868],"study_design_scores_gemma":[0.000012591576,0.000051408937,0.0013756754,0.000027733042,0.000041529005,0.0001929543,0.0000521461,0.96339214,0.00505247,0.023277665,0.006496084,0.00002756894],"about_ca_topic_score_codex":0.019157227,"about_ca_topic_score_gemma":0.0181386,"teacher_disagreement_score":0.019157227,"about_ca_system_score_codex":0.0007585204,"about_ca_system_score_gemma":0.001819853,"threshold_uncertainty_score":0.03809142},"labels":[],"label_agreement":null},{"id":"W1998070266","doi":"10.1007/s00138-014-0630-5","title":"A computationally efficient importance sampling tracking algorithm","year":2014,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Resampling; Algorithm; Tracking (education); Sampling (signal processing); Computer science; Condensation; Acceleration; Sequence (biology); Scheme (mathematics); Mathematics; Computer vision","score_opus":0.018080215179475754,"score_gpt":0.32432840734870466,"score_spread":0.3062481921692289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998070266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0014341894,0.00007889685,0.9976362,0.000033069,0.000046666843,0.00002096478,0.000011872573,0.00023425104,0.000503982],"genre_scores_gemma":[0.057928193,0.00018206489,0.9377129,0.00010044179,0.000120703604,0.0000948777,0.00017380292,0.00012374079,0.0035632078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905604,0.00016144445,0.00004088138,0.00018905847,0.00048585195,0.000066839086],"domain_scores_gemma":[0.9986945,0.0004942394,0.0000711796,0.00022230318,0.00043273123,0.000085023945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013010814,0.0007940181,0.0013549029,0.00107475,0.00062760274,0.001118387,0.0015645943,0.0012738124,0.0035407434],"category_scores_gemma":[0.004142123,0.0007961828,0.0006928596,0.0014088845,0.00048358052,0.001313448,0.0016725567,0.0017568894,0.0020551377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024174518,0.0001595376,0.00078499265,0.00010401715,0.00006545786,0.00008210545,0.00007239709,0.1052438,0.022318417,0.02247766,0.0069291345,0.8415208],"study_design_scores_gemma":[0.000020546908,0.000029567886,0.00020802722,0.0000043901155,0.000011563985,0.00007339824,0.0000039146116,0.99101114,0.0026519932,0.0037379987,0.0022390184,0.00000835894],"about_ca_topic_score_codex":0.004945115,"about_ca_topic_score_gemma":0.005454244,"teacher_disagreement_score":0.004945115,"about_ca_system_score_codex":0.000638819,"about_ca_system_score_gemma":0.0012993265,"threshold_uncertainty_score":0.011844993},"labels":[],"label_agreement":null},{"id":"W1998162566","doi":"10.1109/ccece.2006.277294","title":"Classifying Tracked Objects and their Interactions from Infrared Imagery","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Background subtraction; Artificial intelligence; Process (computing); Segmentation; Computer vision; Partition (number theory); Class (philosophy); Motion (physics); Object detection; Object (grammar); Pixel","score_opus":0.022654889145943203,"score_gpt":0.26460604997852044,"score_spread":0.24195116083257723,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998162566","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17135294,0.00038442411,0.82207936,0.00007649872,0.000041876527,0.00012365256,0.00017277288,0.0014378497,0.004330661],"genre_scores_gemma":[0.52144647,0.0005551851,0.4724863,0.0000652545,0.000050920484,0.00009706086,0.00073073804,0.00016725589,0.0044008563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963295,0.000032882846,0.000020452873,0.0001240225,0.00013939172,0.00005022875],"domain_scores_gemma":[0.9997439,0.00006430279,0.00006513427,0.000036175115,0.00007078935,0.000019609071],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037135519,0.00056577456,0.00045831443,0.0018621074,0.00043820636,0.00089062337,0.00059214135,0.00066924526,0.00084543234],"category_scores_gemma":[0.0009791091,0.0002750278,0.00042244786,0.0008767922,0.0003035861,0.0008078954,0.0003314274,0.00039724493,0.00073334045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035515663,0.00015327132,0.014303583,0.00010678197,0.000069704576,0.00034088633,0.0004740055,0.021121938,0.14729801,0.0022420736,0.0010127081,0.81252193],"study_design_scores_gemma":[0.000032020234,0.00034198037,0.10077203,0.000086452324,0.00024013709,0.0014241213,0.0008365174,0.68867207,0.18523657,0.007953526,0.014282767,0.00012183061],"about_ca_topic_score_codex":0.0028094195,"about_ca_topic_score_gemma":0.004373302,"teacher_disagreement_score":0.0028094195,"about_ca_system_score_codex":0.00039471406,"about_ca_system_score_gemma":0.00027183307,"threshold_uncertainty_score":0.0055860877},"labels":[],"label_agreement":null},{"id":"W1998908637","doi":"10.1109/ccece.2009.5090177","title":"A compactmodular active vision system formulti-target surveillance","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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":"Western University","funders":"","keywords":"Computer science; Omnidirectional antenna; Modular design; Active vision; Scalability; Software; Software architecture; Machine vision; Computer hardware; Computer vision; Artificial intelligence; Embedded system","score_opus":0.013395983263884916,"score_gpt":0.2899203828991333,"score_spread":0.2765243996352484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1998908637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027551427,0.0008223748,0.962461,0.00008346484,0.00013398341,0.00015047603,0.00011469709,0.002865708,0.0058167637],"genre_scores_gemma":[0.3412639,0.0008062675,0.64090127,0.00021307111,0.00017335864,0.000310162,0.0005381377,0.00013177579,0.01566201],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978787,0.000017922435,0.000008713299,0.000046636684,0.00011797039,0.000020893807],"domain_scores_gemma":[0.9997956,0.00003418276,0.000026560952,0.000034668683,0.000072525465,0.000036354544],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026263492,0.00040237396,0.00053898955,0.00039693565,0.00035271086,0.00055783277,0.0009631063,0.000452935,0.0040483447],"category_scores_gemma":[0.00031993326,0.00026936663,0.00019939349,0.00029506878,0.00019829042,0.00090364995,0.00077541725,0.00045783824,0.001453145],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023700073,0.00013658912,0.00078689656,0.00026527967,0.000028060886,0.00018936719,0.000114244045,0.006595986,0.42580023,0.006882118,0.0051582004,0.553806],"study_design_scores_gemma":[0.00024183745,0.0032415283,0.0074039144,0.00011435937,0.0001660716,0.004696015,0.00011338169,0.47431377,0.33355674,0.0046392777,0.17135091,0.0001621183],"about_ca_topic_score_codex":0.00046637436,"about_ca_topic_score_gemma":0.000715046,"teacher_disagreement_score":0.0040483447,"about_ca_system_score_codex":0.00027369166,"about_ca_system_score_gemma":0.00034479695,"threshold_uncertainty_score":0.013543069},"labels":[],"label_agreement":null},{"id":"W1999553202","doi":"10.4218/etrij.07.0207.0017","title":"Spatial Histograms for Region-Based Tracking","year":2007,"lang":"en","type":"article","venue":"ETRI Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalsa Corporation","funders":"","keywords":"Histogram; Artificial intelligence; Weighting; Similarity (geometry); Color histogram; Pattern recognition (psychology); Tracking (education); Mean-shift; Similarity measure; Measure (data warehouse); Histogram matching; Computer science; Intersection (aeronautics); Spatial analysis; Computer vision; Histogram equalization; Particle filter; Mathematics; Data mining; Geography; Image (mathematics); Image processing; Statistics; Color image; Cartography","score_opus":0.05921807663640975,"score_gpt":0.33111161406764283,"score_spread":0.27189353743123307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W1999553202","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":[0.0015102194,0.00032618764,0.9969427,0.00003876661,0.000034321056,0.000018575092,0.00010892312,0.00051905226,0.0005010843],"genre_scores_gemma":[0.17048725,0.0015003812,0.8235506,0.00019423042,0.0001629997,0.00019174417,0.0009787922,0.00034264926,0.0025914116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999461,0.00015978087,0.00003231859,0.00011891357,0.00019374333,0.000034241686],"domain_scores_gemma":[0.9984664,0.0006631274,0.00017763751,0.00033667206,0.00030080156,0.000055520726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008339702,0.00044487542,0.00053878664,0.0015022735,0.0002796785,0.0011434229,0.0008499747,0.0007670939,0.004182431],"category_scores_gemma":[0.0045630634,0.00036041116,0.00050501106,0.0022301641,0.00054606335,0.0021869806,0.0008069036,0.0008166428,0.0016452898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022100816,0.00006773308,0.0017855761,0.0002940132,0.000106558386,0.00018027892,0.00012816746,0.17774071,0.026156101,0.123816766,0.010121376,0.65938175],"study_design_scores_gemma":[0.000032642136,0.00010994743,0.0019360761,0.00006265542,0.00005439575,0.00036414224,0.00006131869,0.8658216,0.015391245,0.08238989,0.033718266,0.00005781106],"about_ca_topic_score_codex":0.002341158,"about_ca_topic_score_gemma":0.0020005372,"teacher_disagreement_score":0.004182431,"about_ca_system_score_codex":0.00067156676,"about_ca_system_score_gemma":0.00059041916,"threshold_uncertainty_score":0.013991594},"labels":[],"label_agreement":null},{"id":"W2000085115","doi":"10.1109/icip.2014.7025890","title":"Streaming spatio-temporal video segmentation using Gaussian Mixture Model","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Segmentation; Mixture model; Frame (networking); Artificial intelligence; Scalability; Gaussian; Computer vision; Similarity (geometry); Consistency (knowledge bases); Pattern recognition (psychology); Image (mathematics)","score_opus":0.033203464255561324,"score_gpt":0.30517718709109154,"score_spread":0.27197372283553023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000085115","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006732057,0.00021200988,0.9915615,0.00004261627,0.000026295918,0.000026035852,0.000059350896,0.0009967412,0.0003434547],"genre_scores_gemma":[0.2765408,0.0007803596,0.7180751,0.00009681147,0.00009245474,0.00014055069,0.00088289945,0.00030566318,0.0030853008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950254,0.00007763729,0.000027599444,0.00017957187,0.00016092713,0.00005158395],"domain_scores_gemma":[0.9996619,0.0000992787,0.0000440156,0.000049678492,0.000121507444,0.000023599476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005817522,0.00076025684,0.00085582683,0.0015079144,0.00035103914,0.00081761373,0.0011216005,0.00091175246,0.00085447094],"category_scores_gemma":[0.0012964236,0.0004203576,0.0011459991,0.0013066522,0.00041130726,0.000979437,0.0006260026,0.00081295124,0.00067633705],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043052805,0.000103527214,0.0020955186,0.0001850761,0.0001789382,0.00030168146,0.00024367243,0.34253547,0.07842002,0.008201385,0.0045178942,0.5627862],"study_design_scores_gemma":[0.000004062228,0.000019344132,0.00045959884,0.000005538615,0.000016173652,0.000062229956,0.000015334752,0.990457,0.006341566,0.0016297597,0.000976472,0.000013035688],"about_ca_topic_score_codex":0.010750948,"about_ca_topic_score_gemma":0.0074705333,"teacher_disagreement_score":0.010750948,"about_ca_system_score_codex":0.0007599769,"about_ca_system_score_gemma":0.00071592425,"threshold_uncertainty_score":0.021376729},"labels":[],"label_agreement":null},{"id":"W2000384087","doi":"10.1145/1459359.1459562","title":"Bi-layer video segmentation with foreground and background infrared illumination","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Computer science; Image segmentation; Foreground detection; Infrared; Pattern recognition (psychology); Object detection; Optics; Physics","score_opus":0.04761873593033814,"score_gpt":0.2899382419753267,"score_spread":0.24231950604498856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000384087","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024563795,0.0002975043,0.97273827,0.000055634016,0.0000246902,0.00003890746,0.000027984895,0.00082887925,0.0014243171],"genre_scores_gemma":[0.18285365,0.00041901373,0.8130429,0.00008939343,0.000037843765,0.000044105156,0.00026895312,0.00021523413,0.0030289236],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999355,0.000101263846,0.000033830616,0.00017470261,0.0002183308,0.00011681156],"domain_scores_gemma":[0.9994443,0.00019171939,0.00006890888,0.00012677039,0.00012904296,0.000039337232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075241405,0.0007844854,0.00071213813,0.00073238224,0.0003600247,0.0011102728,0.0011724199,0.0007858801,0.0014790988],"category_scores_gemma":[0.0013692003,0.00046355042,0.00072578,0.000637361,0.00043754355,0.0021577175,0.0009002438,0.00086385966,0.00080163655],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070694287,0.00020249191,0.0021710666,0.00031321324,0.0001004422,0.00016978184,0.0002929533,0.06873199,0.41822565,0.0076801213,0.000999206,0.5004061],"study_design_scores_gemma":[0.000022130474,0.00017048453,0.0017275426,0.000027912756,0.000075610216,0.00027121784,0.00007985162,0.7577077,0.23342961,0.0024332919,0.0040205508,0.000034182045],"about_ca_topic_score_codex":0.002368009,"about_ca_topic_score_gemma":0.003590681,"teacher_disagreement_score":0.002368009,"about_ca_system_score_codex":0.0006305946,"about_ca_system_score_gemma":0.00056353136,"threshold_uncertainty_score":0.00494802},"labels":[],"label_agreement":null},{"id":"W2000731666","doi":"10.1016/j.infrared.2006.06.015","title":"Outdoor infrared video surveillance: A novel dynamic technique for the subtraction of a changing background of IR images","year":2006,"lang":"en","type":"article","venue":"Infrared Physics & Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Background subtraction; Computer science; Infrared; Computer vision; Artificial intelligence; Tracking (education); Miniaturization; Subtraction; Remote sensing; Pixel; Optics","score_opus":0.01730625962544621,"score_gpt":0.28796376015115055,"score_spread":0.27065750052570436,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2000731666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059024636,0.0012328438,0.93021756,0.00008414159,0.00016195963,0.00007208703,0.00023393423,0.0015870328,0.007385774],"genre_scores_gemma":[0.32983014,0.0019933803,0.655869,0.00018185998,0.0002760142,0.00010621322,0.00081596064,0.0003169813,0.01061041],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975175,0.000025372254,0.000005943779,0.00006306088,0.00012845293,0.000025367126],"domain_scores_gemma":[0.9998485,0.00003391398,0.000028105975,0.000021915877,0.000045027253,0.000022492455],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026769916,0.0004900766,0.0004167882,0.0008645377,0.00022909154,0.00048917026,0.00066485966,0.0003565227,0.0014664438],"category_scores_gemma":[0.00038574776,0.00021892044,0.00031036895,0.0007343429,0.00020063423,0.00048318747,0.00042578942,0.00041005074,0.0006075638],"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":[0.0004275738,0.00014267862,0.00196137,0.00020374052,0.00005622799,0.0001449892,0.00008804899,0.0020577712,0.52000415,0.0018200008,0.0032791772,0.46981415],"study_design_scores_gemma":[0.00007814617,0.00079042045,0.028690577,0.00008087963,0.00022669803,0.004038213,0.00012383255,0.19875267,0.70954376,0.0014901935,0.056086477,0.00009814775],"about_ca_topic_score_codex":0.0009041026,"about_ca_topic_score_gemma":0.0020192377,"teacher_disagreement_score":0.0014664438,"about_ca_system_score_codex":0.0001853047,"about_ca_system_score_gemma":0.00025772394,"threshold_uncertainty_score":0.0049057603},"labels":[],"label_agreement":null},{"id":"W2001160538","doi":"10.1109/icdim.2009.5356792","title":"Adaptive foreground segmentation using fuzzy approach","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Pixel; Artificial intelligence; Histogram; Pattern recognition (psychology); Image segmentation; Cluster analysis; Computer science; Computer vision; Segmentation; Fuzzy logic; Frame (networking); Fuzzy set; Mathematics; Image (mathematics)","score_opus":0.07371928966222245,"score_gpt":0.32378755928925435,"score_spread":0.2500682696270319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2001160538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065337103,0.00025109935,0.99187887,0.000032107444,0.000023994011,0.000018569377,0.000013609598,0.00032779214,0.0009203904],"genre_scores_gemma":[0.19558233,0.00054094865,0.8010575,0.000082588915,0.00007423889,0.000054239066,0.00010546745,0.00012181337,0.0023809585],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967015,0.000034367982,0.000016917635,0.000115019066,0.00012472515,0.00003880067],"domain_scores_gemma":[0.99979335,0.00006758161,0.000023025392,0.000027434831,0.00007224828,0.0000164341],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038203833,0.0006110697,0.00076363893,0.0012956021,0.00056748604,0.000842817,0.0012158598,0.00096336415,0.0013587885],"category_scores_gemma":[0.0007036073,0.0004428481,0.0009220559,0.0007201703,0.00048137776,0.0011254745,0.0006046713,0.00056399434,0.00052361493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019566223,0.000077831064,0.0013319631,0.0003036014,0.00011430011,0.00037434805,0.00040888906,0.1400386,0.16737735,0.015041359,0.0019051292,0.672831],"study_design_scores_gemma":[0.000015144188,0.000048795817,0.00080776826,0.000029121089,0.000051172214,0.00027406306,0.00006268328,0.95177287,0.034235965,0.0070181997,0.0056435857,0.000040664963],"about_ca_topic_score_codex":0.0034382658,"about_ca_topic_score_gemma":0.003198455,"teacher_disagreement_score":0.0034382658,"about_ca_system_score_codex":0.000727531,"about_ca_system_score_gemma":0.0005153477,"threshold_uncertainty_score":0.006836474},"labels":[],"label_agreement":null},{"id":"W2002002766","doi":"10.1007/s00138-013-0568-z","title":"Background subtraction using finite mixtures of asymmetric Gaussian distributions and shadow detection","year":2013,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Background subtraction; Mixture model; Artificial intelligence; Computer science; Computer vision; Segmentation; Robustness (evolution); Gaussian; Shadow (psychology); Image segmentation; Gaussian network model; Pixel","score_opus":0.020416483717582482,"score_gpt":0.312397982708344,"score_spread":0.29198149899076153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2002002766","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068707396,0.00017123725,0.9921972,0.000026833772,0.000016558237,0.000009998697,0.000010284086,0.00043020165,0.0002669852],"genre_scores_gemma":[0.24077697,0.00072753284,0.75620985,0.000096757416,0.00006806085,0.000044606204,0.00021901279,0.00024014951,0.00161694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921584,0.00018201156,0.000032907046,0.00018046085,0.00032337097,0.00006545059],"domain_scores_gemma":[0.9992834,0.00033892895,0.000079069556,0.00010355791,0.00015888491,0.000036137833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011687,0.0010249077,0.0010602701,0.001741728,0.00043540742,0.0009387826,0.0016094582,0.0010038936,0.00071429036],"category_scores_gemma":[0.0024358495,0.0006587276,0.0013251733,0.0014819398,0.0007529577,0.0015942453,0.00091006304,0.0011084393,0.0005208754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004745152,0.00016442906,0.0020084125,0.00027729708,0.00022874458,0.00031368205,0.00029314353,0.25684828,0.109731965,0.01688251,0.0018438088,0.6109331],"study_design_scores_gemma":[0.000011644555,0.000027895767,0.00090517325,0.000009491482,0.00003231562,0.00021539976,0.000017734537,0.9675803,0.025580866,0.0040154415,0.0015769048,0.000026676396],"about_ca_topic_score_codex":0.0048943735,"about_ca_topic_score_gemma":0.0044353926,"teacher_disagreement_score":0.0048943735,"about_ca_system_score_codex":0.0007408614,"about_ca_system_score_gemma":0.0006672073,"threshold_uncertainty_score":0.00973177},"labels":[],"label_agreement":null},{"id":"W2003683977","doi":"10.1016/j.cviu.2011.10.006","title":"An iterative integrated framework for thermal–visible image registration, sensor fusion, and people tracking for video surveillance applications","year":2011,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":163,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; RANSAC; Tracking (education); Affine transformation; Video tracking; Image registration; Geometric transformation; Transformation (genetics); Matching (statistics); Image fusion; Pixel; Image sensor; Sensor fusion; Trajectory; Tracking system; Object (grammar); Image (mathematics); Kalman filter; Mathematics","score_opus":0.06477169842145275,"score_gpt":0.33410368690693826,"score_spread":0.2693319884854855,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2003683977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00066098175,0.000026719616,0.99899894,0.000008603842,0.00000638233,0.000008597289,0.0000052503024,0.00014286651,0.0001417231],"genre_scores_gemma":[0.053616136,0.00011238815,0.9439921,0.000033603836,0.000032617045,0.000115059505,0.00010256105,0.00013275527,0.0018628908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99899715,0.00019011057,0.000052142637,0.00020048146,0.00046455723,0.00009549493],"domain_scores_gemma":[0.99936,0.00017975135,0.00006360598,0.00008443526,0.00027057723,0.000041644336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013543932,0.0009077287,0.0011484447,0.000864278,0.00061657064,0.0011490734,0.0025831244,0.0012269205,0.0022922189],"category_scores_gemma":[0.0023073074,0.00075514935,0.0017382114,0.0010504933,0.0006147016,0.0013892045,0.0018904606,0.0014839591,0.0009598031],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022673442,0.0001805345,0.00081200514,0.00014237936,0.00022664774,0.0001388867,0.00024667432,0.44757408,0.03367294,0.028061833,0.0026723733,0.48604482],"study_design_scores_gemma":[0.000005181948,0.000024984947,0.0001110612,0.0000035137762,0.000014139536,0.000032710217,0.000007680702,0.9939195,0.0026682895,0.002301325,0.0009006298,0.000010942808],"about_ca_topic_score_codex":0.0110215675,"about_ca_topic_score_gemma":0.015860174,"teacher_disagreement_score":0.0110215675,"about_ca_system_score_codex":0.0007527816,"about_ca_system_score_gemma":0.0020543046,"threshold_uncertainty_score":0.02191478},"labels":[],"label_agreement":null},{"id":"W2004364127","doi":"10.1007/s11760-013-0470-1","title":"An efficient method of cast shadow removal using multiple features","year":2013,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pixel; Computer vision; Shadow (psychology); Artificial intelligence; Computer science; Object (grammar); Background subtraction; Feature (linguistics); Frame (networking); Similarity (geometry); Image (mathematics)","score_opus":0.0258690166895678,"score_gpt":0.33055754334847215,"score_spread":0.30468852665890434,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004364127","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005973703,0.00022844292,0.9922585,0.000037454916,0.000077003926,0.000028892588,0.00003730121,0.00058479625,0.00077394536],"genre_scores_gemma":[0.0635348,0.0003288568,0.93056047,0.000050684233,0.00009050439,0.00006187917,0.00020055067,0.0001584489,0.0050137048],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995413,0.000040299583,0.000016187361,0.000066838016,0.00029614408,0.00003933906],"domain_scores_gemma":[0.99961674,0.00007424143,0.000033321678,0.00008820785,0.00016268514,0.00002473309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027737144,0.0008366864,0.000946883,0.0011271278,0.00059070846,0.0006586343,0.0010101652,0.0007224996,0.0027826552],"category_scores_gemma":[0.000852828,0.0005413776,0.00089236046,0.0011447704,0.00028514632,0.0009336255,0.0009968338,0.0009997193,0.0015455392],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017846868,0.00008975971,0.00033994898,0.00012271237,0.000059380585,0.000092742186,0.000076703465,0.009448253,0.15773275,0.0033972424,0.003517197,0.8249449],"study_design_scores_gemma":[0.00006228503,0.00016458522,0.002686257,0.000029933488,0.00011341146,0.00095783465,0.000048237624,0.8123524,0.15928884,0.0034035756,0.020804027,0.00008851879],"about_ca_topic_score_codex":0.0023914396,"about_ca_topic_score_gemma":0.003935368,"teacher_disagreement_score":0.0027826552,"about_ca_system_score_codex":0.0002949065,"about_ca_system_score_gemma":0.0007720823,"threshold_uncertainty_score":0.009308875},"labels":[],"label_agreement":null},{"id":"W2004675356","doi":"10.1145/1099396.1099410","title":"A multi-criteria model for robust foreground extraction","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Initialization; Probabilistic logic; Computer vision; Foreground detection; Pattern recognition (psychology); Robustness (evolution); Pixel","score_opus":0.13003599350164752,"score_gpt":0.3797090309072611,"score_spread":0.24967303740561358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2004675356","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0013329126,0.00015752905,0.99753475,0.00006807358,0.000016011594,0.000026490326,0.000030066665,0.00013513629,0.0006990279],"genre_scores_gemma":[0.34180343,0.0006885575,0.6448737,0.00022088943,0.00015334898,0.00057340984,0.0004743054,0.00038144455,0.010830862],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99792564,0.00060286064,0.00013601562,0.00040150367,0.0007685395,0.00016556041],"domain_scores_gemma":[0.99805826,0.0009467578,0.00018211809,0.000118769894,0.0006131516,0.00008088566],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033019737,0.0015212297,0.0017920092,0.001361802,0.00067241816,0.0024906395,0.0029262379,0.0024575172,0.004103063],"category_scores_gemma":[0.005713113,0.00093030947,0.0015843282,0.0013503013,0.0009948768,0.0017203228,0.0015724577,0.0017369534,0.0019951302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006445375,0.000031524403,0.00025299075,0.000087004235,0.0000670736,0.00008779689,0.00005519392,0.9347948,0.0023036047,0.020738455,0.001196763,0.040320486],"study_design_scores_gemma":[0.0000038057296,0.0000101626765,0.000038130413,0.000004312774,0.000004417217,0.000010343586,0.00000190318,0.9961662,0.00020445127,0.0031397545,0.00041113255,0.000005366223],"about_ca_topic_score_codex":0.00988306,"about_ca_topic_score_gemma":0.0071778363,"teacher_disagreement_score":0.00988306,"about_ca_system_score_codex":0.0018288167,"about_ca_system_score_gemma":0.0013316534,"threshold_uncertainty_score":0.019651055},"labels":[],"label_agreement":null},{"id":"W2006031834","doi":"10.1117/12.850273","title":"Automatic scene activity modeling for improving object classification","year":2010,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Object (grammar); Scene statistics; Object detection; Redundancy (engineering); Object model; Pattern recognition (psychology); Population","score_opus":0.02313369874351651,"score_gpt":0.2690775662266322,"score_spread":0.2459438674831157,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2006031834","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021647846,0.000110113615,0.9759222,0.000038942635,0.000011221578,0.000041221865,0.00008609723,0.0016954463,0.00044681097],"genre_scores_gemma":[0.36632583,0.00035177247,0.6290298,0.00008075992,0.000038436083,0.00014702685,0.0016404433,0.00040619733,0.001979647],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99916875,0.00017704141,0.00004561288,0.0002308714,0.00028516943,0.00009262115],"domain_scores_gemma":[0.99913377,0.0002783426,0.00010411772,0.0001823418,0.0002691029,0.00003246184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011272518,0.0010454208,0.0010281223,0.0020025393,0.00045331684,0.0012025137,0.0013637202,0.0007346936,0.0012078201],"category_scores_gemma":[0.0025161218,0.0005119167,0.0010779004,0.0016161028,0.00036133316,0.0016654467,0.00072234124,0.00074939075,0.0014855235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021251685,0.00025649866,0.0051098834,0.00010132149,0.000092167735,0.00008069754,0.00019801495,0.20666373,0.030932475,0.0041979314,0.0020855512,0.75006926],"study_design_scores_gemma":[0.000004162124,0.00003296597,0.0013078587,0.0000046828513,0.000012741394,0.000048164136,0.000022827477,0.9882774,0.007399878,0.0017617786,0.0011174133,0.000010207123],"about_ca_topic_score_codex":0.0056836833,"about_ca_topic_score_gemma":0.006639336,"teacher_disagreement_score":0.0056836833,"about_ca_system_score_codex":0.0007630214,"about_ca_system_score_gemma":0.0008648756,"threshold_uncertainty_score":0.011301219},"labels":[],"label_agreement":null},{"id":"W2007766641","doi":"10.1007/s11042-011-0751-z","title":"Real-time vandalism detection by monitoring object activities","year":2011,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada; Concordia University","funders":"","keywords":"Computer science; Event (particle physics); Frame (networking); Segmentation; Computer vision; Phone; Object (grammar); Artificial intelligence; Graffiti; Real-time computing; Computer security; Telecommunications","score_opus":0.03723536504774554,"score_gpt":0.28006348341622306,"score_spread":0.2428281183684775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2007766641","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":[0.72921264,0.0010394232,0.2526902,0.00021173716,0.00022800511,0.00019972622,0.0006573408,0.0040001436,0.011760676],"genre_scores_gemma":[0.9586478,0.00025337967,0.036952604,0.000050050778,0.00006296157,0.000035401055,0.00043492476,0.00007547048,0.0034874459],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992914,0.00008645308,0.000027029007,0.00014779443,0.0003508871,0.00009631219],"domain_scores_gemma":[0.9985116,0.0003951425,0.00030237986,0.00017755156,0.00048224782,0.00013114049],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047512524,0.0006128095,0.00065331225,0.0024582478,0.00028877158,0.0008315968,0.00065518875,0.0008455951,0.00096962444],"category_scores_gemma":[0.0016610215,0.00026296038,0.000249879,0.0008489317,0.00026781583,0.0007913151,0.00048302376,0.00051478116,0.0008526678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009869466,0.0005348407,0.05603425,0.000330824,0.00018584395,0.00062557636,0.00043774882,0.011247732,0.28273585,0.0008694334,0.0064950003,0.63951594],"study_design_scores_gemma":[0.00004658831,0.0006834422,0.16646142,0.00004577439,0.00017861901,0.0022775629,0.00040809863,0.6689851,0.15283151,0.0013493932,0.0066390554,0.00009343133],"about_ca_topic_score_codex":0.0014582633,"about_ca_topic_score_gemma":0.0023154598,"teacher_disagreement_score":0.0024582478,"about_ca_system_score_codex":0.00023025522,"about_ca_system_score_gemma":0.00021476382,"threshold_uncertainty_score":0.0032436848},"labels":[],"label_agreement":null},{"id":"W2008013560","doi":"10.1007/s11042-012-1294-7","title":"Utility based decision support engine for camera view selection in multimedia surveillance systems","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Ottawa","funders":"","keywords":"Computer science; Operator (biology); Task (project management); Event (particle physics); Schedule; Relation (database); Plan (archaeology); Action (physics); Selection (genetic algorithm); Decision support system; Human–computer interaction; Artificial intelligence; Operations research; Data mining","score_opus":0.04361010130554662,"score_gpt":0.3217473181234587,"score_spread":0.2781372168179121,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008013560","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09534582,0.00042279862,0.89734554,0.0001676369,0.00004774199,0.00018536842,0.00034220098,0.0045987065,0.001544158],"genre_scores_gemma":[0.9052932,0.00012632654,0.09297627,0.000065838045,0.000031281194,0.00008820898,0.00027899034,0.00008521051,0.001054631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987716,0.00032035477,0.00012241476,0.00018050676,0.00044228608,0.00016277409],"domain_scores_gemma":[0.99691087,0.0018613645,0.00020157555,0.00020559285,0.000701972,0.000118626565],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002303197,0.0008267128,0.0012999449,0.0016498584,0.00043699393,0.0017932447,0.001264175,0.00069186056,0.003065427],"category_scores_gemma":[0.0070539964,0.00030508995,0.0005002126,0.0010025224,0.00026809532,0.0014718244,0.00072863454,0.00067386514,0.0006567004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045252186,0.0007930744,0.0095302705,0.00034976949,0.0002253942,0.00060863624,0.00025651086,0.27163076,0.020243077,0.011182579,0.006914316,0.6737405],"study_design_scores_gemma":[0.000028956594,0.00007046744,0.0005860242,0.000008581098,0.000032672167,0.000046754907,0.000018431701,0.99112684,0.0056057274,0.0020988744,0.00036478537,0.000011985424],"about_ca_topic_score_codex":0.004546312,"about_ca_topic_score_gemma":0.0037392357,"teacher_disagreement_score":0.004546312,"about_ca_system_score_codex":0.0007820201,"about_ca_system_score_gemma":0.0008793741,"threshold_uncertainty_score":0.012180626},"labels":[],"label_agreement":null},{"id":"W2008747262","doi":"10.1109/ccece.2008.4564892","title":"Towards an intelligent tele-surveillance system for public transport areas: Fuzzy logic based camera control","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Fuzzy logic; Fuzzy control system; Control (management); Intelligent control; Computer vision; Artificial intelligence; Real-time computing; Computer security","score_opus":0.03795392905076752,"score_gpt":0.23162115698782335,"score_spread":0.19366722793705582,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2008747262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036241308,0.00038519056,0.95593065,0.00020795323,0.00008839402,0.00016997689,0.000035816294,0.0018085305,0.005132185],"genre_scores_gemma":[0.7211328,0.0004598613,0.27079275,0.00037445582,0.000071774804,0.00015821955,0.000078316734,0.00004117897,0.0068906187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997665,0.000028724537,0.000012431185,0.00006113192,0.000097444645,0.00003370659],"domain_scores_gemma":[0.99982303,0.00002984736,0.000024821758,0.000009374314,0.00009480407,0.000018277298],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003678637,0.00038767472,0.00030635318,0.00035638374,0.0003280509,0.0009324578,0.0008352037,0.0007956435,0.0015981802],"category_scores_gemma":[0.00042001813,0.00015826091,0.0002596675,0.0001871539,0.0003448601,0.0005187755,0.0002537552,0.00064364634,0.00039211128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008311729,0.0005804993,0.0026614226,0.00034088077,0.00010329735,0.00065538083,0.0006295062,0.07260914,0.44475222,0.0113250725,0.00464767,0.46086377],"study_design_scores_gemma":[0.00012039186,0.00053662405,0.002136762,0.000055561097,0.000106923726,0.00029508205,0.00009744898,0.9011528,0.08545087,0.0019870691,0.008016347,0.000044137683],"about_ca_topic_score_codex":0.005691528,"about_ca_topic_score_gemma":0.0044840765,"teacher_disagreement_score":0.005691528,"about_ca_system_score_codex":0.00048402825,"about_ca_system_score_gemma":0.00046747664,"threshold_uncertainty_score":0.011316776},"labels":[],"label_agreement":null},{"id":"W2010195704","doi":"10.1145/2656346.2656365","title":"Keypoint-based binocular distance measurement for pedestrian detection system","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Computer vision; Pedestrian; Pedestrian detection; Feature extraction; Parallax; Distance measurement; Support vector machine; Context (archaeology); Measure (data warehouse); Data mining; Engineering; Geography","score_opus":0.03253971601258227,"score_gpt":0.24980661491700426,"score_spread":0.217266898904422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010195704","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15163705,0.0010786811,0.82485247,0.00017005703,0.0003411482,0.00028183917,0.0007946243,0.014531296,0.0063129254],"genre_scores_gemma":[0.84360015,0.00035394583,0.15164056,0.00015654914,0.000079535384,0.00014997432,0.00071798306,0.00007851092,0.0032227829],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954695,0.00004604113,0.000021126016,0.00012014367,0.00019986636,0.000065893655],"domain_scores_gemma":[0.99955195,0.000034788558,0.000042231113,0.000052810392,0.0002711467,0.000047040015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036122967,0.00054218236,0.0008477762,0.0011931288,0.00030643644,0.00039291728,0.0008502837,0.00055326516,0.0034735925],"category_scores_gemma":[0.000807299,0.0002659984,0.0002939325,0.00057967665,0.00011970788,0.0006567777,0.00079831976,0.0003432095,0.0017952338],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082879193,0.00022171713,0.0073753665,0.00047094,0.000078359786,0.00039605817,0.00019959574,0.0039130845,0.35053778,0.0013937572,0.010740535,0.62384397],"study_design_scores_gemma":[0.00018385182,0.0017539416,0.028632268,0.00006770145,0.00021595907,0.002707179,0.00018761292,0.5223921,0.41734,0.0018006908,0.024509666,0.0002090679],"about_ca_topic_score_codex":0.0018915739,"about_ca_topic_score_gemma":0.0021782606,"teacher_disagreement_score":0.0034735925,"about_ca_system_score_codex":0.00039989222,"about_ca_system_score_gemma":0.000480797,"threshold_uncertainty_score":0.011620283},"labels":[],"label_agreement":null},{"id":"W2010581833","doi":"10.1007/s11042-010-0649-1","title":"Effective multimedia surveillance using a human-centric approach","year":2010,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; University of Winnipeg","funders":"","keywords":"Computer science; Task (project management); Computer vision; Artificial intelligence; Smart camera; Operator (biology); Premise; Relevance (law); Real-time computing","score_opus":0.033558227452341195,"score_gpt":0.31550726779004756,"score_spread":0.28194904033770635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010581833","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021370044,0.0006560362,0.9740059,0.00012256585,0.00006116665,0.00005198661,0.00005113029,0.0002132244,0.0034679815],"genre_scores_gemma":[0.5868956,0.0016183631,0.40533057,0.00019221881,0.00025991237,0.00011242582,0.0001677941,0.00008326714,0.0053399187],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993352,0.00021730839,0.000018970837,0.00016386974,0.00020548912,0.000059073445],"domain_scores_gemma":[0.99962676,0.00012145254,0.000046389567,0.00007040472,0.00010355488,0.000031492888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067437784,0.0008107476,0.0006737794,0.001036224,0.00044572246,0.00091173896,0.0006539034,0.0006986537,0.00084619434],"category_scores_gemma":[0.0009779629,0.00030380933,0.00048662655,0.0007025091,0.0004645185,0.0011573998,0.000986176,0.0004532039,0.0002451941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052247365,0.0002571935,0.0022974883,0.00026188052,0.00019875774,0.00027182512,0.00036241606,0.13670231,0.15617651,0.03048996,0.004488928,0.6679703],"study_design_scores_gemma":[0.00002314878,0.0003000654,0.0030762146,0.00003438871,0.00011921875,0.00046109493,0.00016869605,0.9280988,0.04856942,0.01081126,0.00829815,0.000039546303],"about_ca_topic_score_codex":0.0010574905,"about_ca_topic_score_gemma":0.0018056494,"teacher_disagreement_score":0.0010574905,"about_ca_system_score_codex":0.00046863777,"about_ca_system_score_gemma":0.00061838416,"threshold_uncertainty_score":0.0035665035},"labels":[],"label_agreement":null},{"id":"W2010840665","doi":"10.1117/12.2053177","title":"Improved frame differencing based moving object detection using feet-step sound","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Computer science; Computer vision; Artificial intelligence; Background subtraction; Object detection; Frame (networking); Object (grammar); Noise (video); Video tracking; Frame rate; Microphone; Pixel; Pattern recognition (psychology); Image (mathematics); Sound pressure","score_opus":0.015936008471930017,"score_gpt":0.24660530067771722,"score_spread":0.2306692922057872,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2010840665","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06456165,0.0007839551,0.9267184,0.000078101526,0.00022287684,0.00012298912,0.00023384417,0.004044216,0.0032338875],"genre_scores_gemma":[0.16620535,0.00047311868,0.82648087,0.000089242676,0.00006378611,0.00008413762,0.00065432524,0.00016670677,0.0057825036],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990889,0.000079968035,0.000040555446,0.00016814462,0.0005615323,0.00006078051],"domain_scores_gemma":[0.9987142,0.00037265744,0.000067109075,0.00020378894,0.0005959106,0.00004635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087493064,0.00063251745,0.0007867796,0.0026284233,0.00027545658,0.00085321965,0.0012855687,0.0008408814,0.0031735906],"category_scores_gemma":[0.0019579988,0.0003611515,0.0005619536,0.0018380282,0.00029699106,0.0010050385,0.0006020531,0.00070340413,0.0016959017],"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":[0.0004853327,0.00021225336,0.0023493487,0.00027491222,0.000103881684,0.0002605213,0.00015846138,0.0062171477,0.33386078,0.0018650946,0.002398941,0.65181327],"study_design_scores_gemma":[0.000119979064,0.00057861017,0.015533715,0.000047718004,0.00018154806,0.001404903,0.00010498026,0.51288074,0.44844958,0.0014752295,0.019064227,0.00015877963],"about_ca_topic_score_codex":0.0025705916,"about_ca_topic_score_gemma":0.0038174812,"teacher_disagreement_score":0.0031735906,"about_ca_system_score_codex":0.00039285622,"about_ca_system_score_gemma":0.00048008413,"threshold_uncertainty_score":0.01061672},"labels":[],"label_agreement":null},{"id":"W2012600100","doi":"10.1109/icme.2014.6890125","title":"Boosted local binaries for object detection","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Local binary patterns; Computer science; Pattern recognition (psychology); Artificial intelligence; Binary number; Encoding (memory); Classifier (UML); Feature (linguistics); Domain (mathematical analysis); Object detection; Histogram; Mathematics; Image (mathematics)","score_opus":0.017325696261779853,"score_gpt":0.2723935195696463,"score_spread":0.2550678233078664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2012600100","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":[0.029088812,0.0012796075,0.9636164,0.00014572791,0.0001491469,0.0000951982,0.00038943288,0.0032371182,0.0019986043],"genre_scores_gemma":[0.5227413,0.00095672987,0.4640034,0.0004485933,0.00025799687,0.00026790306,0.0018256644,0.00039472332,0.009103597],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992453,0.00010826932,0.000031692452,0.00018385136,0.00033493558,0.00009602061],"domain_scores_gemma":[0.9992539,0.00018833426,0.00008787539,0.00016805471,0.00023608941,0.000065637985],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000731333,0.0008586473,0.0014647773,0.002417267,0.00041925628,0.0010508538,0.0017447146,0.0007617997,0.005011649],"category_scores_gemma":[0.0016867644,0.0004112786,0.0005824269,0.0025423903,0.0005019775,0.0017011872,0.00164358,0.0012893092,0.0034636687],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00070231233,0.00029523353,0.0019303801,0.00022150914,0.000091843416,0.000077208875,0.000053392607,0.030398449,0.056022897,0.0051908386,0.0068118214,0.8982041],"study_design_scores_gemma":[0.000029759503,0.00018493089,0.0026886447,0.000031240983,0.000047096368,0.00024168953,0.000035322497,0.9472917,0.037164465,0.00683647,0.005412089,0.000036628073],"about_ca_topic_score_codex":0.002435496,"about_ca_topic_score_gemma":0.0034122553,"teacher_disagreement_score":0.005011649,"about_ca_system_score_codex":0.0006389512,"about_ca_system_score_gemma":0.0007025958,"threshold_uncertainty_score":0.016765654},"labels":[],"label_agreement":null},{"id":"W2013025968","doi":"10.1117/12.382979","title":"&lt;title&gt;Kernel-based multiple-cue algorithm for object segmentation&lt;/title&gt;","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Segmentation; Kernel (algebra); Motion estimation; Motion vector; Image segmentation; Motion compensation; Quarter-pixel motion; Object (grammar); Mathematics; Image (mathematics)","score_opus":0.013947811017786955,"score_gpt":0.2532939510484193,"score_spread":0.23934614003063234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013025968","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":[0.005527689,0.00057859125,0.9891746,0.000112647635,0.000106818756,0.000052667085,0.00007276207,0.002469912,0.0019044211],"genre_scores_gemma":[0.08325179,0.0004835476,0.9075179,0.00015646442,0.0001249354,0.00009051779,0.00072028564,0.00055895036,0.0070956345],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996247,0.000046382924,0.00002481887,0.000093537696,0.00015590084,0.000054686905],"domain_scores_gemma":[0.9995691,0.000072828385,0.000032737225,0.000092610724,0.00020047594,0.00003229725],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031580823,0.0007220546,0.0009817801,0.0012698094,0.00052491,0.0011104335,0.0013221193,0.0009977834,0.0072300453],"category_scores_gemma":[0.0010004382,0.0002539573,0.00056005723,0.0015972332,0.00053572847,0.0013833455,0.0007294881,0.0008988078,0.0041728057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037912137,0.00007561356,0.00047697334,0.00013930864,0.000041210125,0.000121989004,0.00007652129,0.026786035,0.048512407,0.00938152,0.011618009,0.90239143],"study_design_scores_gemma":[0.00004366128,0.00010908873,0.00073260994,0.000023056156,0.000027678683,0.00019875563,0.000036002646,0.91271716,0.058381546,0.005460123,0.02221669,0.00005370332],"about_ca_topic_score_codex":0.006474805,"about_ca_topic_score_gemma":0.00626845,"teacher_disagreement_score":0.0072300453,"about_ca_system_score_codex":0.0007758744,"about_ca_system_score_gemma":0.000948948,"threshold_uncertainty_score":0.02418691},"labels":[],"label_agreement":null},{"id":"W2013604415","doi":"10.1007/s11042-012-1075-3","title":"Fast moving object detection with non-stationary background","year":2012,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Optical flow; Delaunay triangulation; Background subtraction; Object detection; Foreground detection; Corner detection; Motion estimation; Anomaly detection; Feature (linguistics); Cluster analysis; Motion detection; Pattern recognition (psychology); Motion (physics); Algorithm; Pixel; Image (mathematics)","score_opus":0.030685619952663806,"score_gpt":0.2871196692035083,"score_spread":0.25643404925084445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2013604415","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":[0.05328925,0.0009028103,0.9429988,0.00010134551,0.00014572057,0.00004971652,0.000064910506,0.0006767143,0.0017708031],"genre_scores_gemma":[0.39570782,0.0017938477,0.59166706,0.00019690151,0.0002151912,0.00009125639,0.00058125297,0.00023943507,0.00950719],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992273,0.00010090859,0.000033269454,0.00019158327,0.000343826,0.000103180566],"domain_scores_gemma":[0.99905497,0.000354878,0.00006181639,0.00017249085,0.00028547546,0.00007032031],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012663425,0.0009559355,0.0011551066,0.002157082,0.00052278663,0.0011264253,0.0010267519,0.0013614486,0.0015379464],"category_scores_gemma":[0.0025800487,0.0007017386,0.0007281444,0.0017429431,0.0004541323,0.0014635614,0.0010500081,0.0010176799,0.0010480918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007989865,0.00027102878,0.0036720182,0.00035250027,0.00021475194,0.00089629786,0.00018675526,0.025327722,0.21893044,0.0073549957,0.0026269385,0.7393676],"study_design_scores_gemma":[0.000037582853,0.00020168335,0.007432493,0.000030443649,0.00017952018,0.0016146266,0.00006477591,0.8956784,0.086367145,0.0036884856,0.004671226,0.00003359575],"about_ca_topic_score_codex":0.0014727799,"about_ca_topic_score_gemma":0.0019691666,"teacher_disagreement_score":0.002157082,"about_ca_system_score_codex":0.00030462054,"about_ca_system_score_gemma":0.0005544313,"threshold_uncertainty_score":0.006697178},"labels":[],"label_agreement":null},{"id":"W2014887345","doi":"10.1109/cjece.2007.365712","title":"Automatic detection of player positions and trajectories during a soccer match for the measurement of physical and tactical performance","year":2007,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Projection (relational algebra); Perspective (graphical); Event (particle physics); Measure (data warehouse); Cover (algebra); Artificial intelligence; Computer vision; Field (mathematics); Simulation; Mathematics; Engineering; Data mining; Algorithm","score_opus":0.011122401929163498,"score_gpt":0.21569893685337377,"score_spread":0.20457653492421027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014887345","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":[0.56615674,0.00041808764,0.42672953,0.00008104407,0.000072159535,0.00017146807,0.00092379306,0.0014411119,0.004006077],"genre_scores_gemma":[0.8665695,0.00029206328,0.12914878,0.000025923946,0.00003747212,0.00008803199,0.0011701632,0.00006399915,0.0026039137],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99969935,0.000039692426,0.000008340083,0.000090675865,0.00012128508,0.00004067282],"domain_scores_gemma":[0.9997162,0.00005124988,0.000052569514,0.00002263025,0.00010429083,0.000053049775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019351831,0.0005264565,0.00040154537,0.0014767254,0.00026018074,0.00039230834,0.0004830889,0.00038304046,0.00090711744],"category_scores_gemma":[0.0005899531,0.00019335987,0.00013756672,0.0005518608,0.00012810258,0.000303093,0.00041771337,0.00022971928,0.00062706106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010104092,0.00036787608,0.052445892,0.00020530642,0.00010036859,0.0003357606,0.00046544865,0.01472619,0.2788377,0.0012626647,0.002250295,0.647992],"study_design_scores_gemma":[0.00010216093,0.0010477625,0.26943746,0.000068531765,0.0001443415,0.0016894046,0.0007001729,0.55168706,0.16374329,0.0016211866,0.009672395,0.000086297565],"about_ca_topic_score_codex":0.002402484,"about_ca_topic_score_gemma":0.006192066,"teacher_disagreement_score":0.002402484,"about_ca_system_score_codex":0.00017820146,"about_ca_system_score_gemma":0.0003623936,"threshold_uncertainty_score":0.0047770143},"labels":[],"label_agreement":null},{"id":"W2014959166","doi":"10.1145/1992896.1992914","title":"Occlusion handling based on sub-blobbing in automated video surveillance system","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Tracking (education); Context (archaeology); Occlusion; Video tracking; Feature (linguistics); Process (computing); Object detection; Object (grammar); Pattern recognition (psychology)","score_opus":0.03080439445709003,"score_gpt":0.2621430520961431,"score_spread":0.23133865763905304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2014959166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.189328,0.0005657904,0.8067871,0.00007509718,0.000031501775,0.000054683092,0.000029616074,0.0012601472,0.0018680613],"genre_scores_gemma":[0.81192005,0.0003483328,0.18593122,0.000040412455,0.00003362246,0.000034673114,0.000085101456,0.000063003885,0.0015436125],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99938774,0.00016059315,0.000027025479,0.00012132882,0.00023099138,0.0000722346],"domain_scores_gemma":[0.9993599,0.00019333577,0.00013726174,0.000092795104,0.00016815624,0.000048537604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007207003,0.0002894996,0.0006832305,0.0007923171,0.0005024739,0.0005065468,0.00046663094,0.0004601969,0.00056181545],"category_scores_gemma":[0.0016045994,0.00026020926,0.0003051074,0.00066985685,0.00040568184,0.0006743782,0.00042228037,0.00030660475,0.00021700724],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010219448,0.00020922872,0.00723228,0.00015005545,0.00010553451,0.0005615422,0.00080180407,0.13618362,0.1979185,0.005749411,0.0020057168,0.6480604],"study_design_scores_gemma":[0.00002130203,0.00012776282,0.0075244177,0.0000102590475,0.000046037578,0.00025587593,0.000036637593,0.9629029,0.02546355,0.001489297,0.0020960455,0.000025891024],"about_ca_topic_score_codex":0.006038995,"about_ca_topic_score_gemma":0.004104295,"teacher_disagreement_score":0.006038995,"about_ca_system_score_codex":0.0005453525,"about_ca_system_score_gemma":0.0004556576,"threshold_uncertainty_score":0.012007713},"labels":[],"label_agreement":null},{"id":"W2016153077","doi":"10.1109/jetcas.2013.2256827","title":"Software Laboratory for Camera Networks Research","year":2013,"lang":"en","type":"article","venue":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Pennsylvania","keywords":"Software; Computer science; Computer graphics (images); Operating system","score_opus":0.05937322974766214,"score_gpt":0.3387975889828744,"score_spread":0.27942435923521225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016153077","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067934613,0.0018204551,0.8033693,0.0016979214,0.0011432589,0.00055312127,0.0064302697,0.0825136,0.09567859],"genre_scores_gemma":[0.12298175,0.00351258,0.7036597,0.0008332915,0.00042800375,0.0019202358,0.027320728,0.012598459,0.12674527],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99757844,0.00047687523,0.00014418049,0.00058317947,0.0010220755,0.00019526717],"domain_scores_gemma":[0.9955623,0.00099362,0.0002068475,0.0013329356,0.0014413144,0.0004629297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002180297,0.0017535198,0.0012550235,0.0017055252,0.00075964554,0.0023992702,0.0027445296,0.0012760649,0.094314426],"category_scores_gemma":[0.0066548362,0.00076982647,0.0008903001,0.0017561879,0.0007832018,0.003633621,0.002063535,0.0029444387,0.036793444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006173767,0.00047736664,0.0030057807,0.00061363785,0.00014093716,0.0003535191,0.00035802653,0.030432453,0.02085468,0.1845468,0.29859808,0.4600014],"study_design_scores_gemma":[0.0004090094,0.00032054321,0.0014183822,0.00022118082,0.00007229186,0.0004708648,0.00009724437,0.22050694,0.01792089,0.049662545,0.70878285,0.00011728903],"about_ca_topic_score_codex":0.0044754,"about_ca_topic_score_gemma":0.0036065148,"teacher_disagreement_score":0.094314426,"about_ca_system_score_codex":0.0015818637,"about_ca_system_score_gemma":0.0026640655,"threshold_uncertainty_score":0.3155132},"labels":[],"label_agreement":null},{"id":"W2016498280","doi":"10.1007/s00034-014-9820-7","title":"A Parallel Systematic Resampling Algorithm for High-Speed Particle Filters in Embedded Systems","year":2014,"lang":"en","type":"article","venue":"Circuits Systems and Signal Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Speedup; Resampling; Algorithm; Computer science; Set (abstract data type); Floating point; Point (geometry); Particle filter; Auxiliary particle filter; Parallel computing; Filter (signal processing); Mathematics; Artificial intelligence","score_opus":0.042041799864920404,"score_gpt":0.28525076145988676,"score_spread":0.24320896159496636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2016498280","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0020002907,0.00009654033,0.9972168,0.00003180673,0.000041563035,0.000021472308,0.000013335067,0.00024165565,0.00033653702],"genre_scores_gemma":[0.059107028,0.00021965902,0.9380427,0.00005907038,0.00007861639,0.00011407329,0.00013762871,0.00014470969,0.0020964788],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934596,0.0001608315,0.000046411977,0.00009608992,0.00030657445,0.000044132474],"domain_scores_gemma":[0.9990952,0.00029542897,0.000056850437,0.00019988808,0.00031733976,0.00003539504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010995158,0.0008466339,0.00081000867,0.0006489006,0.000626656,0.00066809624,0.00096174143,0.00072416395,0.0028087664],"category_scores_gemma":[0.0026856484,0.00056956365,0.00090628554,0.0008818493,0.00045629957,0.0008721061,0.0008748044,0.0011105514,0.0012714257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052541355,0.00015829608,0.0009083316,0.00022035115,0.00019681787,0.00012239291,0.000121827536,0.24013267,0.045252755,0.033536557,0.006287511,0.67253715],"study_design_scores_gemma":[0.000029036391,0.000056004454,0.00023882055,0.0000074187087,0.000021104148,0.000049034417,0.0000058552146,0.9860787,0.007460837,0.003074279,0.0029643835,0.000014423765],"about_ca_topic_score_codex":0.007306675,"about_ca_topic_score_gemma":0.010252879,"teacher_disagreement_score":0.007306675,"about_ca_system_score_codex":0.00058588607,"about_ca_system_score_gemma":0.001624554,"threshold_uncertainty_score":0.014528275},"labels":[],"label_agreement":null},{"id":"W2017804980","doi":"10.4018/ijsi.2013100104","title":"Construction of Shadow Model by Robust Features to Illumination Changes","year":2013,"lang":"en","type":"article","venue":"International Journal of Software Innovation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Chrominance; Shadow (psychology); Computer vision; Artificial intelligence; Computer science; Color space; Image (mathematics); Luminance; Pattern recognition (psychology)","score_opus":0.0222056206639148,"score_gpt":0.2839366666656747,"score_spread":0.2617310460017599,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017804980","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024105975,0.00010589699,0.9743937,0.000026579057,0.000017040487,0.000022251728,0.000046242854,0.00057607354,0.0007061503],"genre_scores_gemma":[0.6972503,0.00051082036,0.29820916,0.0000716108,0.000055055018,0.00008069159,0.0004749739,0.0002746827,0.003072744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997154,0.000029959523,0.000010689368,0.00006693591,0.00014753151,0.00002947584],"domain_scores_gemma":[0.9997696,0.000040044913,0.000031321226,0.000063172214,0.00007885082,0.000016872236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020744452,0.0005048691,0.0006477593,0.00071410596,0.00023935983,0.0005467721,0.0006269185,0.00038655227,0.0008937403],"category_scores_gemma":[0.00070579967,0.00035292664,0.00081144244,0.00042439965,0.0003705489,0.0011348699,0.00054617703,0.0005336987,0.00040390814],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022692964,0.00010008438,0.0030223601,0.00018657406,0.00013448033,0.00034554276,0.00024764237,0.43912098,0.13799521,0.011650114,0.0029195186,0.4040506],"study_design_scores_gemma":[0.000007571753,0.00003521983,0.00082599605,0.0000047537746,0.000018152383,0.00010844044,0.000016888866,0.98042196,0.015030561,0.0019009126,0.0016125791,0.000016868067],"about_ca_topic_score_codex":0.0031766181,"about_ca_topic_score_gemma":0.0022422844,"teacher_disagreement_score":0.0031766181,"about_ca_system_score_codex":0.00044945636,"about_ca_system_score_gemma":0.00059636997,"threshold_uncertainty_score":0.006316304},"labels":[],"label_agreement":null},{"id":"W2017960002","doi":"10.1016/j.rti.2005.03.004","title":"Detection of cyclic human activities based on the morphological analysis of the inter-frame similarity matrix","year":2005,"lang":"en","type":"article","venue":"Real-Time Imaging","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Similarity (geometry); Frame (networking); Segmentation; Matrix (chemical analysis); Artificial intelligence; Pattern recognition (psychology); Computer science; Ground truth; Motion (physics); Activity detection; Computer vision; Mathematics; Image (mathematics)","score_opus":0.017516115602828813,"score_gpt":0.30396807748959304,"score_spread":0.28645196188676425,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2017960002","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.23431474,0.00043476353,0.7624436,0.00010341864,0.000053711556,0.00007652027,0.00011485772,0.00040337833,0.002055002],"genre_scores_gemma":[0.76463544,0.00054183154,0.23296,0.000033190987,0.000055889333,0.000047350582,0.00021394373,0.000064697124,0.0014476606],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99977213,0.000028287483,0.000011997909,0.000058246595,0.000103773506,0.000025471738],"domain_scores_gemma":[0.9993187,0.00017284996,0.0001489814,0.000055510707,0.00023851544,0.00006535871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028710306,0.000337023,0.0003474993,0.0017437523,0.00019622792,0.00047732965,0.00039374782,0.000339731,0.00086783624],"category_scores_gemma":[0.0012844922,0.00018298336,0.00034940845,0.0011403081,0.00039249499,0.0005397381,0.0003379243,0.0003161833,0.00032057718],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006571026,0.00012716607,0.007073175,0.00025451352,0.0000992547,0.00036920488,0.00027251852,0.0091542415,0.37343696,0.0038992888,0.001079175,0.6035774],"study_design_scores_gemma":[0.000052050684,0.0006156639,0.105438545,0.00006172889,0.00018664986,0.004146482,0.00037950778,0.72442913,0.14945452,0.008689999,0.0064286888,0.00011703812],"about_ca_topic_score_codex":0.0009573998,"about_ca_topic_score_gemma":0.0011162753,"teacher_disagreement_score":0.0017437523,"about_ca_system_score_codex":0.00015063677,"about_ca_system_score_gemma":0.00035369105,"threshold_uncertainty_score":0.002903223},"labels":[],"label_agreement":null},{"id":"W2019143289","doi":"10.1109/iccsna.2010.5588984","title":"Multi-target tracking with mobile camera using convex combination detection and nearest distance decision","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Tracking (education); Standard deviation; Pattern recognition (psychology); Mathematics","score_opus":0.020661948245342156,"score_gpt":0.2960013780740958,"score_spread":0.27533942982875365,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019143289","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019236902,0.00012580479,0.97987914,0.00005225303,0.000015160222,0.000014556061,0.000008792356,0.00018070974,0.00048676695],"genre_scores_gemma":[0.52564776,0.00021193159,0.47268182,0.000060852573,0.000032178807,0.000047520472,0.00005244869,0.000047849353,0.0012175768],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989856,0.00029125845,0.000034781584,0.00024098139,0.0003848225,0.000062633546],"domain_scores_gemma":[0.9987993,0.0006505253,0.00015516007,0.00010498978,0.00022555192,0.000064499865],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009211084,0.0007342663,0.0013469419,0.0008762418,0.00038640012,0.00073903607,0.0010788492,0.0008162853,0.0005646532],"category_scores_gemma":[0.0022689397,0.0005143113,0.00088056264,0.000763984,0.0007085057,0.0015839739,0.00084997877,0.0008570914,0.00021391876],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005355125,0.00014880483,0.0024643976,0.00014826811,0.00020256321,0.0003782604,0.00020741552,0.576309,0.052117087,0.0140308,0.0012287433,0.35222906],"study_design_scores_gemma":[0.0000053574317,0.00003833689,0.00019597073,0.0000018223537,0.000005927377,0.000055432603,0.000004528903,0.99502665,0.0034884408,0.001004634,0.00016364102,0.000009311992],"about_ca_topic_score_codex":0.0031094204,"about_ca_topic_score_gemma":0.0022459188,"teacher_disagreement_score":0.0031094204,"about_ca_system_score_codex":0.00093582034,"about_ca_system_score_gemma":0.0004906565,"threshold_uncertainty_score":0.0067899227},"labels":[],"label_agreement":null},{"id":"W2019530136","doi":"10.1109/avss.2013.6636632","title":"AWARE: A video monitoring library applied to the Air Traffic Control context","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National d'Optique","funders":"","keywords":"Runway; Air traffic control; Computer science; Context (archaeology); Real-time computing; Video monitoring; Control (management); Tracking system; Artificial intelligence; Engineering; Geography; Kalman filter","score_opus":0.01414533942525056,"score_gpt":0.2371798942436083,"score_spread":0.22303455481835774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2019530136","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07369063,0.0020400134,0.79826516,0.00021182568,0.00016389521,0.0005746253,0.0027203579,0.10817845,0.014154937],"genre_scores_gemma":[0.5420307,0.001922614,0.42095137,0.0005951109,0.00032339513,0.00043398666,0.006225121,0.0043747914,0.023142882],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996014,0.00006977276,0.000019660052,0.00013249324,0.000121544595,0.00005512252],"domain_scores_gemma":[0.99956375,0.00009806856,0.000046016397,0.000098475204,0.00009428706,0.00009950088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034817198,0.0011131353,0.000708863,0.0012718751,0.00038065505,0.0012333285,0.0014321778,0.00078031205,0.0069199484],"category_scores_gemma":[0.0010320432,0.00045016687,0.0003870822,0.0007105915,0.00020245096,0.0014039637,0.0012512906,0.0007652135,0.0032649033],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020055724,0.00060092047,0.004634071,0.0011823447,0.00021623608,0.00155706,0.00084835396,0.00896675,0.21539615,0.0037940552,0.036659762,0.7241387],"study_design_scores_gemma":[0.0003410755,0.0020536087,0.031767953,0.00047833996,0.00059017946,0.0046421518,0.0005993345,0.35381162,0.3546464,0.0040851342,0.2466132,0.00037093903],"about_ca_topic_score_codex":0.0017776503,"about_ca_topic_score_gemma":0.0020269812,"teacher_disagreement_score":0.0069199484,"about_ca_system_score_codex":0.00031950194,"about_ca_system_score_gemma":0.00048644128,"threshold_uncertainty_score":0.02314955},"labels":[],"label_agreement":null},{"id":"W2020008225","doi":"10.1109/smc.2013.724","title":"Tracked Object Association in Multi-camera Surveillance Network","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Homography; Object (grammar); Camera resectioning; Association (psychology); Field of view; Object detection; Ground plane; Camera auto-calibration; Constraint (computer-aided design); Computer graphics (images); Mathematics; Pattern recognition (psychology)","score_opus":0.026314565200330427,"score_gpt":0.28179437073029556,"score_spread":0.25547980552996513,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2020008225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043331563,0.0005224793,0.9552262,0.00006800628,0.00003385322,0.00002710022,0.000036569167,0.00022761001,0.00052667136],"genre_scores_gemma":[0.7683292,0.00059088226,0.22896743,0.00007012615,0.00009986763,0.00008548244,0.00020853823,0.00003764617,0.0016108977],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983575,0.00041706086,0.00007179523,0.0005928826,0.00039186588,0.0001688396],"domain_scores_gemma":[0.9987287,0.0004316491,0.00027728913,0.00017828862,0.0002809814,0.00010310213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013979365,0.00066666864,0.0011890245,0.0012045084,0.0006234189,0.0010361479,0.0017495089,0.0009965654,0.00046782344],"category_scores_gemma":[0.0026119507,0.00063894386,0.0006193453,0.0012899799,0.0006078543,0.0022974482,0.0013984019,0.00072961807,0.00017278561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005398144,0.00014905272,0.008390323,0.00015412935,0.00020252466,0.0006738035,0.00043143806,0.6530657,0.017864734,0.017831597,0.0014088033,0.29928815],"study_design_scores_gemma":[0.000007117831,0.0000381425,0.00076233334,0.000004227004,0.000020635409,0.00008369353,0.000026530752,0.9946127,0.0018059484,0.0021729795,0.00045701937,0.000008738123],"about_ca_topic_score_codex":0.0048804567,"about_ca_topic_score_gemma":0.0035237232,"teacher_disagreement_score":0.0048804567,"about_ca_system_score_codex":0.0009528933,"about_ca_system_score_gemma":0.00061731186,"threshold_uncertainty_score":0.009704113},"labels":[],"label_agreement":null},{"id":"W2023120519","doi":"10.1007/s00371-014-1022-6","title":"Unique people count from monocular videos","year":2014,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"University of California, San Diego; University of Virginia","keywords":"Computer science; Bottleneck; Computer vision; Artificial intelligence; Feature (linguistics); Region of interest; Tracking (education); Background subtraction; Video tracking; Volume (thermodynamics); Monocular; Boundary (topology); Object (grammar); Pixel; Mathematics","score_opus":0.012917023435802881,"score_gpt":0.2765635483620235,"score_spread":0.26364652492622065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023120519","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":[0.6320865,0.0021918549,0.34823748,0.00025455392,0.00037417625,0.0001903758,0.004048429,0.0011098059,0.011506774],"genre_scores_gemma":[0.9030493,0.0011109012,0.0858629,0.00012731148,0.00032766574,0.00008507651,0.0033538782,0.00008818255,0.0059947725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934095,0.00009236734,0.00002546335,0.00021107818,0.0002373743,0.00009264921],"domain_scores_gemma":[0.9993825,0.00015076714,0.00009495769,0.00009761497,0.00021196305,0.00006224378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036344296,0.000662735,0.0008316871,0.002917303,0.0003002065,0.0007757838,0.0005760224,0.0005400613,0.0020448014],"category_scores_gemma":[0.0022417211,0.00030790537,0.00034964317,0.0019171623,0.00026344974,0.0009003421,0.0010384604,0.00031675596,0.0012481498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007671503,0.00020471508,0.08222657,0.00046032268,0.00023705844,0.00071849383,0.00038536263,0.013943871,0.06779524,0.0025664514,0.010269703,0.82042503],"study_design_scores_gemma":[0.000038333616,0.0004311459,0.34036922,0.00010741254,0.00019882542,0.00531236,0.0010267901,0.59025073,0.048205625,0.005018267,0.008957911,0.00008335863],"about_ca_topic_score_codex":0.0032530588,"about_ca_topic_score_gemma":0.0075942744,"teacher_disagreement_score":0.0032530588,"about_ca_system_score_codex":0.00028717314,"about_ca_system_score_gemma":0.00024058919,"threshold_uncertainty_score":0.006840527},"labels":[],"label_agreement":null},{"id":"W2023203414","doi":"10.1109/fpt.2012.6412130","title":"An energy-efficient, fast FPGA hardware architecture for OpenCV-Compatible object detection","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Field-programmable gate array; Computer science; Object detection; Embedded system; Architecture; Computer hardware; Hardware architecture; Object (grammar); Artificial intelligence; Operating system; Pattern recognition (psychology); Software","score_opus":0.02761485296492128,"score_gpt":0.3024174569293474,"score_spread":0.27480260396442613,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2023203414","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":[0.081769004,0.001410371,0.88518023,0.00039738984,0.0004027932,0.00019589606,0.00023640755,0.011440257,0.018967738],"genre_scores_gemma":[0.632792,0.00046220407,0.35121122,0.00029428944,0.00010703991,0.00013184227,0.0005174103,0.00020678635,0.014277294],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978656,0.00002116579,0.000013778438,0.000053564847,0.000086986045,0.000037859987],"domain_scores_gemma":[0.9998085,0.000028585822,0.000017832886,0.000030557523,0.00009991668,0.000014574904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018598638,0.0003799662,0.0002243229,0.0005552706,0.00030583606,0.00065191713,0.0011629338,0.00038152296,0.006224784],"category_scores_gemma":[0.0005072433,0.00023324146,0.00018374884,0.0004066227,0.00018139492,0.000680977,0.00030255862,0.000425844,0.00190261],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007196726,0.00018878227,0.0023362946,0.0003672937,0.00008083776,0.00050084456,0.00016941519,0.012245571,0.21176375,0.018049395,0.02105361,0.7325245],"study_design_scores_gemma":[0.00037344327,0.00199981,0.007692494,0.00017322098,0.000185679,0.0028420012,0.00014042546,0.471122,0.38429382,0.01062578,0.12038509,0.00016630671],"about_ca_topic_score_codex":0.0016795596,"about_ca_topic_score_gemma":0.0031869507,"teacher_disagreement_score":0.006224784,"about_ca_system_score_codex":0.00050347904,"about_ca_system_score_gemma":0.00055062864,"threshold_uncertainty_score":0.020823956},"labels":[],"label_agreement":null},{"id":"W2024495812","doi":"10.1109/itsc.2011.6082972","title":"Real-time vehicle detection and tracking using stereo vision and multi-view AdaBoost","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Computer vision; AdaBoost; Computer science; Optical flow; Robustness (evolution); Kalman filter; False positive paradox; Detector; Stereopsis; Ground plane; Object detection; Pattern recognition (psychology); Support vector machine; Image (mathematics)","score_opus":0.07725495372989236,"score_gpt":0.317000329585579,"score_spread":0.23974537585568664,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2024495812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011483115,0.000069962756,0.986273,0.000028907985,0.000024308512,0.000027050612,0.000024732464,0.0015980302,0.00047093385],"genre_scores_gemma":[0.28084168,0.00009350567,0.7167703,0.00007998757,0.000034128123,0.000091537564,0.00014662242,0.00010658801,0.0018356929],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939775,0.00007055755,0.000026242738,0.0001596929,0.00025637995,0.00008934524],"domain_scores_gemma":[0.9993998,0.00012967699,0.0000828397,0.00007817343,0.00025782143,0.000051752035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008455399,0.00060790934,0.00080376153,0.0010575812,0.00038399143,0.0007503315,0.0014953833,0.00093700347,0.0015753049],"category_scores_gemma":[0.001111972,0.00063674385,0.00064517494,0.00054819725,0.0003274179,0.0012030182,0.0006121347,0.0007273455,0.0008319962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035484275,0.0003415053,0.0022922992,0.0001243945,0.00013532556,0.000083452964,0.0000768165,0.09780273,0.11362158,0.0025845303,0.0026412008,0.7799413],"study_design_scores_gemma":[0.000026838357,0.000093564006,0.0013104494,0.000006328053,0.000023559043,0.00007302166,0.000009348261,0.96377325,0.032431368,0.00093543535,0.0012953873,0.00002145998],"about_ca_topic_score_codex":0.0051463763,"about_ca_topic_score_gemma":0.005909486,"teacher_disagreement_score":0.0051463763,"about_ca_system_score_codex":0.00062969705,"about_ca_system_score_gemma":0.0009296838,"threshold_uncertainty_score":0.010232866},"labels":[],"label_agreement":null},{"id":"W2027701441","doi":"10.1145/1461893.1461910","title":"Unsupervised approach for building non-parametric background and foreground models of scenes with significant foreground activity","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Background subtraction; Foreground detection; Computer science; Artificial intelligence; Initialization; Pattern recognition (psychology); Kernel (algebra); Kernel density estimation; Pixel; Prior probability; Parametric statistics; Computer vision; Frame (networking); Mathematics; Statistics; Bayesian probability","score_opus":0.10632750054632928,"score_gpt":0.301048133796664,"score_spread":0.19472063325033473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2027701441","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007053696,0.000034207595,0.99185103,0.000022043514,0.0000044997914,0.000026822823,0.000036522826,0.00068459316,0.00028645218],"genre_scores_gemma":[0.23624124,0.00014638733,0.7599056,0.0001230464,0.000045190758,0.00018853093,0.00061917125,0.0003621498,0.0023687414],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993236,0.00017527418,0.000028326369,0.00021014256,0.0002024569,0.000060285507],"domain_scores_gemma":[0.9987104,0.0005236415,0.00016987043,0.00030611255,0.00024004513,0.000049928436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00081753574,0.00085012085,0.0008456301,0.0011785653,0.0004346964,0.0009764192,0.0020331116,0.0010413879,0.0009866487],"category_scores_gemma":[0.0024826452,0.00075192953,0.0010782722,0.00073350366,0.0007427117,0.0014142664,0.001183153,0.0013572566,0.0007116151],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002576881,0.00035956746,0.0041964636,0.00015180158,0.0003260081,0.00020683401,0.00042837186,0.4529192,0.043774102,0.014617549,0.0027544133,0.48000804],"study_design_scores_gemma":[0.0000067800884,0.000021753402,0.0007451273,0.000005105765,0.00001501203,0.00007878775,0.000017824948,0.9877357,0.0064689615,0.0039023822,0.0009865841,0.000015980833],"about_ca_topic_score_codex":0.0042271153,"about_ca_topic_score_gemma":0.011244842,"teacher_disagreement_score":0.0042271153,"about_ca_system_score_codex":0.00079075387,"about_ca_system_score_gemma":0.001107845,"threshold_uncertainty_score":0.00840497},"labels":[],"label_agreement":null},{"id":"W2028218976","doi":"10.1145/2030045.2030055","title":"Inhabitant prediction in smart houses","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Apartment; Support vector machine; Computer science; Classifier (UML); Predictive modelling; Machine learning; Artificial intelligence; Data mining; Engineering; Civil engineering","score_opus":0.06096245642818541,"score_gpt":0.2546083562784825,"score_spread":0.1936458998502971,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028218976","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":[0.95120084,0.00027134994,0.04585111,0.00013439535,0.000026736207,0.000026923966,0.001031647,0.00038348418,0.0010735362],"genre_scores_gemma":[0.9911374,0.00009171208,0.0075853453,0.000012951131,0.000010120798,0.0000109011935,0.00076375983,0.000008842481,0.00037891493],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973434,0.00006114395,0.000021049063,0.00009306957,0.000053830383,0.000036493206],"domain_scores_gemma":[0.9993666,0.00024352799,0.00014892616,0.00006385182,0.00011875989,0.00005835699],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003053862,0.0005400208,0.00044098595,0.0010690938,0.00017599393,0.00047364592,0.0003331628,0.00041945893,0.00077825174],"category_scores_gemma":[0.0015065983,0.00021446527,0.00031353187,0.0007624937,0.00015275348,0.0007201995,0.00032198586,0.00031718597,0.00049320125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040649637,0.0003810622,0.6519257,0.00013630721,0.00017185666,0.00055408594,0.0003760646,0.15173796,0.0058388957,0.000717685,0.0026702795,0.18508361],"study_design_scores_gemma":[0.0000062113418,0.00009334628,0.12104842,0.000017002767,0.000026972477,0.00018120119,0.0002225147,0.87360686,0.0027520654,0.0012140579,0.0008153419,0.000016050297],"about_ca_topic_score_codex":0.014267562,"about_ca_topic_score_gemma":0.020831322,"teacher_disagreement_score":0.014267562,"about_ca_system_score_codex":0.00031463287,"about_ca_system_score_gemma":0.0001715403,"threshold_uncertainty_score":0.02836901},"labels":[],"label_agreement":null},{"id":"W2028456138","doi":"10.1109/isspa.2012.6310538","title":"Head detection using Kinect camera and its application to fall detection","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":60,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Head (geology); Computer graphics (images); Object detection; Pattern recognition (psychology); Geology","score_opus":0.043235325623752256,"score_gpt":0.33646512398817735,"score_spread":0.2932297983644251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2028456138","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":[0.042241707,0.001957778,0.94752985,0.0002615829,0.00025236246,0.00020682829,0.0009346307,0.0031358982,0.003479332],"genre_scores_gemma":[0.3069611,0.0025738892,0.68322533,0.00025804673,0.00012855254,0.00025684148,0.00093210797,0.00023493114,0.005429272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999385,0.000072730996,0.000042916567,0.00011560122,0.00034319583,0.00004054023],"domain_scores_gemma":[0.9995442,0.000083956234,0.00006824734,0.000035993704,0.0002191407,0.000048360795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035079374,0.0008299928,0.00084023416,0.0018174924,0.00029873784,0.00052317197,0.00081627315,0.00077909546,0.0022499627],"category_scores_gemma":[0.001033521,0.0005212933,0.000515191,0.001193268,0.00023623488,0.0007100655,0.0006020465,0.00046670323,0.00092187454],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055332365,0.00013929367,0.011190271,0.0008877973,0.000107692525,0.00071876164,0.00024932987,0.013609626,0.26051757,0.0024429867,0.005502144,0.70408136],"study_design_scores_gemma":[0.00009875964,0.0006048954,0.06285438,0.00029713448,0.00018673706,0.0047031376,0.00026362643,0.5247818,0.3690819,0.00501207,0.031813927,0.00030158952],"about_ca_topic_score_codex":0.0026474732,"about_ca_topic_score_gemma":0.0035421364,"teacher_disagreement_score":0.0026474732,"about_ca_system_score_codex":0.0003393474,"about_ca_system_score_gemma":0.00047816732,"threshold_uncertainty_score":0.0075268745},"labels":[],"label_agreement":null},{"id":"W2031557528","doi":"10.1109/icecs.2011.6122262","title":"A tracking algorithm suitable for embedded systems implementation","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Resampling; Particle filter; Robustness (evolution); Computer science; Implementation; Auxiliary particle filter; Algorithm; Tracking (education); Filter (signal processing); Computer engineering; Artificial intelligence; Computer vision; Kalman filter; Ensemble Kalman filter","score_opus":0.10507781199020023,"score_gpt":0.3505233256935543,"score_spread":0.2454455137033541,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2031557528","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":[0.00068601005,0.0000522902,0.99738103,0.000024698915,0.00004909184,0.000028405108,0.000017773547,0.00089059526,0.00087017124],"genre_scores_gemma":[0.03009974,0.00020876895,0.96408546,0.000057572004,0.000055196273,0.0001793892,0.00015216827,0.00017392354,0.0049877684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996985,0.000033547836,0.000029153482,0.00007261504,0.00014549826,0.000020598309],"domain_scores_gemma":[0.9996037,0.00008597133,0.000029033912,0.0000903642,0.000169176,0.000021766165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039979027,0.00055397674,0.00058034,0.00042764164,0.0003925938,0.00069367746,0.0007803437,0.00096521137,0.0056674676],"category_scores_gemma":[0.0013545003,0.00033658263,0.00050482363,0.00057622936,0.0002412183,0.00069047045,0.0005542075,0.0008680885,0.0039594295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016713925,0.000077089004,0.0005084828,0.00029755922,0.00006839993,0.0002791123,0.00011358094,0.107891485,0.09653839,0.035167046,0.012771791,0.74612],"study_design_scores_gemma":[0.00007690119,0.00012193874,0.00044472417,0.000036653073,0.000034284338,0.0004237576,0.000015152666,0.87934726,0.033456147,0.010121904,0.075889185,0.000032081407],"about_ca_topic_score_codex":0.0008677312,"about_ca_topic_score_gemma":0.00085696194,"teacher_disagreement_score":0.0056674676,"about_ca_system_score_codex":0.00026070865,"about_ca_system_score_gemma":0.000704416,"threshold_uncertainty_score":0.018959582},"labels":[],"label_agreement":null},{"id":"W2033229116","doi":"10.1109/itsc.2007.4357793","title":"Probabilistic Collision Prediction for Vision-Based Automated Road Safety Analysis","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Collision; Computer science; Probabilistic logic; Traffic conflict; Process (computing); Computation; Motion (physics); Artificial intelligence; Track (disk drive); Collision avoidance; Traffic analysis; Real-time computing; Machine learning; Data mining; Floating car data; Transport engineering; Computer security; Traffic congestion; Algorithm; Engineering","score_opus":0.016506543526234378,"score_gpt":0.3273080428498818,"score_spread":0.3108014993236474,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033229116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053538315,0.00026098828,0.9441507,0.00010223559,0.000026787307,0.000047912374,0.00007999844,0.0010864214,0.0007065722],"genre_scores_gemma":[0.8228376,0.0001561142,0.17584059,0.00006018704,0.000038685663,0.00008981083,0.00024237642,0.000051770247,0.0006828614],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994704,0.00010912028,0.000022529153,0.00013828922,0.00020333596,0.000056338115],"domain_scores_gemma":[0.9987966,0.0006235561,0.00015313015,0.000121180055,0.00025151297,0.000054057717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087604095,0.00041241542,0.0007523954,0.0012573698,0.00045161057,0.00062267575,0.0012295932,0.0006096933,0.0009878004],"category_scores_gemma":[0.00454414,0.0004626631,0.0004264505,0.0009029434,0.00049609505,0.0010278451,0.0007254322,0.0008912728,0.00028288396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025903247,0.00016332643,0.00432065,0.00006073863,0.00005718493,0.00008346306,0.000080641454,0.7114033,0.006170583,0.004796642,0.0022006424,0.2704038],"study_design_scores_gemma":[0.0000044518183,0.00001513388,0.0004342209,0.0000019091,0.0000032228281,0.000012612324,0.0000049040805,0.9969758,0.0006785414,0.0017233071,0.00014158506,0.0000043809646],"about_ca_topic_score_codex":0.008973344,"about_ca_topic_score_gemma":0.006461206,"teacher_disagreement_score":0.008973344,"about_ca_system_score_codex":0.00091893226,"about_ca_system_score_gemma":0.0010062165,"threshold_uncertainty_score":0.017842174},"labels":[],"label_agreement":null},{"id":"W2033588814","doi":"10.3141/2299-13","title":"Automated Collection of Pedestrian Data through Computer Vision Techniques","year":2012,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal; Simon Fraser University; University of British Columbia","funders":"","keywords":"Pedestrian; Computer science; Data collection; Walkability; Automation; Data set; Set (abstract data type); Artificial intelligence; Transport engineering; Computer vision; Simulation; Data mining; Statistics; Engineering; Mathematics; Built environment","score_opus":0.19638943137950415,"score_gpt":0.47153001912450204,"score_spread":0.2751405877449979,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2033588814","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11355926,0.0002361356,0.88003814,0.00007316514,0.000047845737,0.00026500842,0.00046269275,0.0027318732,0.0025859107],"genre_scores_gemma":[0.33550408,0.00018742861,0.66128296,0.000075251555,0.00003794177,0.00025226542,0.000901117,0.00007764074,0.0016812218],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896264,0.00021217973,0.00006444192,0.00028765135,0.00037883356,0.00009433447],"domain_scores_gemma":[0.99862206,0.00031344092,0.00018327957,0.00021727118,0.0006293897,0.000034513323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011891446,0.00068754546,0.0008095125,0.003230744,0.000528822,0.0008165879,0.00065155723,0.00051570864,0.0011080597],"category_scores_gemma":[0.0023107578,0.0003454519,0.0004943176,0.0029074177,0.00039514722,0.00089470897,0.00062619714,0.00055831735,0.0009014115],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019881602,0.00023497967,0.013563081,0.00017747312,0.000074498515,0.00012204065,0.00028836224,0.026808763,0.061264034,0.0017045855,0.0039020232,0.8916612],"study_design_scores_gemma":[0.00007195872,0.00052400783,0.12077407,0.00007721686,0.000106940315,0.00074105634,0.00047189096,0.7580899,0.09706066,0.006612331,0.01531975,0.00015028969],"about_ca_topic_score_codex":0.009402174,"about_ca_topic_score_gemma":0.015858022,"teacher_disagreement_score":0.009402174,"about_ca_system_score_codex":0.00053672,"about_ca_system_score_gemma":0.0013891754,"threshold_uncertainty_score":0.018694937},"labels":[],"label_agreement":null},{"id":"W2036057345","doi":"10.3390/s100201041","title":"A Multiscale Region-Based Motion Detection and Background Subtraction Algorithm","year":2010,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":71,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Background subtraction; Artificial intelligence; Computer vision; Histogram; Computer science; Subtraction; Noise (video); Algorithm; Mixture model; Division (mathematics); Gaussian; Pattern recognition (psychology); Motion (physics); Motion detection; Image (mathematics); Pixel; Mathematics","score_opus":0.02169145873165617,"score_gpt":0.27228805612461715,"score_spread":0.250596597392961,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036057345","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":[0.0043679983,0.00024742217,0.9934928,0.000030373183,0.00003429959,0.00002628976,0.000041342755,0.0011326072,0.00062682544],"genre_scores_gemma":[0.049588643,0.00027297204,0.94734764,0.000064398155,0.00005119703,0.000054972454,0.00020812932,0.00017094328,0.0022410888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994624,0.000048504633,0.00002338433,0.00013648371,0.00029025783,0.00003896483],"domain_scores_gemma":[0.99973506,0.000058379785,0.00002931581,0.000038986913,0.00011416655,0.000024038754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000495446,0.0007025606,0.00092723925,0.0013130729,0.00030149118,0.0005261164,0.0012696692,0.0006169898,0.0025059232],"category_scores_gemma":[0.0009208009,0.00050560065,0.0009173388,0.0008001705,0.0002422901,0.00083998486,0.00071687123,0.00065170345,0.0015693511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015636368,0.00008166905,0.0007241672,0.00015210235,0.00012540842,0.0001643808,0.000081061786,0.025959477,0.22562784,0.0057838317,0.0037142395,0.7374295],"study_design_scores_gemma":[0.000038582773,0.00015516905,0.0032071695,0.000024246565,0.00012626746,0.0008171834,0.00002484827,0.8710068,0.09880926,0.002496923,0.023218311,0.0000753156],"about_ca_topic_score_codex":0.0019210341,"about_ca_topic_score_gemma":0.0022402138,"teacher_disagreement_score":0.0025059232,"about_ca_system_score_codex":0.0003771754,"about_ca_system_score_gemma":0.0005111909,"threshold_uncertainty_score":0.008383155},"labels":[],"label_agreement":null},{"id":"W2036229640","doi":"10.1167/8.6.509","title":"Impact of stereoscopic vision and 3D representation of visual space on multiple object tracking performance","year":2010,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Stereoscopy; Computer vision; Artificial intelligence; Computer science; Measure (data warehouse); Representation (politics); Observer (physics); Stereopsis; Set (abstract data type); Object (grammar); Tracking (education); Eye tracking; Space (punctuation); Physics","score_opus":0.024328595766253763,"score_gpt":0.3852139541973387,"score_spread":0.36088535843108493,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036229640","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":[0.98150486,0.00040964378,0.015690729,0.000045600435,0.000033861197,0.00004410428,0.0001194928,0.00020981122,0.0019418797],"genre_scores_gemma":[0.99350995,0.0002124032,0.0052682697,0.000037747373,0.0000113126835,0.000029220471,0.00024729082,0.000060778035,0.0006229992],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987717,0.00032306204,0.00011819096,0.00023786118,0.0004001992,0.00014895297],"domain_scores_gemma":[0.9925694,0.0048316354,0.00086459605,0.00068676757,0.0005544122,0.00049316126],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014237704,0.0006926176,0.0005442296,0.00057367346,0.00019602726,0.00090892386,0.0003241505,0.0007577896,0.0016421371],"category_scores_gemma":[0.012187248,0.00023240302,0.0003513756,0.0002936955,0.00045481665,0.0010553236,0.0009961866,0.0004822989,0.00028440918],"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":[0.012248632,0.0013546612,0.03880516,0.000684138,0.0005713947,0.0002962757,0.0008495774,0.05355402,0.6903036,0.0012770768,0.00066626095,0.19938919],"study_design_scores_gemma":[0.0003531181,0.021120952,0.39474955,0.00021090331,0.00083267153,0.0013262808,0.0008459367,0.23299964,0.34150136,0.0033987402,0.0023102958,0.00035059606],"about_ca_topic_score_codex":0.0018958506,"about_ca_topic_score_gemma":0.00096485135,"teacher_disagreement_score":0.0018958506,"about_ca_system_score_codex":0.00039745003,"about_ca_system_score_gemma":0.00049061206,"threshold_uncertainty_score":0.0075297356},"labels":[],"label_agreement":null},{"id":"W2036872036","doi":"10.3390/s130708750","title":"Multi-View Human Activity Recognition in Distributed Camera Sensor Networks","year":2013,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Activity recognition; Computer science; Smart camera; Artificial intelligence; Wireless sensor network; Computer vision; Distributed computing; Real-time computing; Computer network","score_opus":0.04845155648201575,"score_gpt":0.3046635040526914,"score_spread":0.2562119475706756,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2036872036","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07755543,0.0005178915,0.9201734,0.00018766163,0.000048895436,0.000054605356,0.00008272162,0.00057709776,0.00080228894],"genre_scores_gemma":[0.87891793,0.0003469692,0.11870778,0.00007965601,0.000089754096,0.00009264206,0.00033930782,0.00002989198,0.0013960474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920124,0.00024281477,0.000032456202,0.0002740244,0.00017573171,0.000073856],"domain_scores_gemma":[0.99910295,0.0003868609,0.00015904767,0.00012301872,0.00015496912,0.00007319064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010529817,0.00068112795,0.001091739,0.0007559609,0.00029276987,0.00053196255,0.0011863292,0.0006512959,0.00042467564],"category_scores_gemma":[0.0023745154,0.00037470413,0.00045925996,0.0008111132,0.0005153553,0.0010503663,0.0006381597,0.0007342572,0.00018809653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040419228,0.00022975054,0.0027261989,0.00007461476,0.000084732594,0.00016856602,0.00012042814,0.7533903,0.008962195,0.0026591993,0.0016894357,0.22949034],"study_design_scores_gemma":[0.0000063504995,0.000016930484,0.0004831521,0.0000014971515,0.000002561638,0.000021134623,0.000014164537,0.99756265,0.00077239046,0.0009961725,0.00012021843,0.0000028233997],"about_ca_topic_score_codex":0.005984155,"about_ca_topic_score_gemma":0.004900364,"teacher_disagreement_score":0.005984155,"about_ca_system_score_codex":0.00056482194,"about_ca_system_score_gemma":0.00041460473,"threshold_uncertainty_score":0.011898637},"labels":[],"label_agreement":null},{"id":"W2038827477","doi":"10.1109/crv.2013.11","title":"A GIS-Centric Optical Tracking System and Lap Simulator for Short Track Speed Skating","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Simulation; Tracking (education); Particle filter; Computation; Trajectory; Track (disk drive); Real-time computing; Computer vision; Artificial intelligence; Filter (signal processing)","score_opus":0.03753929867177446,"score_gpt":0.29935296203290757,"score_spread":0.2618136633611331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2038827477","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08508397,0.00007248041,0.88653916,0.00009069829,0.00007709277,0.00028874344,0.00075656193,0.020855634,0.0062357225],"genre_scores_gemma":[0.7178419,0.000121225894,0.27473113,0.00007495703,0.00002388908,0.00038979933,0.0017424785,0.00038789952,0.0046866606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986696,0.000017556033,0.000007208143,0.000031779382,0.000063828425,0.000012648489],"domain_scores_gemma":[0.9998211,0.000034358818,0.000015418795,0.000040202292,0.000058830054,0.000030133368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031245372,0.00041139784,0.00032932972,0.0005393136,0.00024969943,0.00042588337,0.0010006559,0.00041095685,0.0051490483],"category_scores_gemma":[0.0005977662,0.00019936495,0.0002907163,0.0003397645,0.00020543151,0.0005303561,0.0005000562,0.00033241438,0.0011587462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010804712,0.0005123892,0.014657697,0.00035592628,0.00020976055,0.00088294386,0.0007553126,0.34707895,0.1446134,0.009663515,0.019766945,0.46042266],"study_design_scores_gemma":[0.00006707299,0.0001853602,0.002319051,0.000013769068,0.000030939096,0.00015065109,0.000047871865,0.9597691,0.0214319,0.00074501993,0.015208381,0.000030881645],"about_ca_topic_score_codex":0.005406349,"about_ca_topic_score_gemma":0.003969241,"teacher_disagreement_score":0.005406349,"about_ca_system_score_codex":0.00045914546,"about_ca_system_score_gemma":0.0007500098,"threshold_uncertainty_score":0.017225266},"labels":[],"label_agreement":null},{"id":"W2041981713","doi":"10.1117/12.548359","title":"Advanced surveillance systems: combining video and thermal imagery for pedestrian detection","year":2004,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Pedestrian; Pedestrian detection; Segmentation; Context (archaeology); Region of interest; Exploit; Track (disk drive); Image (mathematics); Image segmentation; Computer security; Geography","score_opus":0.013027566542164193,"score_gpt":0.2445575705122008,"score_spread":0.23153000397003662,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2041981713","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17368406,0.0063097226,0.8076326,0.0002430304,0.00024482838,0.00026984443,0.00041266455,0.002102327,0.009100895],"genre_scores_gemma":[0.5672055,0.0020772426,0.42542326,0.00013180728,0.00023551637,0.00010105254,0.00050705764,0.000062726096,0.004255832],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994444,0.00014532013,0.000013009719,0.00013053771,0.0002265155,0.00004026729],"domain_scores_gemma":[0.9997546,0.000054931224,0.00003484875,0.000029655223,0.00009921786,0.000026786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068548747,0.00055638177,0.0004578075,0.0012976805,0.0002037595,0.0006148082,0.00041797743,0.0005785763,0.0018344922],"category_scores_gemma":[0.00076784205,0.00029344528,0.0003402939,0.0008521723,0.000208158,0.0007569621,0.00048436772,0.00032555594,0.0007439095],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009406132,0.00027176942,0.005236807,0.0004174233,0.00019087622,0.00020107802,0.0001228543,0.017286891,0.21118498,0.0024646206,0.0038003612,0.75788176],"study_design_scores_gemma":[0.00014464652,0.002483263,0.04676819,0.00019388265,0.00041651106,0.0034597008,0.00023652495,0.69720066,0.21049383,0.007504224,0.030895595,0.00020308692],"about_ca_topic_score_codex":0.0009922269,"about_ca_topic_score_gemma":0.0018675812,"teacher_disagreement_score":0.0018344922,"about_ca_system_score_codex":0.00025673484,"about_ca_system_score_gemma":0.00019501497,"threshold_uncertainty_score":0.006136954},"labels":[],"label_agreement":null},{"id":"W2043327173","doi":"10.1117/12.2024533","title":"Joint estimation fusion and tracking of objects in a single camera using EM-EKF","year":2013,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Kalman filter; Extended Kalman filter; Tracking (education); Video tracking; Joint (building); Robot; Object (grammar); Engineering","score_opus":0.024029028081510116,"score_gpt":0.2534090335067476,"score_spread":0.22938000542523748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2043327173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010639196,0.0001320478,0.98842955,0.000027560058,0.000023174236,0.000011916884,0.000021195494,0.000374579,0.00034068062],"genre_scores_gemma":[0.37947777,0.0003180181,0.6158013,0.000069066635,0.000057815076,0.00007479758,0.00038572197,0.0001058904,0.003709728],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993013,0.00009499328,0.000050497918,0.00023870423,0.00023618685,0.00007821441],"domain_scores_gemma":[0.9995109,0.0001016577,0.00007685588,0.00010820013,0.00017977387,0.000022634475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010695017,0.0011030708,0.0015135141,0.00088131387,0.00035877424,0.00078744156,0.0011844672,0.0010259369,0.000751605],"category_scores_gemma":[0.0021785859,0.00060341426,0.0010574108,0.001042312,0.00045441056,0.0017276659,0.0012624934,0.0008893759,0.00052218797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019315137,0.00010882964,0.0032000877,0.00010539292,0.00018021646,0.00013107683,0.00015194438,0.5072327,0.024333598,0.0035900634,0.0017515454,0.4590214],"study_design_scores_gemma":[0.000007178366,0.000031783093,0.000935204,0.000006026544,0.00001915219,0.00005233728,0.000015672864,0.99197376,0.005151926,0.0010176003,0.0007733931,0.000015923531],"about_ca_topic_score_codex":0.0058370265,"about_ca_topic_score_gemma":0.0052613537,"teacher_disagreement_score":0.0058370265,"about_ca_system_score_codex":0.0005354814,"about_ca_system_score_gemma":0.0007333813,"threshold_uncertainty_score":0.011606097},"labels":[],"label_agreement":null},{"id":"W2044096113","doi":"10.1016/j.trc.2015.04.003","title":"Automated classification based on video data at intersections with heavy pedestrian and bicycle traffic: Methodology and application","year":2015,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":80,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Pedestrian; Computer science; Transport engineering; Artificial intelligence; Engineering","score_opus":0.3242214089628708,"score_gpt":0.4510138252324115,"score_spread":0.1267924162695407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2044096113","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6319158,0.00073663605,0.35789695,0.0001714723,0.00030359533,0.00034351303,0.0021889298,0.0022923814,0.0041507306],"genre_scores_gemma":[0.86390114,0.0003040508,0.12944858,0.000052334915,0.000113688635,0.00015811269,0.0031442558,0.00006105399,0.0028167113],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994475,0.000079483434,0.00003522088,0.00017725353,0.00014377006,0.00011663947],"domain_scores_gemma":[0.9988005,0.00027807817,0.00011026713,0.00009878721,0.00063865416,0.00007373138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006180074,0.0007395152,0.00078010245,0.0036304404,0.00047507585,0.0011149797,0.00081224006,0.0009564661,0.0013292453],"category_scores_gemma":[0.0015054725,0.00022599414,0.00050180097,0.002145826,0.00022829324,0.0007176501,0.0004449874,0.00042091153,0.0008609076],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00074862846,0.00083801517,0.05924091,0.00017206742,0.000113551345,0.0003148623,0.00018255897,0.020159148,0.044605173,0.00077834836,0.0045606466,0.8682861],"study_design_scores_gemma":[0.000039438168,0.0002535523,0.06507861,0.000036311463,0.00013018513,0.00027655822,0.00047354767,0.90439993,0.024977162,0.0013698832,0.0029189163,0.000045924633],"about_ca_topic_score_codex":0.008040586,"about_ca_topic_score_gemma":0.008621785,"teacher_disagreement_score":0.008040586,"about_ca_system_score_codex":0.0004978359,"about_ca_system_score_gemma":0.0006842163,"threshold_uncertainty_score":0.015987575},"labels":[],"label_agreement":null},{"id":"W2045405067","doi":"10.1145/2647868.2655070","title":"A Real-Time Smart Assistant for Video Surveillance Through Handheld Devices","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Zoom; Mobile device; Computer vision; Context (archaeology); Object detection; Face detection; Real-time computing; Artificial intelligence; Multimedia; Facial recognition system; Feature extraction; World Wide Web","score_opus":0.025438538424764307,"score_gpt":0.2979382208437711,"score_spread":0.2724996824190068,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045405067","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":[0.13857324,0.001827006,0.82998705,0.00020844866,0.00038266295,0.0007462851,0.00043554796,0.018537153,0.009302631],"genre_scores_gemma":[0.44606566,0.0006207777,0.5381222,0.00027536572,0.00012874576,0.0002730802,0.0003908216,0.00015663002,0.013966854],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979764,0.000033484674,0.000011895062,0.0000646583,0.000072890754,0.000019380035],"domain_scores_gemma":[0.9996604,0.00009304484,0.00003943375,0.000048751706,0.00008686184,0.00007153085],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002617465,0.00058250484,0.00035852857,0.00035380034,0.0003060493,0.00043282044,0.00086026266,0.00050746265,0.0039515262],"category_scores_gemma":[0.0006578997,0.00017897898,0.0002761637,0.0001846611,0.0001448324,0.0004935363,0.00042814968,0.00041007693,0.001448734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008219832,0.0002692932,0.002170757,0.00057652284,0.00006216319,0.0008944331,0.00051627995,0.0011158003,0.29592422,0.001281536,0.008292932,0.6880741],"study_design_scores_gemma":[0.00069739576,0.006140453,0.03228738,0.0002898664,0.00044945118,0.013666968,0.0008505761,0.29179683,0.41791213,0.002063043,0.23351471,0.00033107062],"about_ca_topic_score_codex":0.0011738742,"about_ca_topic_score_gemma":0.0017886882,"teacher_disagreement_score":0.0039515262,"about_ca_system_score_codex":0.00017315407,"about_ca_system_score_gemma":0.00031133508,"threshold_uncertainty_score":0.013219178},"labels":[],"label_agreement":null},{"id":"W2045610461","doi":"10.1109/have.2013.6679617","title":"Particle filtering enhanced human tracking on context-aware robotic system","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Particle filter; Artificial intelligence; Context (archaeology); Computer vision; Tracking (education); Mechanism (biology); Mobile robot; Feature (linguistics); Tracking system; Object detection; Robot; Kalman filter; Pattern recognition (psychology)","score_opus":0.045133596134058096,"score_gpt":0.29196215742676773,"score_spread":0.24682856129270964,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2045610461","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.050643716,0.00034406397,0.9463079,0.00006598749,0.000072527844,0.00003361099,0.000016686763,0.0008893796,0.0016261408],"genre_scores_gemma":[0.8368486,0.00023317084,0.16080505,0.000065614615,0.00004166155,0.000048192655,0.00003035475,0.000021152366,0.0019061945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997676,0.000036560574,0.000010393809,0.000071455055,0.000084238,0.000029770232],"domain_scores_gemma":[0.99983716,0.00003931627,0.000025173382,0.000030202382,0.000055342658,0.00001281567],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025430886,0.00036059262,0.00040394877,0.00023774496,0.00028038386,0.0003790899,0.000520034,0.0005021912,0.0004928199],"category_scores_gemma":[0.0005086462,0.00021241452,0.00030677492,0.0001661381,0.00018642582,0.00049754366,0.00042210994,0.00040338386,0.00017951839],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056729594,0.0001776191,0.0034552182,0.0002218293,0.000122031124,0.00064339966,0.0003837318,0.2679297,0.26475158,0.010676839,0.0023002762,0.44877046],"study_design_scores_gemma":[0.00002154326,0.00014533139,0.0017099639,0.000007836494,0.000029468056,0.00013826405,0.000016680524,0.9718152,0.023060322,0.0011510905,0.0018853908,0.000018891345],"about_ca_topic_score_codex":0.003811873,"about_ca_topic_score_gemma":0.0027618757,"teacher_disagreement_score":0.003811873,"about_ca_system_score_codex":0.000283241,"about_ca_system_score_gemma":0.00041890788,"threshold_uncertainty_score":0.0075793862},"labels":[],"label_agreement":null},{"id":"W2046086677","doi":"10.1117/12.2053176","title":"A comparison of moving object detection methods for real-time moving object detection","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Background subtraction; Object detection; Computer vision; Artificial intelligence; Viola–Jones object detection framework; Object-class detection; Laptop; Optical flow; Face detection; Feature extraction; Pattern recognition (psychology); Facial recognition system; Pixel","score_opus":0.019130857531165148,"score_gpt":0.30429442315911065,"score_spread":0.2851635656279455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046086677","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.113432825,0.011332307,0.8630236,0.00032412875,0.00060324837,0.00026760125,0.00022535403,0.0038530522,0.006937982],"genre_scores_gemma":[0.27798536,0.0049277367,0.71110725,0.00015776194,0.00013195477,0.00017921905,0.00068144547,0.00036491186,0.004464388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99663335,0.00047951072,0.00020871538,0.00046859903,0.0020316904,0.00017802048],"domain_scores_gemma":[0.995133,0.0015165897,0.0003666128,0.000408728,0.002426304,0.00014884218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003346994,0.0009783976,0.0009087611,0.0040239915,0.00043799487,0.001100656,0.001536839,0.0013879343,0.0018982341],"category_scores_gemma":[0.007944948,0.00043446544,0.0009085654,0.0019020505,0.0004270379,0.0018919988,0.0006328492,0.0007808982,0.0009120257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007621936,0.00027900375,0.0041632755,0.000767301,0.0002924442,0.00018989496,0.00021982954,0.015686834,0.06409946,0.0019038412,0.002440213,0.9091957],"study_design_scores_gemma":[0.00013719773,0.0019429218,0.030512637,0.00032698156,0.0004170409,0.003353679,0.00048467243,0.7137792,0.21745402,0.0021323452,0.029144922,0.00031436892],"about_ca_topic_score_codex":0.003245664,"about_ca_topic_score_gemma":0.0029164057,"teacher_disagreement_score":0.0040239915,"about_ca_system_score_codex":0.00059287326,"about_ca_system_score_gemma":0.0006504469,"threshold_uncertainty_score":0.017700791},"labels":[],"label_agreement":null},{"id":"W2046204128","doi":"10.1109/iros.2014.6942942","title":"Detection of small moving objects using a moving camera","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Computer vision; Background subtraction; Computer science; Pixel; Motion compensation; Object detection; Tracking (education); Particle filter; Foreground detection; Motion estimation; Filter (signal processing); Pattern recognition (psychology)","score_opus":0.033656624890703785,"score_gpt":0.27568277653015094,"score_spread":0.24202615163944716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046204128","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":[0.074519515,0.0019467028,0.9187559,0.00007865559,0.00009216833,0.00010497714,0.000088107816,0.0020870697,0.0023268883],"genre_scores_gemma":[0.35002804,0.001528899,0.64358497,0.0001540979,0.00008932564,0.00007225813,0.00033620637,0.00014980786,0.0040563745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999374,0.0000551845,0.000019678648,0.00026038755,0.00023070157,0.000060152666],"domain_scores_gemma":[0.9996474,0.000075714954,0.00004667627,0.0000616068,0.00012331347,0.000045289664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039329802,0.0009133575,0.0011241607,0.0026454472,0.000488106,0.0008846894,0.000990473,0.0010656685,0.0010454287],"category_scores_gemma":[0.0008730619,0.0005336239,0.00058505766,0.0014487471,0.00043694396,0.0010224398,0.0007848772,0.00061792956,0.00086412847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002732666,0.00008372574,0.0022839606,0.000209085,0.00011924593,0.00049105496,0.00014107574,0.0062796706,0.4048376,0.0014905336,0.0012217946,0.58256894],"study_design_scores_gemma":[0.00005275557,0.0005024157,0.019940063,0.00006232936,0.00029795745,0.0029690524,0.00021151571,0.5426023,0.41585267,0.0020445532,0.0153377885,0.00012670233],"about_ca_topic_score_codex":0.0035573721,"about_ca_topic_score_gemma":0.004251136,"teacher_disagreement_score":0.0035573721,"about_ca_system_score_codex":0.00035617535,"about_ca_system_score_gemma":0.00045052552,"threshold_uncertainty_score":0.007073343},"labels":[],"label_agreement":null},{"id":"W2046876370","doi":"10.1109/avss.2006.83","title":"Object Contour Tracking in Videos by Matching Finite Mixture Models","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Artificial intelligence; Computer vision; Initialization; Video tracking; Tracking (education); Computer science; Object (grammar); Matching (statistics); Frame (networking); Boundary (topology); Segmentation; Active appearance model; Active contour model; Pattern recognition (psychology); Image segmentation; Mathematics; Image (mathematics)","score_opus":0.020651279800815155,"score_gpt":0.2765845236392823,"score_spread":0.25593324383846716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2046876370","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004188406,0.0001239901,0.9952669,0.000019174764,0.000011265804,0.000012676465,0.0000088731595,0.00022575658,0.00014299866],"genre_scores_gemma":[0.160438,0.0004648295,0.83712786,0.000060360137,0.000042169682,0.00008135322,0.00015025378,0.00016683238,0.0014683042],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99908674,0.00021373159,0.000047622834,0.00026177577,0.00033199543,0.00005810982],"domain_scores_gemma":[0.99874395,0.00066830416,0.00017569902,0.00016293109,0.00019549439,0.000053600208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020995103,0.00083924114,0.0012314949,0.0020572345,0.00049704453,0.0012738791,0.0018315755,0.0016414352,0.00093545933],"category_scores_gemma":[0.005400047,0.00082558824,0.0012676881,0.0017869014,0.00074682356,0.0023963582,0.0011285858,0.0012031272,0.0006629173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032171357,0.00011000168,0.0020821688,0.00016838741,0.00017005092,0.00022087846,0.0002983687,0.42991397,0.031359315,0.020017881,0.0010752921,0.5142619],"study_design_scores_gemma":[0.000007642868,0.000027741064,0.00029046097,0.000009923997,0.000015146671,0.00008100708,0.000012434679,0.9878171,0.0050678197,0.005676073,0.0009792617,0.000015337173],"about_ca_topic_score_codex":0.0035917608,"about_ca_topic_score_gemma":0.002265832,"teacher_disagreement_score":0.0035917608,"about_ca_system_score_codex":0.0008623785,"about_ca_system_score_gemma":0.00056504866,"threshold_uncertainty_score":0.011103451},"labels":[],"label_agreement":null},{"id":"W2049858397","doi":"10.1109/wacv.2014.6836011","title":"Structure-aware keypoint tracking for partial occlusion handling","year":2014,"lang":"en","type":"article","venue":"IEEE Winter Conference on Applications of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Computer vision; BitTorrent tracker; Tracking (education); Matching (statistics); Video tracking; Pattern recognition (psychology); Active appearance model; Feature (linguistics); Object (grammar); Key (lock); Feature matching; Probabilistic logic; Eye tracking; Feature extraction; Image (mathematics); Mathematics","score_opus":0.03101083606798564,"score_gpt":0.32623165869238957,"score_spread":0.2952208226244039,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049858397","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":[0.00387781,0.00011429828,0.99521106,0.000014587463,0.00001728553,0.000009724085,0.000021181242,0.00052842847,0.00020566766],"genre_scores_gemma":[0.4508686,0.00046373578,0.54541713,0.000062940104,0.000073154406,0.00008641943,0.00038704585,0.00035116123,0.0022898905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991941,0.000097079326,0.00003693652,0.00020034931,0.00039693652,0.00007464329],"domain_scores_gemma":[0.9988985,0.00031990817,0.00014259327,0.00035591333,0.00023848926,0.000044594406],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069631985,0.0008098265,0.0011819848,0.0011602946,0.00052070606,0.0008320247,0.0021186662,0.000987384,0.0013208631],"category_scores_gemma":[0.0031427506,0.0006821342,0.00086177676,0.0014630381,0.00044169484,0.0018752168,0.001566671,0.0013033361,0.00071501057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020953563,0.00006935486,0.0018579424,0.00015422226,0.00013774354,0.00020292075,0.0002353261,0.43262568,0.09039461,0.017211199,0.0033665746,0.45353487],"study_design_scores_gemma":[0.000010978111,0.000023738176,0.00030864295,0.0000041619364,0.000014125498,0.00008548967,0.000007326332,0.9816727,0.012075274,0.0037184423,0.0020687005,0.0000104289475],"about_ca_topic_score_codex":0.0044746078,"about_ca_topic_score_gemma":0.004290381,"teacher_disagreement_score":0.0044746078,"about_ca_system_score_codex":0.0007709556,"about_ca_system_score_gemma":0.00087762426,"threshold_uncertainty_score":0.008897126},"labels":[],"label_agreement":null},{"id":"W2049975772","doi":"10.1109/icip.2013.6738708","title":"Combination of thermal and color images for accurate foreground / background segmentation in outdoor environment","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Institut National d'Optique","funders":"","keywords":"Ground truth; Artificial intelligence; Computer science; Computer vision; Segmentation; Visibility; Pixel; Context (archaeology); Image segmentation; Color space; Pattern recognition (psychology); Image (mathematics); Geography","score_opus":0.033584503176889755,"score_gpt":0.29525635275815726,"score_spread":0.2616718495812675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2049975772","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12521574,0.000908903,0.86965644,0.00009269501,0.000064933236,0.000056054043,0.000075024414,0.0014482085,0.0024820033],"genre_scores_gemma":[0.50119615,0.00054474996,0.49681643,0.00006505447,0.00007653814,0.00003108476,0.00018357027,0.00021847461,0.00086793577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946755,0.00011596412,0.000022949635,0.00014970379,0.00016303298,0.00008077154],"domain_scores_gemma":[0.99950325,0.00013125195,0.00007859133,0.00008047486,0.00017308973,0.000033202785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008465225,0.0008130644,0.000755981,0.0014127648,0.0003109711,0.0010797904,0.0005715401,0.00044586783,0.00068253116],"category_scores_gemma":[0.0016305984,0.0003997396,0.00048317338,0.0008107631,0.0003041397,0.0011245892,0.0004912894,0.0004205227,0.0005854356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043548577,0.00020803511,0.0063260435,0.00029319432,0.0001745038,0.00013909698,0.00019141054,0.039621152,0.4666094,0.0013483021,0.0009247403,0.48372874],"study_design_scores_gemma":[0.000023089762,0.00031701272,0.017749963,0.000054153857,0.0002847587,0.00062786305,0.00019209209,0.65056944,0.32415026,0.0015108708,0.0044446704,0.0000757938],"about_ca_topic_score_codex":0.0011154201,"about_ca_topic_score_gemma":0.0031136377,"teacher_disagreement_score":0.0014127648,"about_ca_system_score_codex":0.0002302493,"about_ca_system_score_gemma":0.00038445953,"threshold_uncertainty_score":0.004476905},"labels":[],"label_agreement":null},{"id":"W2050268335","doi":"10.1117/12.704717","title":"Hysteresis-based selective Gaussian-mixture model for real-time background update","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Pixel; Computer vision; Clutter; Pattern recognition (psychology); Matching (statistics); Gaussian; Frame (networking); Component (thermodynamics); Overhead (engineering); Mixture model; Algorithm; Mathematics; Radar","score_opus":0.01790380269666645,"score_gpt":0.2693551051630364,"score_spread":0.25145130246636993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2050268335","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":[0.007589342,0.00020720965,0.9910507,0.000033099866,0.000021161482,0.000013928065,0.000018837483,0.00066824496,0.00039754994],"genre_scores_gemma":[0.46607688,0.00071190024,0.52723354,0.0001905859,0.00007272831,0.00010176103,0.00033450028,0.00034239568,0.0049357554],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995426,0.00006878079,0.000017552346,0.00010161276,0.00020519078,0.00006436683],"domain_scores_gemma":[0.9996748,0.00008799679,0.000036057336,0.00005840133,0.00011789182,0.000024859246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056551467,0.0005957857,0.00072129746,0.0008082472,0.00028354875,0.0007229976,0.0020195895,0.00067581673,0.0011654162],"category_scores_gemma":[0.0011577366,0.0003949318,0.000715015,0.0009409102,0.00043001262,0.0012463809,0.0008357466,0.00087625266,0.0006105583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046805062,0.00016051387,0.0023008399,0.00014015016,0.0001518783,0.00015306484,0.00022330096,0.30795074,0.06668805,0.013900336,0.0027663477,0.6050967],"study_design_scores_gemma":[0.0000071136037,0.00002529625,0.00027976418,0.000002738767,0.000014471935,0.000045584653,0.0000069512234,0.9904178,0.007118813,0.0008867152,0.0011847592,0.000009930255],"about_ca_topic_score_codex":0.006931479,"about_ca_topic_score_gemma":0.006540972,"teacher_disagreement_score":0.006931479,"about_ca_system_score_codex":0.00065250957,"about_ca_system_score_gemma":0.00075330905,"threshold_uncertainty_score":0.013782263},"labels":[],"label_agreement":null},{"id":"W2051367632","doi":"10.3166/ria.27.65-93","title":"Coopération entre perception déportée et embarquée sur un robot guide pour l’aide à sa navigation","year":2013,"lang":"fr","type":"article","venue":"Revue d intelligence artificielle","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Humanities; Computer science; Port (circuit theory); Engineering; Art; Electrical engineering","score_opus":0.07761499089720843,"score_gpt":0.31963408443662794,"score_spread":0.24201909353941953,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2051367632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0835646,0.00043873768,0.9097297,0.00014840513,0.00004335338,0.00007852911,0.0000286249,0.0008872305,0.005080768],"genre_scores_gemma":[0.67175233,0.0004878447,0.3141663,0.00010877099,0.000027858374,0.00011002366,0.00010810067,0.00009732833,0.013141398],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992644,0.00016103401,0.00002631168,0.00021496577,0.00024723442,0.00008608365],"domain_scores_gemma":[0.99884194,0.0004812464,0.00013561283,0.00014287677,0.00031701525,0.000081341896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086743943,0.0009635253,0.0006135248,0.00049486756,0.0005891139,0.0009816604,0.00069801486,0.0008972945,0.0036838797],"category_scores_gemma":[0.001734331,0.00047436226,0.0004963344,0.00034100542,0.0005582479,0.0012042806,0.001112362,0.00067958544,0.0010234963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006843197,0.0002730203,0.00594617,0.00042043073,0.00014929428,0.00067568506,0.0020438186,0.13648303,0.3422024,0.008758975,0.0019575178,0.5004054],"study_design_scores_gemma":[0.000057518868,0.001260004,0.01283679,0.00013126119,0.00019043774,0.0011211219,0.001263222,0.76311547,0.18424295,0.0079324385,0.027713574,0.00013522564],"about_ca_topic_score_codex":0.005769703,"about_ca_topic_score_gemma":0.0055479254,"teacher_disagreement_score":0.005769703,"about_ca_system_score_codex":0.000504981,"about_ca_system_score_gemma":0.001067799,"threshold_uncertainty_score":0.012323737},"labels":[],"label_agreement":null},{"id":"W2052320263","doi":"10.1109/glocom.2014.7036997","title":"Extending the detection range of vision-based driver assistance systems application to Pedestrian Protection System","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Pedestrian; Pedestrian detection; Computer science; Advanced driver assistance systems; Architecture; Scale (ratio); Range (aeronautics); Artificial intelligence; Computer vision; Real-time computing; Transport engineering; Geography; Engineering; Cartography","score_opus":0.01655767880427574,"score_gpt":0.26963949553141064,"score_spread":0.2530818167271349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052320263","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36052898,0.0021851794,0.622406,0.00033615704,0.00019151531,0.00013904458,0.0001267095,0.0052838903,0.0088024745],"genre_scores_gemma":[0.92278606,0.0003565926,0.07513145,0.000092514645,0.000033999233,0.000024126226,0.00007417204,0.000021425427,0.0014796931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997578,0.00003200712,0.000010587219,0.0000759318,0.00007565922,0.000047935147],"domain_scores_gemma":[0.9995771,0.00007843135,0.00003169826,0.000048843693,0.00022860325,0.00003536628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029517233,0.00043093917,0.00036530453,0.0006432281,0.00024037503,0.00047547385,0.0005528587,0.00045815544,0.0012685203],"category_scores_gemma":[0.0007183549,0.00024872026,0.00028114437,0.0002922208,0.00019195258,0.0005063019,0.0004983983,0.00033029722,0.00051476446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006301716,0.00026806517,0.011606901,0.00028784695,0.00009172261,0.00046315716,0.00023622546,0.05369842,0.3148911,0.0026250652,0.003942842,0.61125857],"study_design_scores_gemma":[0.00004272452,0.0006331669,0.014229276,0.00004044934,0.000103173945,0.0007703513,0.00009738836,0.84825623,0.12543122,0.0015229192,0.0088176625,0.00005539958],"about_ca_topic_score_codex":0.0048380713,"about_ca_topic_score_gemma":0.004428067,"teacher_disagreement_score":0.0048380713,"about_ca_system_score_codex":0.00046602442,"about_ca_system_score_gemma":0.0004970017,"threshold_uncertainty_score":0.009619832},"labels":[],"label_agreement":null},{"id":"W2052375315","doi":"10.1109/avss.2013.6636607","title":"Meta-tracking for video scene understanding","year":2013,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Computer vision; Video tracking; Tracking (education); Artificial intelligence; Computer graphics (images); Video processing","score_opus":0.38166127103300035,"score_gpt":0.37006800172944165,"score_spread":0.011593269303558695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052375315","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":[0.0015356564,0.00025958265,0.9967572,0.000037729722,0.00002114166,0.00001788792,0.00009295229,0.00077901024,0.00049885956],"genre_scores_gemma":[0.123428404,0.0011932603,0.8714974,0.00009964673,0.00009780544,0.00014250245,0.0009300634,0.00044824928,0.0021626663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996586,0.00005160337,0.00002044552,0.00012209886,0.00011093125,0.00003631152],"domain_scores_gemma":[0.99944943,0.00018282451,0.00007686436,0.0001657127,0.00009533774,0.000029807708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054545986,0.0010534824,0.00096413924,0.0022813906,0.00040480244,0.0015518944,0.0012656178,0.0011415326,0.0028303002],"category_scores_gemma":[0.0017617439,0.00066584285,0.0015894187,0.0016562287,0.0005650228,0.0020994863,0.0013588526,0.0012585429,0.001184083],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025901385,0.00007608227,0.0012851025,0.0004001,0.00024501406,0.00029857166,0.00030821303,0.21910651,0.057892725,0.05058363,0.0063864565,0.66315866],"study_design_scores_gemma":[0.000010697149,0.000027631744,0.00052877143,0.000036216436,0.000029826337,0.00013178283,0.00004224523,0.95164263,0.010703584,0.02871351,0.008111939,0.00002115377],"about_ca_topic_score_codex":0.003335997,"about_ca_topic_score_gemma":0.0034130157,"teacher_disagreement_score":0.003335997,"about_ca_system_score_codex":0.0008241801,"about_ca_system_score_gemma":0.0006089325,"threshold_uncertainty_score":0.009468257},"labels":[],"label_agreement":null},{"id":"W2052524720","doi":"10.1109/cvprw.2012.6238919","title":"Changedetection.net: A new change detection benchmark dataset","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":814,"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; National Science Foundation","keywords":"Computer science; Benchmarking; Benchmark (surveying); Change detection; Artificial intelligence; Ranking (information retrieval); Ground truth; Frame (networking); Shadow (psychology); Machine learning; Data mining; Information retrieval","score_opus":0.09362766237507576,"score_gpt":0.32002408910115687,"score_spread":0.22639642672608112,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052524720","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08732125,0.003512428,0.030533066,0.0012958609,0.0012845065,0.0017541718,0.8355939,0.02403829,0.014666603],"genre_scores_gemma":[0.02522989,0.0003371966,0.021250023,0.00017558789,0.00008748758,0.00058648677,0.949928,0.0004190143,0.0019862885],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976357,0.00027147317,0.00031669918,0.00065735215,0.00088133395,0.00023739654],"domain_scores_gemma":[0.9967713,0.000564692,0.00038344145,0.0008912994,0.0010627769,0.00032653456],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018169363,0.002569262,0.0013688529,0.0057437564,0.001331951,0.0020725727,0.0044900337,0.0026000931,0.003850404],"category_scores_gemma":[0.0054389294,0.00052985206,0.0016345957,0.0053920834,0.00078540616,0.0023334625,0.0020304574,0.0022712855,0.0051182825],"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":[0.0013055562,0.0014101277,0.017412646,0.0022015162,0.000426417,0.00066906144,0.0002134368,0.024717662,0.013168246,0.0030520172,0.7610869,0.17433661],"study_design_scores_gemma":[0.0009619825,0.001060114,0.10648755,0.0004755283,0.00034900586,0.0029427037,0.00068136235,0.18273945,0.03482057,0.0055712163,0.66348577,0.00042473673],"about_ca_topic_score_codex":0.024530942,"about_ca_topic_score_gemma":0.04013817,"teacher_disagreement_score":0.024530942,"about_ca_system_score_codex":0.0019631344,"about_ca_system_score_gemma":0.0015223635,"threshold_uncertainty_score":0.04877633},"labels":[],"label_agreement":null},{"id":"W2052751181","doi":"10.1109/icmew.2012.90","title":"Motion Segmentation Based on 3D Histogram and Temporal Mode Selection","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Histogram; Computer science; Artificial intelligence; Segmentation; Pattern recognition (psychology); Computer vision; Image segmentation; Selection (genetic algorithm); Mode (computer interface); Motion estimation; Process (computing); Motion (physics); Image (mathematics)","score_opus":0.028025916885404883,"score_gpt":0.3104100020649499,"score_spread":0.282384085179545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2052751181","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":[0.026057346,0.0003034033,0.97110885,0.000037298378,0.000038580907,0.00005260732,0.0001047988,0.0011808438,0.0011163006],"genre_scores_gemma":[0.3083897,0.0006511963,0.6878818,0.00007100383,0.00007735013,0.000118375436,0.00048690877,0.00026029637,0.00206334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996793,0.00003945647,0.000017650467,0.00007429155,0.00015165999,0.000037570284],"domain_scores_gemma":[0.9996513,0.00009048967,0.000055684348,0.00003923578,0.00013927801,0.000024051807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025724864,0.0005125722,0.0005148545,0.0021726287,0.00024886304,0.00056016614,0.0005863212,0.00036357224,0.0013682384],"category_scores_gemma":[0.00082281313,0.0002787093,0.0005502279,0.0013197523,0.0002838495,0.0007545868,0.0004411604,0.0002709843,0.0006124945],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025411462,0.00006601577,0.0026367013,0.00015423463,0.000053492615,0.00011149638,0.00013602996,0.020372538,0.22442432,0.0027261765,0.0016726874,0.74739224],"study_design_scores_gemma":[0.000033370052,0.00016897281,0.012790561,0.00003147795,0.00007126804,0.0009029962,0.000118321106,0.8127603,0.16103995,0.0036415344,0.008342697,0.00009855053],"about_ca_topic_score_codex":0.0023155182,"about_ca_topic_score_gemma":0.0030713768,"teacher_disagreement_score":0.0023155182,"about_ca_system_score_codex":0.00031179877,"about_ca_system_score_gemma":0.0003789407,"threshold_uncertainty_score":0.004604101},"labels":[],"label_agreement":null},{"id":"W2053268880","doi":"10.1109/iros.2010.5650252","title":"A stereo camera based full body human motion capture system using a partitioned particle filter","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Motion capture; Particle filter; Tracking (education); Software portability; Monocular; Tracking system; Ground truth; Filter (signal processing); Motion (physics)","score_opus":0.035538863287239764,"score_gpt":0.29717341846956746,"score_spread":0.2616345551823277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053268880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021307595,0.00015288126,0.9743096,0.00007235869,0.000064027554,0.00015523534,0.00031742084,0.0016105494,0.0020103003],"genre_scores_gemma":[0.23835354,0.0002716319,0.75444794,0.00032168685,0.000067611814,0.00028029183,0.0008849191,0.000087549444,0.0052847886],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972194,0.000035866185,0.000009868838,0.00009185536,0.00011814038,0.000022318212],"domain_scores_gemma":[0.9997917,0.000040050836,0.000021022304,0.00004051533,0.000081957325,0.000024766076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034124998,0.00037889625,0.0006047002,0.0004900682,0.00027892066,0.00044325503,0.00072856824,0.00067146594,0.0027471466],"category_scores_gemma":[0.0004604007,0.00030799254,0.00031016782,0.0004473498,0.00015251174,0.0004546831,0.00049537304,0.0004530324,0.0011839613],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055033393,0.00035725805,0.004046691,0.00035880238,0.0001803401,0.00040392973,0.00027620955,0.029194789,0.32230985,0.0031800242,0.010543828,0.628598],"study_design_scores_gemma":[0.00019933602,0.0006183372,0.019385794,0.00005501579,0.00012549732,0.0012841822,0.000079577345,0.87498045,0.08034154,0.0014728573,0.021332297,0.00012521184],"about_ca_topic_score_codex":0.004266275,"about_ca_topic_score_gemma":0.007049115,"teacher_disagreement_score":0.004266275,"about_ca_system_score_codex":0.0003014751,"about_ca_system_score_gemma":0.0006276279,"threshold_uncertainty_score":0.009190142},"labels":[],"label_agreement":null},{"id":"W2053287050","doi":"10.1155/2009/797052","title":"Spatiotemporal Region Enhancement and Merging for Unsupervized Object Segmentation","year":2009,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal; Concordia University","funders":"","keywords":"Segmentation; Object (grammar); Artificial intelligence; Pattern recognition (psychology); Computer science; Computer vision","score_opus":0.0328918791170396,"score_gpt":0.33267812769307886,"score_spread":0.29978624857603925,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053287050","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020447323,0.0002398202,0.9770725,0.0000351846,0.000022122693,0.00005611421,0.000046310146,0.0010664227,0.0010141028],"genre_scores_gemma":[0.11351025,0.00026003708,0.8831006,0.000049551763,0.00004337168,0.000065139284,0.00023730536,0.0002989055,0.0024348064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995103,0.00006376752,0.00003544198,0.00012780906,0.00021812222,0.000044525816],"domain_scores_gemma":[0.9994553,0.00013922957,0.000091529124,0.0001467884,0.0001401632,0.000027142145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004921582,0.0005274048,0.0004829494,0.0011334983,0.00034937184,0.0007244235,0.0012635924,0.00045633613,0.0019897143],"category_scores_gemma":[0.0012278529,0.00036897635,0.00063308555,0.0008365633,0.00037984506,0.0012254057,0.000645985,0.00042274257,0.00078113825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035521403,0.000104469786,0.0013509452,0.00014740248,0.000064155814,0.00025242096,0.00031635116,0.018161159,0.30210757,0.004321705,0.0016585065,0.6711602],"study_design_scores_gemma":[0.000029510318,0.0002721086,0.005620307,0.000024577912,0.00010260368,0.0012173739,0.000101666556,0.54874307,0.42389435,0.002506916,0.017427934,0.000059630845],"about_ca_topic_score_codex":0.0029064296,"about_ca_topic_score_gemma":0.004241555,"teacher_disagreement_score":0.0029064296,"about_ca_system_score_codex":0.00047541928,"about_ca_system_score_gemma":0.0006710482,"threshold_uncertainty_score":0.006656289},"labels":[],"label_agreement":null},{"id":"W2053714435","doi":"10.1109/ccece.2008.4564764","title":"Cooperative hybrid multi-camera tracking for people surveillance","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Camera auto-calibration; Zoom; Computer science; Artificial intelligence; Tracking (education); Event (particle physics); Smart camera; Tracking system; Field of view; Camera resectioning; Multi camera; Image resolution; Kalman filter; Engineering","score_opus":0.03671067253139077,"score_gpt":0.24260458452195482,"score_spread":0.20589391199056406,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2053714435","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041601077,0.0006678872,0.9551952,0.00006340908,0.000050418257,0.000041943793,0.000028645849,0.0007951131,0.0015563618],"genre_scores_gemma":[0.82039356,0.00041959478,0.17630698,0.00007436161,0.00005766769,0.00009333799,0.00009628552,0.000035918758,0.002522303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929094,0.00018726841,0.00002230242,0.00020435924,0.00022214912,0.00007290184],"domain_scores_gemma":[0.9994142,0.00023267562,0.00009287124,0.00009071192,0.00012648798,0.000043013704],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087840477,0.0006258985,0.0007986272,0.0008807267,0.00039045923,0.0006498509,0.0009646156,0.00080397597,0.00080615276],"category_scores_gemma":[0.0012125433,0.00039117996,0.00058128807,0.00073136506,0.0002613363,0.00093671866,0.00095023285,0.0004569207,0.00030734812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000692264,0.00028853244,0.0060455846,0.00028984854,0.0003029915,0.00049409794,0.000501642,0.26461017,0.10421501,0.0042909496,0.0026535548,0.61561525],"study_design_scores_gemma":[0.00002386603,0.0001535691,0.0019459587,0.0000083489,0.00003787478,0.00013562845,0.00003506558,0.98659873,0.00844353,0.001105037,0.001493383,0.000018949828],"about_ca_topic_score_codex":0.0037293155,"about_ca_topic_score_gemma":0.0035496019,"teacher_disagreement_score":0.0037293155,"about_ca_system_score_codex":0.00038571152,"about_ca_system_score_gemma":0.00036708245,"threshold_uncertainty_score":0.007415235},"labels":[],"label_agreement":null},{"id":"W2054804191","doi":"10.1109/iros.2010.5654417","title":"Target tracking for moving robots using object-based visual attention","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Tracking (education); Robot; Video tracking; Eye tracking; Object (grammar); Segmentation; Object detection; Psychology","score_opus":0.038616496325488935,"score_gpt":0.34506671002870265,"score_spread":0.30645021370321374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054804191","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025666965,0.00032389845,0.9718902,0.000049206978,0.00002536734,0.000022705159,0.000008669236,0.0005665258,0.0014464279],"genre_scores_gemma":[0.5116055,0.00052179175,0.48505843,0.00014547925,0.00006747791,0.000058384332,0.000058104608,0.000095054966,0.0023896908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998684,0.00001699587,0.00000434127,0.000055425626,0.000039293358,0.000015542846],"domain_scores_gemma":[0.9998023,0.00007246595,0.000036663834,0.00003310413,0.00004091215,0.000014621438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003282843,0.00034475012,0.00034338952,0.00044029893,0.00024409035,0.0003881262,0.00082359486,0.00046439492,0.0005837668],"category_scores_gemma":[0.00078599615,0.00022068126,0.00039550185,0.00027748678,0.00036085502,0.000616294,0.0005407737,0.00037764947,0.00017618753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013151349,0.000106817686,0.0012816528,0.00012477492,0.00009215024,0.0001397029,0.0002063234,0.08587972,0.32376865,0.007600417,0.0010590818,0.57960916],"study_design_scores_gemma":[0.000027523354,0.00017821915,0.0035986518,0.000015095311,0.000054738975,0.00024173858,0.000025445866,0.92163455,0.063861854,0.007190069,0.0031397848,0.000032241773],"about_ca_topic_score_codex":0.0021082468,"about_ca_topic_score_gemma":0.00276529,"teacher_disagreement_score":0.0021082468,"about_ca_system_score_codex":0.0005016202,"about_ca_system_score_gemma":0.00034688483,"threshold_uncertainty_score":0.0041919947},"labels":[],"label_agreement":null},{"id":"W2054902502","doi":"10.1155/2014/757845","title":"From Smart Camera to SmartHub: Embracing Cloud for Video Surveillance","year":2014,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Scalability; Cloud computing; Smart camera; Architecture; Real-time computing; Bandwidth (computing); Artificial intelligence; Database; Computer network; Operating system","score_opus":0.014284331686759442,"score_gpt":0.28379490901147386,"score_spread":0.2695105773247144,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2054902502","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0904761,0.0022964021,0.8936147,0.0019039421,0.00019955015,0.00026570717,0.00015418834,0.0031433715,0.007946032],"genre_scores_gemma":[0.75322473,0.0008537754,0.24241434,0.0005629055,0.00009499402,0.00007069091,0.00017686513,0.000165128,0.002436613],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948585,0.00009450971,0.00001701961,0.00013681024,0.00013683268,0.00012897907],"domain_scores_gemma":[0.9994842,0.00008571637,0.000055830133,0.00013751532,0.00009552199,0.00014109736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051069143,0.00033568984,0.0005571688,0.00036524463,0.00081709377,0.0013434713,0.0011515378,0.00070314005,0.0015178748],"category_scores_gemma":[0.0007411544,0.00028136157,0.00031371243,0.0007054863,0.0007582512,0.002772041,0.0016750061,0.000887835,0.00038142223],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018823117,0.0006112766,0.009464907,0.00059476297,0.00020240348,0.0019354416,0.0012775627,0.0715431,0.2375969,0.100178406,0.034069937,0.5406429],"study_design_scores_gemma":[0.00013061149,0.00029651227,0.0032856667,0.000086919266,0.000056269833,0.0010800121,0.00065571663,0.82229036,0.09320925,0.02755477,0.051260713,0.00009323467],"about_ca_topic_score_codex":0.0050341305,"about_ca_topic_score_gemma":0.004769008,"teacher_disagreement_score":0.0050341305,"about_ca_system_score_codex":0.00082249095,"about_ca_system_score_gemma":0.00080320064,"threshold_uncertainty_score":0.010009646},"labels":[],"label_agreement":null},{"id":"W2055480697","doi":"10.1007/s00138-011-0342-z","title":"Pedestrian tracking using color, thermal and location cue measurements: a DSmT-based framework","year":2011,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bishop's University","funders":"","keywords":"Clutter; Robustness (evolution); Computer vision; Computer science; Artificial intelligence; Particle filter; Tracking (education); Pedestrian; Frame (networking); Filter (signal processing); Radar; Engineering","score_opus":0.10047990648105044,"score_gpt":0.3476925107248356,"score_spread":0.24721260424378516,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2055480697","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013672064,0.00021630118,0.98433334,0.00006915293,0.00006890797,0.00003524775,0.00013651418,0.0005040836,0.00096443034],"genre_scores_gemma":[0.3574074,0.00047297726,0.63732827,0.00012587331,0.00016995157,0.00011690987,0.0009069215,0.00013116743,0.003340641],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995896,0.00006501934,0.000018165063,0.00010660441,0.00015938425,0.00006119331],"domain_scores_gemma":[0.9996178,0.00005008077,0.00004200273,0.0000648883,0.00018295232,0.000042288142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056942354,0.00082804804,0.0015622675,0.0015532388,0.00053170347,0.0010848381,0.0015970584,0.0010436947,0.0011029468],"category_scores_gemma":[0.0010956706,0.0005867886,0.0012055322,0.0017194476,0.00046105045,0.0007511496,0.0010536761,0.0007497936,0.0008608457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006021746,0.00037148732,0.005003337,0.00022590563,0.00028128971,0.00023575054,0.00012949505,0.30076298,0.07405364,0.009853664,0.004365385,0.6041149],"study_design_scores_gemma":[0.000009895925,0.00003568547,0.00078272657,0.0000055168275,0.00002928394,0.000065043525,0.0000095451405,0.9928318,0.0039193756,0.0015514999,0.00074639695,0.000013137344],"about_ca_topic_score_codex":0.007862864,"about_ca_topic_score_gemma":0.010039072,"teacher_disagreement_score":0.007862864,"about_ca_system_score_codex":0.0005246123,"about_ca_system_score_gemma":0.0012696504,"threshold_uncertainty_score":0.01563418},"labels":[],"label_agreement":null},{"id":"W2056028064","doi":"10.1109/icma.2010.5588232","title":"Moving shadow detection based on normalized eigenvalue of Wishart matrix","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer vision; Artificial intelligence; Shadow (psychology); Eigenvalues and eigenvectors; Pixel; Computer science; Object detection; Wishart distribution; Invariant (physics); Image (mathematics); Pattern recognition (psychology); Mathematics","score_opus":0.012698274764258409,"score_gpt":0.28723406355930275,"score_spread":0.27453578879504437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056028064","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032893773,0.00024297918,0.96545815,0.000042892632,0.000022701033,0.000016972155,0.000033602755,0.00036798528,0.0009208534],"genre_scores_gemma":[0.6179777,0.00072348875,0.3789153,0.00007204187,0.00010333673,0.000059098442,0.0002232397,0.00012811497,0.0017977176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992761,0.00015908314,0.000028767181,0.00015418278,0.00032203842,0.0000599917],"domain_scores_gemma":[0.9986504,0.0005746854,0.0001675555,0.00016898967,0.0003701843,0.000068241934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057356525,0.0004429813,0.0005736338,0.0016687124,0.00030050287,0.0008412412,0.00052254024,0.00037725165,0.00095242227],"category_scores_gemma":[0.0029667786,0.00027377883,0.00047395576,0.0010741154,0.0008367036,0.0012697786,0.0005340615,0.0004972547,0.00040800616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005670948,0.00014120266,0.008699468,0.00033516987,0.0001903549,0.00074835593,0.00045067575,0.13986792,0.2813779,0.04994174,0.002756233,0.5149239],"study_design_scores_gemma":[0.000010162408,0.000066917346,0.0034756863,0.000010593528,0.000020251999,0.00051404774,0.000042101692,0.9563254,0.028489795,0.009877406,0.0011132903,0.00005434591],"about_ca_topic_score_codex":0.0017386482,"about_ca_topic_score_gemma":0.0014135905,"teacher_disagreement_score":0.0017386482,"about_ca_system_score_codex":0.00036137606,"about_ca_system_score_gemma":0.00043688065,"threshold_uncertainty_score":0.0034570694},"labels":[],"label_agreement":null},{"id":"W2056595270","doi":"10.1117/12.2074960","title":"Localizing people in crosswalks with a moving handheld camera: proof of concept","year":2015,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","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":"Computer Research Institute of Montréal","funders":"","keywords":"Mobile device; Computer science; Computer vision; Computer graphics (images); Proof of concept; Artificial intelligence; World Wide Web","score_opus":0.018461265036608495,"score_gpt":0.25441387606740695,"score_spread":0.23595261103079845,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2056595270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3437034,0.0011032451,0.64010435,0.0006190566,0.0005498646,0.0015341872,0.0004339648,0.004428497,0.0075234747],"genre_scores_gemma":[0.6535167,0.0005208535,0.3384254,0.0003877306,0.00006849298,0.0006447204,0.00025884798,0.00011761469,0.006059641],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954045,0.00005608688,0.0000128905995,0.00013747167,0.00018770053,0.00006552226],"domain_scores_gemma":[0.9994671,0.00009459171,0.000060176055,0.00007693245,0.00017282699,0.00012841697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006340989,0.00076792366,0.0006160855,0.000370118,0.00025543323,0.00059278484,0.0018051484,0.0011392958,0.0026305076],"category_scores_gemma":[0.00078020204,0.00035164287,0.00036686345,0.00013247885,0.00043003596,0.0009666014,0.00083228,0.00085119164,0.00088222756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010313308,0.0013590197,0.0031613314,0.0008124207,0.00017397359,0.0012605105,0.0005182393,0.007932064,0.71373034,0.0014774834,0.0057209637,0.26282242],"study_design_scores_gemma":[0.0011170458,0.014161901,0.023082422,0.00024814942,0.00027297714,0.0057160566,0.0007897552,0.21973188,0.7012948,0.0011714223,0.032094985,0.0003186089],"about_ca_topic_score_codex":0.0025016111,"about_ca_topic_score_gemma":0.002125755,"teacher_disagreement_score":0.0026305076,"about_ca_system_score_codex":0.00034202661,"about_ca_system_score_gemma":0.0005507854,"threshold_uncertainty_score":0.008799911},"labels":[],"label_agreement":null},{"id":"W2057319225","doi":"10.1109/cvprw.2013.122","title":"Target Trajectory Prediction in PTZ Camera Networks","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Probabilistic logic; Computer science; Trajectory; Artificial intelligence; Position (finance); Computer vision; Machine learning","score_opus":0.01321427216894542,"score_gpt":0.2399902632523855,"score_spread":0.2267759910834401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2057319225","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17539556,0.00058429287,0.8193802,0.0002805051,0.000047944486,0.000058098096,0.00018309371,0.0008731898,0.0031971098],"genre_scores_gemma":[0.96578795,0.0002544997,0.032262467,0.000034233795,0.000017419088,0.000039222152,0.00016618407,0.0000349093,0.0014030683],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999321,0.00014861916,0.000026641881,0.00019101615,0.00020288507,0.0001098712],"domain_scores_gemma":[0.99798405,0.0010548253,0.00033766736,0.00020862526,0.00031954187,0.00009535124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010846949,0.00078933855,0.00080801354,0.0005870577,0.00044334857,0.00070498453,0.001114356,0.0008815583,0.0011833546],"category_scores_gemma":[0.0062261056,0.00038710082,0.0003355321,0.00066290237,0.00052320433,0.0022879425,0.0011576347,0.0007403204,0.00031579708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028519472,0.000016909584,0.0029529675,0.000029353376,0.000020419388,0.000063976855,0.00004284782,0.95488846,0.0016605391,0.0014511388,0.00037033352,0.038217835],"study_design_scores_gemma":[0.0000030727447,0.000027266415,0.0005059478,0.0000027018245,0.0000034125871,0.000018845403,0.000009781484,0.9978257,0.0008475933,0.00064714765,0.00010548358,0.0000029888108],"about_ca_topic_score_codex":0.014797582,"about_ca_topic_score_gemma":0.0077732513,"teacher_disagreement_score":0.014797582,"about_ca_system_score_codex":0.0011408998,"about_ca_system_score_gemma":0.000706877,"threshold_uncertainty_score":0.02942288},"labels":[],"label_agreement":null},{"id":"W2059022476","doi":"10.1016/j.cviu.2011.06.006","title":"Multi-camera active surveillance of an articulated human form – An implementation strategy","year":2011,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"","keywords":"Visibility; Viewpoints; Control reconfiguration; Computer science; Artificial intelligence; Computer vision; Active vision; Real-time computing; Embedded system","score_opus":0.13736760693389757,"score_gpt":0.3850655324303814,"score_spread":0.2476979254964838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2059022476","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":[0.014768722,0.0000655178,0.9822932,0.00007042743,0.000012065408,0.00007762896,0.000008526503,0.00017060181,0.0025332896],"genre_scores_gemma":[0.33888677,0.00015761105,0.6538741,0.00007653256,0.000042282874,0.00021064519,0.00005947172,0.000046256187,0.0066463635],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935573,0.00016084504,0.000024183852,0.00018614273,0.0001976278,0.0000754831],"domain_scores_gemma":[0.99945205,0.000100602054,0.00005138745,0.00013807148,0.00021071245,0.000047129564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088208215,0.00084452407,0.00069698546,0.0006186821,0.0005353491,0.0010253164,0.0013556354,0.0010690225,0.002237556],"category_scores_gemma":[0.001048793,0.00049930415,0.00059732,0.00044503386,0.0005195717,0.001285412,0.0015229661,0.0007500788,0.0006341346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005335975,0.00054267514,0.0019516352,0.00015063395,0.00012402632,0.00019697381,0.00035215405,0.048782453,0.20449561,0.05190039,0.001563755,0.68940604],"study_design_scores_gemma":[0.000062971594,0.0005934832,0.0015151678,0.000023977871,0.000080508406,0.0003019513,0.00012708946,0.9194509,0.06388683,0.008493042,0.00543395,0.000030138126],"about_ca_topic_score_codex":0.0014049386,"about_ca_topic_score_gemma":0.0019041852,"teacher_disagreement_score":0.002237556,"about_ca_system_score_codex":0.00051427796,"about_ca_system_score_gemma":0.0009353515,"threshold_uncertainty_score":0.0074853897},"labels":[],"label_agreement":null},{"id":"W2060184076","doi":"10.1109/have.2007.4371591","title":"Virtual Reality-Based Interface for the Control of Multiple Surveillance Cameras","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Virtual reality; Interface (matter); Scheme (mathematics); Computer vision; Graphical user interface; User interface; Artificial intelligence; Virtual machine; Computer graphics (images)","score_opus":0.034180419781129595,"score_gpt":0.3258682121019664,"score_spread":0.29168779232083686,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060184076","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009338846,0.0001283808,0.98217785,0.00007379147,0.00007412353,0.000104227925,0.000072758165,0.0046383794,0.0033917476],"genre_scores_gemma":[0.43224126,0.0003326057,0.55182827,0.0003647786,0.00008591903,0.00054606044,0.000432088,0.00047042023,0.013698665],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999443,0.0001774716,0.000035028213,0.00009236973,0.00021071798,0.000041345687],"domain_scores_gemma":[0.9992415,0.00029532544,0.000065234286,0.00014386549,0.00016408705,0.00008989222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060156896,0.0007392763,0.00037794674,0.00040014333,0.00018489595,0.0008763866,0.0012691529,0.0008449712,0.010877818],"category_scores_gemma":[0.0022835613,0.00019122643,0.00044752017,0.00015886099,0.00039733126,0.00075373414,0.0011034876,0.0006268026,0.0016200937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021986545,0.0005584441,0.0013903824,0.000582982,0.00011235251,0.00092402217,0.0014553403,0.027717449,0.37151986,0.04227335,0.027094303,0.5241727],"study_design_scores_gemma":[0.0004036124,0.0019497318,0.0040223044,0.00018851053,0.00016114046,0.0027756493,0.00022510628,0.71368384,0.14389892,0.011604642,0.12085238,0.0002341628],"about_ca_topic_score_codex":0.00067567086,"about_ca_topic_score_gemma":0.0006353959,"teacher_disagreement_score":0.010877818,"about_ca_system_score_codex":0.00019387198,"about_ca_system_score_gemma":0.00020957892,"threshold_uncertainty_score":0.036389947},"labels":[],"label_agreement":null},{"id":"W2060542827","doi":"10.1142/s021848851100712x","title":"COVARIANCE TRACKING WITH FORGETTING FACTOR AND RANDOM SAMPLING","year":2011,"lang":"en","type":"article","venue":"International Journal of Uncertainty Fuzziness and Knowledge-Based Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Science Fund for Distinguished Young Scholars; National Natural Science Foundation of China","keywords":"Covariance intersection; Covariance; Robustness (evolution); Clutter; Covariance function; Computer science; Algorithm; Matérn covariance function; Forgetting; Artificial intelligence; Mathematics; Covariance matrix; Statistics; Radar","score_opus":0.0700608169165269,"score_gpt":0.3063752585402307,"score_spread":0.2363144416237038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2060542827","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018224396,0.00014832863,0.99754626,0.000017575388,0.000027486803,0.000012184267,0.0000070232963,0.00021115632,0.0002075639],"genre_scores_gemma":[0.23457673,0.00074573775,0.7613109,0.00015716132,0.00017377452,0.00015259685,0.00017500472,0.00018138684,0.0025267953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99814594,0.00032619687,0.00010041139,0.0004743573,0.00083713356,0.0001159623],"domain_scores_gemma":[0.99761814,0.0010247944,0.00024457296,0.00042617976,0.00062632683,0.000059802842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002127851,0.0010087932,0.0014088409,0.0011099259,0.0005171006,0.00086982356,0.0014000462,0.0011822754,0.0009366411],"category_scores_gemma":[0.010017498,0.0005382008,0.0013707052,0.0015175489,0.0008805778,0.0021802753,0.0009818111,0.0012372278,0.0004963623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035833215,0.00010296891,0.0021344093,0.0002693753,0.0002133596,0.00018412106,0.00018760718,0.27817214,0.023229875,0.048297048,0.002514662,0.6443361],"study_design_scores_gemma":[0.000030175186,0.000114293194,0.00065733254,0.000016479256,0.000041418458,0.00019034599,0.000009859103,0.97484684,0.011247559,0.009536895,0.003263872,0.0000448673],"about_ca_topic_score_codex":0.0045200177,"about_ca_topic_score_gemma":0.0031471173,"teacher_disagreement_score":0.0045200177,"about_ca_system_score_codex":0.00071814784,"about_ca_system_score_gemma":0.0009496816,"threshold_uncertainty_score":0.011253297},"labels":[],"label_agreement":null},{"id":"W2061304929","doi":"10.1109/tits.2012.2228640","title":"Automated Real-Time Detection of Potentially Suspicious Behavior in Public Transport Areas","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Event (particle physics); Public security; Artificial intelligence; Video tracking; Computer vision; Geolocation; Object detection; Tracking (education); Matching (statistics); Public transport; Cognitive neuroscience of visual object recognition; Object (grammar); Fainting; Real-time computing; Data mining; Computer security; Pattern recognition (psychology); Engineering","score_opus":0.026273745642701303,"score_gpt":0.2716540302220286,"score_spread":0.2453802845793273,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2061304929","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":[0.87785304,0.00026124023,0.11760965,0.00008277102,0.000023683036,0.00007641494,0.00042684283,0.0023520347,0.0013144226],"genre_scores_gemma":[0.97190195,0.00009277383,0.027135784,0.000011527422,0.0000098154005,0.000018187626,0.00041951676,0.00001526023,0.00039522338],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996804,0.00005687907,0.000016873142,0.00008647082,0.00009921542,0.000060068593],"domain_scores_gemma":[0.9994362,0.00013741001,0.00017167341,0.00005609125,0.00014492145,0.000053759853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000384769,0.00041065158,0.0004461286,0.0018589558,0.00023404589,0.00047396068,0.0005120646,0.00039878234,0.00046786136],"category_scores_gemma":[0.0010573927,0.00016902802,0.00021792205,0.0007420219,0.00021469584,0.000419323,0.00039374782,0.00021677853,0.00028774893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011621318,0.00055316166,0.11688439,0.00020471317,0.00014361482,0.0010813279,0.00048367932,0.06955765,0.15926842,0.0009927304,0.0035973554,0.64607084],"study_design_scores_gemma":[0.000026116038,0.00026211192,0.121302605,0.000017764487,0.000051739262,0.0005145473,0.00035333657,0.8317007,0.04386888,0.00044499323,0.0014223747,0.00003481131],"about_ca_topic_score_codex":0.008130102,"about_ca_topic_score_gemma":0.009522104,"teacher_disagreement_score":0.008130102,"about_ca_system_score_codex":0.00038214252,"about_ca_system_score_gemma":0.00045008768,"threshold_uncertainty_score":0.016165555},"labels":[],"label_agreement":null},{"id":"W2062154187","doi":"10.1109/ivs.2013.6629474","title":"Tracking an on the run vehicle in a metropolitan VANET","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Carleton University","funders":"","keywords":"Vehicular ad hoc network; Computer science; Tracking (education); Metropolitan area; Vehicle tracking system; Scope (computer science); Real-time computing; Computer network; Wireless ad hoc network; Artificial intelligence; Telecommunications; Kalman filter; Wireless; Geography","score_opus":0.045619039249737654,"score_gpt":0.30211210620429785,"score_spread":0.2564930669545602,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2062154187","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":[0.50406635,0.00022537475,0.49095458,0.00025575122,0.000025518466,0.00006220068,0.00012940228,0.00029482832,0.0039859866],"genre_scores_gemma":[0.960845,0.0001802997,0.03538417,0.00003863737,0.000015505842,0.000033127337,0.00016909392,0.000018536699,0.0033157968],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997074,0.000101577185,0.000010530587,0.00008610095,0.000039938303,0.000054467604],"domain_scores_gemma":[0.99968123,0.00013210194,0.000055622273,0.000045523477,0.000042416483,0.000043132422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044892734,0.0003662942,0.0005094738,0.00040854423,0.00050162256,0.0007047054,0.0007167253,0.0005838044,0.0005412034],"category_scores_gemma":[0.001016954,0.00028744544,0.00034761496,0.0004985128,0.0003968974,0.00093547313,0.0010691106,0.00037858015,0.00016713512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033203448,0.0000767121,0.017881412,0.00006396193,0.00013473142,0.00060815754,0.0005266523,0.8876088,0.011076064,0.014307657,0.00089142687,0.06649244],"study_design_scores_gemma":[0.000005308449,0.0000613215,0.0016636434,0.0000053530694,0.00001717148,0.00008420215,0.00015810611,0.9931238,0.0015390568,0.002163858,0.0011672181,0.000010920584],"about_ca_topic_score_codex":0.014272136,"about_ca_topic_score_gemma":0.01827672,"teacher_disagreement_score":0.014272136,"about_ca_system_score_codex":0.00047129253,"about_ca_system_score_gemma":0.0004252209,"threshold_uncertainty_score":0.028378129},"labels":[],"label_agreement":null},{"id":"W2063785986","doi":"10.1007/s00138-010-0300-1","title":"People tracking using a network-based PTZ camera","year":2010,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer vision; Artificial intelligence; Computer science; Frame rate; Frame (networking); Tracking system; Tracking (education)","score_opus":0.016539063497580386,"score_gpt":0.32386302805852296,"score_spread":0.3073239645609426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063785986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14064133,0.00045154814,0.84643215,0.00020268958,0.00020772393,0.00014619343,0.0005071927,0.0027341694,0.008676978],"genre_scores_gemma":[0.76189965,0.00047478738,0.2304536,0.00012370977,0.00012617187,0.0001132381,0.00055981305,0.000065798835,0.0061832387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954647,0.00005326639,0.00001069427,0.0001614752,0.00018201294,0.000045998662],"domain_scores_gemma":[0.9997497,0.000033712164,0.000027715854,0.000042745283,0.00011825605,0.00002779248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031636603,0.0005258783,0.00084780285,0.0012320458,0.0004304976,0.0006731036,0.0007688342,0.0007016764,0.0022248377],"category_scores_gemma":[0.0006183924,0.00036406005,0.0003801622,0.0013318198,0.00024152348,0.0009244978,0.00096791453,0.0005040935,0.0009853406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015413068,0.00028198378,0.0107138,0.00026870615,0.00018024162,0.0004152472,0.00020709589,0.04476581,0.20930593,0.0032517794,0.0072576273,0.72181046],"study_design_scores_gemma":[0.00011576478,0.00034848342,0.015428594,0.000022821607,0.00014376004,0.0012205506,0.00007637602,0.9127112,0.062386017,0.0012138295,0.0062846686,0.000047949063],"about_ca_topic_score_codex":0.005750598,"about_ca_topic_score_gemma":0.006222404,"teacher_disagreement_score":0.005750598,"about_ca_system_score_codex":0.00053313107,"about_ca_system_score_gemma":0.0005033862,"threshold_uncertainty_score":0.011434257},"labels":[],"label_agreement":null},{"id":"W2063836047","doi":"10.1109/jetcas.2013.2256819","title":"Geometry-Based Object Association and Consistent Labeling in Multi-Camera Surveillance","year":2013,"lang":"en","type":"article","venue":"IEEE Journal on Emerging and Selected Topics in Circuits and Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; Object (grammar); Homography; Computer science; Camera resectioning; Field of view; Object detection; Ground plane; Camera auto-calibration; Association (psychology); Plane (geometry); Constraint (computer-aided design); Computer graphics (images); Geometry; Mathematics; Pattern recognition (psychology)","score_opus":0.036185649963141706,"score_gpt":0.28587336462916213,"score_spread":0.2496877146660204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2063836047","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007990128,0.00013605187,0.99144334,0.000032584074,0.000009300818,0.000013023719,0.000014805568,0.00015857274,0.00020223278],"genre_scores_gemma":[0.33630332,0.00041459806,0.6619446,0.000085930355,0.00007079137,0.00007782923,0.00021147063,0.000111433576,0.0007800353],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99758434,0.00068842765,0.0000842968,0.0007857922,0.0006439122,0.00021313236],"domain_scores_gemma":[0.9981248,0.000526752,0.00039344106,0.00048514453,0.0003636199,0.00010623724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013445773,0.00095795677,0.0016380629,0.0014088496,0.00058257754,0.0012278125,0.0028764354,0.0012158672,0.0004888822],"category_scores_gemma":[0.0035353303,0.0010202393,0.0011472311,0.001549188,0.0011706834,0.0025504902,0.0022115358,0.0010725806,0.0003462125],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004655596,0.00014680097,0.0043734387,0.00019258192,0.00019091382,0.00062296237,0.00061010045,0.47645313,0.051697105,0.041967716,0.0016262603,0.42165348],"study_design_scores_gemma":[0.000012337589,0.000081719045,0.0008731208,0.000008731407,0.000027127364,0.00026403822,0.00005326452,0.978204,0.009122407,0.010274097,0.0010511052,0.000027946686],"about_ca_topic_score_codex":0.0030828377,"about_ca_topic_score_gemma":0.002662681,"teacher_disagreement_score":0.0030828377,"about_ca_system_score_codex":0.00081502466,"about_ca_system_score_gemma":0.0007603941,"threshold_uncertainty_score":0.0071108937},"labels":[],"label_agreement":null},{"id":"W2064300260","doi":"10.1109/crv.2008.34","title":"Automatic Registration of Color and Infrared Videos Using Trajectories Obtained from a Multiple Object Tracking Algorithm","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"RANSAC; Computer vision; Artificial intelligence; Computer science; Tracking (education); Intersection (aeronautics); Ground truth; Video tracking; Object (grammar); Image registration; Pixel; Algorithm; Image (mathematics); Geography","score_opus":0.04748374295105534,"score_gpt":0.2852154048132386,"score_spread":0.23773166186218325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064300260","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07902029,0.00022237336,0.9174015,0.000055710316,0.0000641209,0.000083064115,0.00010951798,0.0018778121,0.0011657027],"genre_scores_gemma":[0.39933583,0.00032006565,0.5974359,0.000026552927,0.00003077454,0.00009588989,0.0006039616,0.00016575429,0.0019852945],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999438,0.000079314275,0.00003856617,0.00019387268,0.00019863364,0.000051585634],"domain_scores_gemma":[0.9991609,0.00013451539,0.00019286032,0.0001251296,0.00033869903,0.0000479206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008096536,0.00062455627,0.0006783766,0.0016106449,0.00039551881,0.00077950273,0.0006252701,0.000531836,0.0010655593],"category_scores_gemma":[0.0023823425,0.00029176395,0.0003888343,0.0014414802,0.00029696274,0.0009083239,0.00054405327,0.0005329278,0.0007126501],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094469456,0.00021965067,0.0061071045,0.00014738536,0.00009908744,0.00035645446,0.00029063015,0.07542326,0.19631761,0.0048763966,0.0018082028,0.7134096],"study_design_scores_gemma":[0.00004368793,0.00026796144,0.009370295,0.000030587147,0.000060320574,0.0004920877,0.00014508373,0.8575704,0.12528808,0.0018253884,0.004844907,0.00006123718],"about_ca_topic_score_codex":0.0039279955,"about_ca_topic_score_gemma":0.003313471,"teacher_disagreement_score":0.0039279955,"about_ca_system_score_codex":0.00055899203,"about_ca_system_score_gemma":0.0007701563,"threshold_uncertainty_score":0.0078102946},"labels":[],"label_agreement":null},{"id":"W2064976688","doi":"10.1117/12.775859","title":"Improving multiple target tracking in structured environments using velocity priors","year":2008,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Los Alamos National Laboratory; University of British Columbia; University of Arizona","keywords":"Computer science; Frame (networking); Tracking (education); Prior probability; Computer vision; Artificial intelligence; Matching (statistics); Histogram; Scale (ratio); Detector; Point (geometry); Point set registration; Track (disk drive); Data mining; Image (mathematics); Mathematics; Geography; Statistics","score_opus":0.019868512778936046,"score_gpt":0.24682258876999202,"score_spread":0.226954075991056,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2064976688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012535785,0.00007548882,0.9862422,0.00003904684,0.0000149326315,0.000012506868,0.000029406998,0.0005790692,0.00047165784],"genre_scores_gemma":[0.39537004,0.0003731972,0.60051125,0.00015258948,0.000077036,0.00008205415,0.00073924416,0.0002770728,0.0024175525],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993449,0.000102774124,0.000021845697,0.00021894409,0.00023964574,0.000071945375],"domain_scores_gemma":[0.9983955,0.00088773755,0.00016642168,0.00022359294,0.0002649222,0.00006185082],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011265208,0.0008688506,0.0010197583,0.0012226915,0.0005361126,0.0010392175,0.0012428239,0.0009257447,0.0011248677],"category_scores_gemma":[0.005354639,0.0007906152,0.0006205495,0.0010524829,0.0005835696,0.002673709,0.0013987075,0.0012979308,0.00086144346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013744891,0.0001159597,0.0021666344,0.000049253405,0.000056267727,0.000080141595,0.000093809824,0.73379403,0.015768413,0.005335077,0.0013133072,0.24108967],"study_design_scores_gemma":[0.000007667059,0.00002586066,0.0005286841,0.0000048118486,0.0000059712715,0.000028546328,0.000011310245,0.9934777,0.0029747759,0.0024943661,0.0004311422,0.00000914945],"about_ca_topic_score_codex":0.006853845,"about_ca_topic_score_gemma":0.0074566086,"teacher_disagreement_score":0.006853845,"about_ca_system_score_codex":0.0006168925,"about_ca_system_score_gemma":0.0009562324,"threshold_uncertainty_score":0.013627887},"labels":[],"label_agreement":null},{"id":"W2066021222","doi":"10.1109/tii.2013.2294134","title":"Gaussian Mixture Model With Advanced Distance Measure Based on Support Weights and Histogram of Gradients for Background Suppression","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Mixture model; Histogram; Pattern recognition (psychology); Artificial intelligence; Computer science; Measure (data warehouse); Foreground detection; Pixel; Noise (video); Generalization; Background subtraction; Object detection; Mathematics; Computer vision; Image (mathematics); Data mining","score_opus":0.04100685308295286,"score_gpt":0.27488173095673235,"score_spread":0.23387487787377947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2066021222","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00710576,0.00043574072,0.99148613,0.000054367763,0.00004506391,0.00001892172,0.000021352476,0.00034889538,0.00048368928],"genre_scores_gemma":[0.3764406,0.0017864684,0.614876,0.00014140709,0.00014300519,0.00013152049,0.00042935816,0.00024549197,0.0058060866],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992399,0.00016412202,0.00003445917,0.00017714236,0.00031629886,0.000068184614],"domain_scores_gemma":[0.99961406,0.00012705228,0.000036694146,0.00005158267,0.00014787559,0.000022709246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009782894,0.0009722308,0.0009316119,0.0012237733,0.00030208132,0.0008599132,0.0013066529,0.0008966462,0.00089827477],"category_scores_gemma":[0.0019051805,0.00035068023,0.001080288,0.0013975479,0.00040109828,0.0016487513,0.00084103557,0.0013269363,0.00070934347],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034229603,0.00018613243,0.0035227784,0.00029795148,0.00025156038,0.00019547161,0.00022644753,0.28577065,0.04388668,0.034951393,0.0043839556,0.6259846],"study_design_scores_gemma":[0.0000064776254,0.000047673395,0.00061219896,0.00000825715,0.00003107322,0.000091811235,0.000014950654,0.98869145,0.005509428,0.0028567412,0.0021050633,0.000024868154],"about_ca_topic_score_codex":0.005041105,"about_ca_topic_score_gemma":0.003557805,"teacher_disagreement_score":0.005041105,"about_ca_system_score_codex":0.00049470534,"about_ca_system_score_gemma":0.0008059649,"threshold_uncertainty_score":0.010023475},"labels":[],"label_agreement":null},{"id":"W2067140628","doi":"10.1117/12.704537","title":"Occlusion and split detection and correction for object tracking in surveillance applications","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Communications Research Centre Canada","funders":"","keywords":"Artificial intelligence; Computer vision; Occlusion; Computer science; Segmentation; Tracking (education); Feature (linguistics); Object detection; Object (grammar); Video tracking; Pattern recognition (psychology)","score_opus":0.012291857919845632,"score_gpt":0.2622637493603064,"score_spread":0.24997189144046075,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2067140628","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020585738,0.00031408723,0.9773196,0.00003029102,0.000039840037,0.000052776657,0.000030797182,0.0010847335,0.0005422577],"genre_scores_gemma":[0.29802638,0.00043336552,0.6987433,0.000074245596,0.000066942644,0.00010224876,0.0003506022,0.00017692664,0.002026009],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99896467,0.00009345356,0.0000643044,0.0002535002,0.00052435824,0.00009971227],"domain_scores_gemma":[0.999017,0.0002577549,0.00020331293,0.00017263307,0.0003012345,0.000048168476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090011,0.00072306546,0.00082790357,0.0014348344,0.0006262339,0.0008325204,0.001183641,0.0007612489,0.0010315252],"category_scores_gemma":[0.002291095,0.0004166899,0.00055862445,0.0009059358,0.00050161686,0.0011483512,0.0009248124,0.00075727055,0.00068303937],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005023205,0.0001305344,0.005076294,0.000107206346,0.000071210394,0.0002321816,0.000250177,0.024403675,0.09204446,0.0029634647,0.0023957237,0.8718228],"study_design_scores_gemma":[0.000031577732,0.00024896697,0.008513939,0.000033145105,0.00008038273,0.00079783704,0.00006572583,0.8891248,0.08874876,0.0026307225,0.009676998,0.00004724155],"about_ca_topic_score_codex":0.0022628705,"about_ca_topic_score_gemma":0.0024461243,"teacher_disagreement_score":0.0022628705,"about_ca_system_score_codex":0.00055897067,"about_ca_system_score_gemma":0.0008034628,"threshold_uncertainty_score":0.0047602654},"labels":[],"label_agreement":null},{"id":"W2070691615","doi":"10.3182/20080706-5-kr-1001.01555","title":"Dynamic Object Identification by a Moving Robot Using Laser Data","year":2008,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer vision; Artificial intelligence; Robot; Mobile robot; Object (grammar); Laser; Computer science; Identification (biology); Laser scanning; Optics; Physics","score_opus":0.052577744558761984,"score_gpt":0.31137239776693326,"score_spread":0.2587946532081713,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2070691615","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10210126,0.0003921852,0.8938345,0.00017008693,0.00008937081,0.000052030446,0.00009106104,0.0007423409,0.0025271648],"genre_scores_gemma":[0.6524848,0.0004658333,0.33973297,0.00011644572,0.00010486398,0.00013390428,0.00030267387,0.000091488415,0.006567003],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996044,0.000042782376,0.0000171245,0.00010837973,0.00018227399,0.000045012224],"domain_scores_gemma":[0.9996288,0.0001047327,0.000055843324,0.00007709995,0.00011234679,0.000021127846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039238183,0.00054577604,0.0008561222,0.0019133714,0.00069158437,0.0009367071,0.00079244806,0.0010910991,0.0014473626],"category_scores_gemma":[0.001223754,0.00056196784,0.00043930218,0.0015925014,0.0006804396,0.0013684656,0.0011413492,0.0006881193,0.00096796075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066175376,0.0001935318,0.0054725325,0.00015610896,0.00008574336,0.0005298558,0.00039439072,0.072952226,0.24546231,0.0049565197,0.0020141432,0.66712093],"study_design_scores_gemma":[0.00003286942,0.00036950072,0.010208686,0.000050844134,0.00008592874,0.00074384036,0.00029405023,0.85774016,0.117402315,0.004923175,0.008074787,0.00007393264],"about_ca_topic_score_codex":0.0023405142,"about_ca_topic_score_gemma":0.0022658252,"teacher_disagreement_score":0.0023405142,"about_ca_system_score_codex":0.0003654574,"about_ca_system_score_gemma":0.0005842179,"threshold_uncertainty_score":0.004841864},"labels":[],"label_agreement":null},{"id":"W2073864116","doi":"10.5539/mas.v3n7p78","title":"Shadow Elimination Method for Video Surveillance","year":2009,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Segmentation; Shadow (psychology); Color histogram; Image segmentation; Histogram; RGB color model; Region growing; Feature (linguistics); Pattern recognition (psychology); Color image; Image (mathematics); Scale-space segmentation; Image processing","score_opus":0.021682875109745543,"score_gpt":0.3229870883030282,"score_spread":0.30130421319328266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2073864116","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015665775,0.0006320018,0.98027575,0.00005849754,0.000078541496,0.000036252422,0.000026633275,0.0005856829,0.0026408306],"genre_scores_gemma":[0.30909494,0.0013708523,0.6807204,0.00013477863,0.0001611287,0.000100947895,0.0002409097,0.0001271611,0.008048883],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997936,0.000023865003,0.000009180602,0.00005443081,0.000103611375,0.000015332591],"domain_scores_gemma":[0.9998714,0.000027399996,0.00001403858,0.00001879125,0.00005722098,0.000011158087],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017438064,0.00033352067,0.0003501806,0.0006652333,0.00036313178,0.00032002234,0.0005184957,0.00030830898,0.0019132547],"category_scores_gemma":[0.0003447092,0.00015409956,0.00040303348,0.00042967804,0.00019528421,0.00053012307,0.00033534787,0.0003276199,0.0005991688],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001272825,0.00004951356,0.00059729116,0.00024276182,0.000030274248,0.00016860069,0.00019994026,0.009972299,0.19303574,0.006970293,0.0031988018,0.78540725],"study_design_scores_gemma":[0.00007299508,0.0003537886,0.005791631,0.000059987677,0.00013171947,0.0019602433,0.00018973103,0.6861585,0.24073434,0.007315488,0.05715075,0.00008084474],"about_ca_topic_score_codex":0.0014025891,"about_ca_topic_score_gemma":0.0014518456,"teacher_disagreement_score":0.0019132547,"about_ca_system_score_codex":0.000268046,"about_ca_system_score_gemma":0.0004203151,"threshold_uncertainty_score":0.006400466},"labels":[],"label_agreement":null},{"id":"W2074220689","doi":"10.1109/crv.2010.52","title":"Max-Margin Offline Pedestrian Tracking with Multiple Cues","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia; Polytechnique Montréal; Simon Fraser University","funders":"","keywords":"Robustness (evolution); BitTorrent tracker; Computer science; Artificial intelligence; Discriminative model; Margin (machine learning); Pedestrian; Tracking (education); Computer vision; Pedestrian detection; Data association; Machine learning; Pattern recognition (psychology); Eye tracking; Engineering","score_opus":0.022333601621320386,"score_gpt":0.2769917280985035,"score_spread":0.2546581264771831,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074220689","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0100644315,0.0001426728,0.9881207,0.000034537854,0.000025526113,0.000019793191,0.000036105343,0.00093007385,0.00062614173],"genre_scores_gemma":[0.35367838,0.00018991547,0.64087534,0.00015072459,0.00008020749,0.00008459327,0.00050541834,0.00031717258,0.004118153],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99900097,0.00019948666,0.000043937194,0.00039738265,0.00025518212,0.00010303832],"domain_scores_gemma":[0.9989191,0.00033451634,0.00015554081,0.00029404415,0.00021058122,0.000086264416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014598735,0.0011689439,0.0018107134,0.0010389645,0.00056909776,0.0012221035,0.0018157519,0.001182424,0.0020524638],"category_scores_gemma":[0.0033299997,0.0007774552,0.0007446769,0.0012566623,0.00048540146,0.0023657884,0.0019070983,0.0012195574,0.001306913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009744206,0.00030310327,0.0038488284,0.00016213571,0.0001122548,0.00014352733,0.00019564464,0.1473589,0.03570677,0.0070672,0.0038504275,0.80027676],"study_design_scores_gemma":[0.000020583306,0.00012240141,0.0012322378,0.000015386924,0.000026890586,0.00013616684,0.000026651567,0.9721834,0.019585872,0.0039978093,0.0026292729,0.0000233742],"about_ca_topic_score_codex":0.0016807009,"about_ca_topic_score_gemma":0.0028242012,"teacher_disagreement_score":0.0020524638,"about_ca_system_score_codex":0.0006603012,"about_ca_system_score_gemma":0.0009526305,"threshold_uncertainty_score":0.0077205896},"labels":[],"label_agreement":null},{"id":"W2074685565","doi":"10.1007/s11263-006-8892-7","title":"Pre-Attentive and Attentive Detection of Humans in Wide-Field Scenes","year":2006,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Probabilistic logic; Computer vision; Bayesian probability; Pattern recognition (psychology); Gaze; Saccadic masking; Prior probability; Eye movement","score_opus":0.009060323129259432,"score_gpt":0.3020425760075991,"score_spread":0.2929822528783397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2074685565","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.87081385,0.0012576251,0.115899876,0.00025040325,0.00020499213,0.00012594223,0.00019570338,0.0005986981,0.010652966],"genre_scores_gemma":[0.97759014,0.00028991676,0.017978331,0.00014555771,0.00008178226,0.000023043625,0.00020080549,0.000069601585,0.0036208886],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996463,0.000042621356,0.000008562352,0.00011818113,0.00006585792,0.00011840789],"domain_scores_gemma":[0.9989813,0.0004850108,0.00007493277,0.00009681533,0.0001734649,0.00018843233],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007860958,0.0006341387,0.0005563998,0.00067625684,0.00042729545,0.0009958195,0.00051940634,0.0009053223,0.00222983],"category_scores_gemma":[0.0029123293,0.00046702407,0.00035022316,0.00020705405,0.00056467904,0.0010988635,0.00086298963,0.00075284764,0.0003888714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0044361516,0.00071849965,0.03431584,0.00027647975,0.00018061054,0.0006538187,0.0010811889,0.015154528,0.49102327,0.003270062,0.003902589,0.444987],"study_design_scores_gemma":[0.00010561922,0.0023701845,0.50095004,0.00009248186,0.0003469997,0.0020842927,0.0010174428,0.30751258,0.17174698,0.009099294,0.004554416,0.000119655655],"about_ca_topic_score_codex":0.0023279663,"about_ca_topic_score_gemma":0.004394043,"teacher_disagreement_score":0.0023279663,"about_ca_system_score_codex":0.00032757432,"about_ca_system_score_gemma":0.00057494757,"threshold_uncertainty_score":0.007459581},"labels":[],"label_agreement":null},{"id":"W2075562601","doi":"10.1109/crv.2010.53","title":"Object Inter-camera Tracking with Non-overlapping Views: A New Dynamic Approach","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Disjoint sets; Video tracking; Tracking (education); Object (grammar); Histogram; Matching (statistics); Weighting; Benchmark (surveying); Image (mathematics); Mathematics","score_opus":0.02501069992339483,"score_gpt":0.2906916093993938,"score_spread":0.265680909475999,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075562601","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":[0.0060830885,0.00038181889,0.9923394,0.00004884192,0.000042922602,0.000026765716,0.00003590379,0.00029359222,0.0007475943],"genre_scores_gemma":[0.22544429,0.0012012498,0.76704824,0.00015164526,0.00021973666,0.00011813118,0.00045916115,0.00024963336,0.0051079346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979634,0.00020294324,0.000092656504,0.0007042973,0.0008810628,0.00015563078],"domain_scores_gemma":[0.99887794,0.0001953985,0.00018874573,0.00031719726,0.00032452744,0.00009621535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010365043,0.00074562384,0.0015793323,0.0027698365,0.00059893704,0.0016403454,0.0022035567,0.0010759307,0.00096660503],"category_scores_gemma":[0.0019028073,0.00065407075,0.0012658029,0.0026135498,0.00059865083,0.0025429563,0.0018569768,0.0011231011,0.0006584313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001656559,0.00015312324,0.0028892446,0.00014816933,0.0003144274,0.00025843258,0.00030898149,0.051204793,0.047676846,0.012571964,0.0021857866,0.8821226],"study_design_scores_gemma":[0.000024481347,0.00017520081,0.0039051077,0.00003451801,0.00016790222,0.00081641,0.00012355988,0.9581373,0.016180227,0.0069008116,0.013455403,0.00007918351],"about_ca_topic_score_codex":0.003415107,"about_ca_topic_score_gemma":0.0038481131,"teacher_disagreement_score":0.003415107,"about_ca_system_score_codex":0.00062593195,"about_ca_system_score_gemma":0.0006450855,"threshold_uncertainty_score":0.006790459},"labels":[],"label_agreement":null},{"id":"W2075667775","doi":"10.1109/icip.2010.5653823","title":"Model-based tracking: Temporal conditional random fields","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Conditional random field; Discriminative model; Artificial intelligence; Computer science; Kalman filter; Video tracking; Graphical model; Probabilistic logic; Optical flow; Pattern recognition (psychology); Computer vision; Feature (linguistics); Tracking (education); Event (particle physics); Pixel; Object (grammar); Image (mathematics)","score_opus":0.03737818700167692,"score_gpt":0.3070109240816418,"score_spread":0.2696327370799649,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075667775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018957509,0.00016084679,0.9968071,0.000085160886,0.000024344406,0.000009040943,0.00008107314,0.00047337715,0.0004633795],"genre_scores_gemma":[0.5266338,0.0012614558,0.46552345,0.00033036043,0.00022590294,0.00017277685,0.0010787839,0.0003501988,0.0044233133],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944955,0.00016584012,0.000019013345,0.00015245509,0.00016155413,0.000051470404],"domain_scores_gemma":[0.99857044,0.00084074214,0.00016978527,0.00021443002,0.00015900112,0.000045625016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015451662,0.0006124848,0.00084255607,0.0007935835,0.00031617124,0.0006962151,0.0014882371,0.000795639,0.001693521],"category_scores_gemma":[0.004289772,0.00044228256,0.0007896342,0.0012789834,0.0006592102,0.0017385689,0.0006664466,0.0012176771,0.00050015276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008871151,0.000042484353,0.0013725868,0.00008341872,0.00006782256,0.000082457715,0.000046232937,0.81262,0.003170424,0.05425983,0.003828987,0.124336995],"study_design_scores_gemma":[0.000005687496,0.000013122542,0.00016108334,0.000006103189,0.000009135449,0.000027877266,0.000001609169,0.98776126,0.00060265826,0.01019747,0.001203749,0.000010129374],"about_ca_topic_score_codex":0.009403901,"about_ca_topic_score_gemma":0.006857069,"teacher_disagreement_score":0.009403901,"about_ca_system_score_codex":0.00088270096,"about_ca_system_score_gemma":0.0010237381,"threshold_uncertainty_score":0.018698335},"labels":[],"label_agreement":null},{"id":"W2075812217","doi":"10.1117/12.2063306","title":"Fusion of thermal infrared and visible spectrum for robust pedestrian tracking","year":2014,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Pedestrian; Infrared; Pedestrian detection; Tracking (education); Computer vision; Artificial intelligence; Fusion; Thermal infrared; Visible spectrum; Sensor fusion; Work (physics); Optics; Physics; Engineering; Transport engineering","score_opus":0.018086569922021258,"score_gpt":0.24515025111907243,"score_spread":0.22706368119705117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2075812217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07897064,0.0018660004,0.91169,0.0001195989,0.00033201367,0.000067847504,0.00020838737,0.0020293044,0.0047162785],"genre_scores_gemma":[0.7092074,0.0012231767,0.28519127,0.00019155174,0.00021216334,0.00007128527,0.0005701345,0.000113459035,0.0032195188],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993875,0.00008274078,0.000023200611,0.00016075445,0.00024636573,0.00009937836],"domain_scores_gemma":[0.99970335,0.000044603872,0.00004221721,0.000052187756,0.00012772114,0.000029915424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009196776,0.0007742963,0.0009369307,0.0017947819,0.00035477558,0.0007319797,0.0006500045,0.00067205523,0.0010689349],"category_scores_gemma":[0.000923815,0.00034456595,0.00096718076,0.0012602307,0.00026216582,0.0010848235,0.00089540577,0.0005753946,0.0009299519],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012192471,0.00049016357,0.00885033,0.00034952094,0.0002778713,0.00034649778,0.00021264027,0.03857974,0.21580398,0.0035883319,0.00455184,0.7257298],"study_design_scores_gemma":[0.000044205106,0.00065130607,0.019418433,0.000086454784,0.00043362138,0.0011701878,0.00015597967,0.85592175,0.10742862,0.005572117,0.008967287,0.00015013127],"about_ca_topic_score_codex":0.0013663508,"about_ca_topic_score_gemma":0.0021348149,"teacher_disagreement_score":0.0017947819,"about_ca_system_score_codex":0.0002616472,"about_ca_system_score_gemma":0.00037140827,"threshold_uncertainty_score":0.0048637986},"labels":[],"label_agreement":null},{"id":"W2076866895","doi":"10.1109/tvcg.2013.168","title":"Interactive Exploration of Surveillance Video through Action Shot Summarization and Trajectory Visualization","year":2013,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":82,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"University of Manitoba","keywords":"Computer science; Automatic summarization; Computer vision; Visualization; Artificial intelligence; Video tracking; Trajectory; Object (grammar); Movement (music); Timeline; Shot (pellet); Representation (politics)","score_opus":0.061689843709746325,"score_gpt":0.33447222205369287,"score_spread":0.27278237834394653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2076866895","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04227192,0.00024902084,0.9247075,0.00017004643,0.000039330393,0.00025418404,0.001311679,0.02912154,0.0018748642],"genre_scores_gemma":[0.17545915,0.00030434658,0.81819594,0.00009348243,0.00007151746,0.0002811899,0.0023758414,0.0012115865,0.002006925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952734,0.000107907195,0.000034238896,0.00012465121,0.00015981648,0.00004619367],"domain_scores_gemma":[0.99851495,0.00074078486,0.00016025883,0.00015777996,0.00026800757,0.00015815297],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092769077,0.0010550779,0.00078612874,0.0021334388,0.00026048752,0.0015405312,0.0013438341,0.0005638059,0.0037368508],"category_scores_gemma":[0.0025726699,0.000406366,0.0006728249,0.00082862895,0.00036794785,0.0015996562,0.0014666169,0.00068289816,0.0008722516],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0024203528,0.00041782125,0.006127292,0.0011058679,0.00026812727,0.00095305906,0.0038403356,0.024703987,0.2615479,0.0048348065,0.016929116,0.6768513],"study_design_scores_gemma":[0.00026854913,0.0010826783,0.013543951,0.00021173233,0.00020341914,0.0012533533,0.001430514,0.8002254,0.1284125,0.008204823,0.04486501,0.0002981912],"about_ca_topic_score_codex":0.0029765605,"about_ca_topic_score_gemma":0.003348612,"teacher_disagreement_score":0.0037368508,"about_ca_system_score_codex":0.00035141665,"about_ca_system_score_gemma":0.00038003104,"threshold_uncertainty_score":0.012501001},"labels":[],"label_agreement":null},{"id":"W2078459001","doi":"10.1109/wacv.2014.6836059","title":"Improving background subtraction using Local Binary Similarity Patterns","year":2014,"lang":"en","type":"article","venue":"IEEE Winter Conference on Applications of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":156,"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":"Pixel; Background subtraction; Computer science; Artificial intelligence; Binary number; Similarity (geometry); Subtraction; Pattern recognition (psychology); Computer vision; Component (thermodynamics); Image (mathematics); Mathematics; Arithmetic","score_opus":0.04789635418718716,"score_gpt":0.33383279514476694,"score_spread":0.28593644095757975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2078459001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033511367,0.00045848597,0.96257997,0.000066066284,0.000073917094,0.000043441436,0.000052344385,0.001544899,0.0016695829],"genre_scores_gemma":[0.22612691,0.0009649516,0.7672359,0.000141134,0.000076960416,0.00005710198,0.00057619944,0.00041557144,0.004405291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924576,0.00007475194,0.000031446973,0.00014807675,0.00043248007,0.0000674393],"domain_scores_gemma":[0.99939275,0.00014650343,0.00006014387,0.000093139766,0.00027140358,0.000036038124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006313821,0.0010622567,0.001011869,0.0021378207,0.00028000586,0.0010247466,0.0011410541,0.0007631465,0.0017062236],"category_scores_gemma":[0.002142136,0.000403901,0.00078962283,0.0015827527,0.00030143026,0.0013658253,0.00117885,0.0007869498,0.0015219019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022386476,0.00016651851,0.0011968586,0.00022757884,0.0001042399,0.0001547274,0.00009547327,0.027945373,0.2635396,0.002635012,0.0018223001,0.7018884],"study_design_scores_gemma":[0.000038127444,0.00015270771,0.004261484,0.000026845815,0.00009972224,0.0005514777,0.00007022381,0.7542558,0.23017263,0.0023200316,0.008005254,0.000045690012],"about_ca_topic_score_codex":0.0021058517,"about_ca_topic_score_gemma":0.0026014438,"teacher_disagreement_score":0.0021378207,"about_ca_system_score_codex":0.00029745692,"about_ca_system_score_gemma":0.0005722604,"threshold_uncertainty_score":0.00570786},"labels":[],"label_agreement":null},{"id":"W2079360804","doi":"10.1109/cjece.2003.1532510","title":"Robust image-based detection of activity for traffic control","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Hue; Robustness (evolution); Segmentation; Image processing; Intersection (aeronautics); HSL and HSV; Image (mathematics); Engineering","score_opus":0.012207584544711977,"score_gpt":0.19776654932991766,"score_spread":0.18555896478520567,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079360804","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":[0.038252093,0.001005823,0.95367885,0.00012829968,0.000100284466,0.00006491453,0.00026148732,0.0035321768,0.0029760937],"genre_scores_gemma":[0.73636967,0.0006984374,0.25907645,0.0001259796,0.00012577312,0.00011643354,0.0006983469,0.00019669188,0.0025920875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996879,0.00003940202,0.000010941086,0.00008243464,0.00014656138,0.000032771917],"domain_scores_gemma":[0.999689,0.00007419144,0.00005789895,0.000045440724,0.000115055234,0.00001848817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027375246,0.0004520247,0.00061460695,0.0009666184,0.00014726233,0.00063178485,0.0005758132,0.00046944412,0.0017494756],"category_scores_gemma":[0.001231081,0.0001727739,0.00032478556,0.0006435178,0.0002526411,0.00040467858,0.0002961911,0.00045444455,0.0010603004],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004990313,0.0001573115,0.0014913924,0.00019019522,0.00008255305,0.00012083492,0.000056471803,0.059156835,0.24546416,0.0036445742,0.003941805,0.68519485],"study_design_scores_gemma":[0.000036107198,0.00023271743,0.0060113478,0.000022484131,0.00006796662,0.00026566978,0.00002907477,0.871719,0.111314975,0.003080457,0.0071649044,0.000055354896],"about_ca_topic_score_codex":0.0016836574,"about_ca_topic_score_gemma":0.0015855295,"teacher_disagreement_score":0.0017494756,"about_ca_system_score_codex":0.00035526435,"about_ca_system_score_gemma":0.00032735235,"threshold_uncertainty_score":0.0058526397},"labels":[],"label_agreement":null},{"id":"W2079507716","doi":"10.1109/crv.2010.27","title":"Human Tracking Using Spatialized Multi-level Histogram and Mean Shift","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Histogram; Artificial intelligence; Computer vision; Histogram matching; Mean-shift; Computer science; Pattern recognition (psychology); Histogram of oriented gradients; Feature (linguistics); Representation (politics); Metric (unit); Matching (statistics); Object (grammar); Mathematics; Image (mathematics)","score_opus":0.13056594034185748,"score_gpt":0.3682296280787742,"score_spread":0.2376636877369167,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2079507716","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013693726,0.00021862779,0.9845526,0.000046593574,0.000034345234,0.00001832658,0.00006755658,0.0008828918,0.0004852396],"genre_scores_gemma":[0.41443124,0.00037801784,0.5826587,0.00009064803,0.000067110486,0.000060887647,0.00032773375,0.00017679876,0.0018088953],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959296,0.00006699272,0.00001726663,0.00013869106,0.00014739374,0.00003671385],"domain_scores_gemma":[0.9995839,0.00007249676,0.00006000079,0.00012829817,0.00012555679,0.000029699062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004929812,0.00038870625,0.00065622083,0.0015767006,0.00026061197,0.00071348867,0.00094032096,0.0005224892,0.0011443567],"category_scores_gemma":[0.001197307,0.00035680033,0.0007509331,0.0017608511,0.00034612967,0.0010441974,0.0007747193,0.00048742667,0.0005614469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035537043,0.000094493305,0.0021247831,0.00013066665,0.00019336656,0.00008639859,0.00012488806,0.14224961,0.054835495,0.007705651,0.0025584353,0.7895409],"study_design_scores_gemma":[0.00002258159,0.0000736717,0.0018903216,0.0000066481875,0.00003235259,0.0001580528,0.000020463844,0.97415507,0.016710898,0.004589044,0.002311798,0.000029117873],"about_ca_topic_score_codex":0.0045917514,"about_ca_topic_score_gemma":0.0045040566,"teacher_disagreement_score":0.0045917514,"about_ca_system_score_codex":0.0005864537,"about_ca_system_score_gemma":0.0005276501,"threshold_uncertainty_score":0.009130061},"labels":[],"label_agreement":null},{"id":"W2080292705","doi":"10.1109/tpami.2013.233","title":"The Applicability of Spatiotemporal Oriented Energy Features to Region Tracking","year":2014,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"BitTorrent tracker; Artificial intelligence; Computer science; Robustness (evolution); Computer vision; Histogram; Representation (politics); Tracking (education); Pattern recognition (psychology); Video tracking; Visualization; Eye tracking; Field (mathematics); Mathematics; Image (mathematics); Video processing","score_opus":0.020501730436576136,"score_gpt":0.2860809687502482,"score_spread":0.26557923831367203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080292705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011843163,0.00027751207,0.98600894,0.00007827587,0.00003418276,0.000018640369,0.00010416145,0.0002305732,0.0014045981],"genre_scores_gemma":[0.59626806,0.00092941953,0.3995285,0.00016491425,0.0001629328,0.00010278258,0.00061819813,0.00017104302,0.0020541495],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995197,0.0001010168,0.000030403287,0.00014588013,0.00017137254,0.000031687978],"domain_scores_gemma":[0.9987925,0.000456061,0.00021606426,0.00030958038,0.00018294655,0.000042671705],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007035341,0.00043386593,0.0005126968,0.001140358,0.0002793254,0.0013037273,0.0008228328,0.0006981116,0.0009112787],"category_scores_gemma":[0.004120335,0.00021253707,0.0005483697,0.0013790972,0.0006810764,0.0019372441,0.0009695432,0.00081261084,0.00043756855],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023104539,0.00009121544,0.0049825916,0.0002207617,0.00011518557,0.00028454134,0.00032866994,0.27151808,0.042156644,0.14558549,0.003060129,0.5314258],"study_design_scores_gemma":[0.000011955154,0.00013556896,0.003507877,0.000043002343,0.00004413099,0.0004064756,0.000059290232,0.9226013,0.010509646,0.053225923,0.009404902,0.000049878126],"about_ca_topic_score_codex":0.0016549503,"about_ca_topic_score_gemma":0.00091728993,"teacher_disagreement_score":0.0016549503,"about_ca_system_score_codex":0.0004144753,"about_ca_system_score_gemma":0.000394529,"threshold_uncertainty_score":0.0037206411},"labels":[],"label_agreement":null},{"id":"W2080330379","doi":"10.1109/crv.2014.49","title":"Multiple Feature Fusion in the Dempster-Shafer Framework for Multi-object Tracking","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Video tracking; Histogram; Set (abstract data type); Similarity (geometry); Matching (statistics); Object (grammar); Eye tracking; Representation (politics); Computer vision; Mathematics; Image (mathematics)","score_opus":0.0647071189223361,"score_gpt":0.34378769808657256,"score_spread":0.27908057916423645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2080330379","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027232368,0.0003560751,0.99623704,0.000034759003,0.000017506216,0.00001982967,0.000025144458,0.00030639497,0.00027997312],"genre_scores_gemma":[0.2378487,0.00091982685,0.75870025,0.00009524611,0.00009691011,0.00012738934,0.0002908457,0.00012099137,0.001799832],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984977,0.00031319782,0.000088844296,0.00031797893,0.0006694546,0.0001129376],"domain_scores_gemma":[0.99896276,0.0004304718,0.00014739372,0.00020579333,0.00019982996,0.00005372773],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002876277,0.0012590092,0.002225874,0.0028724447,0.00081401324,0.0013098395,0.002190613,0.0017489084,0.0013629197],"category_scores_gemma":[0.0047220294,0.0006963365,0.0017482567,0.003471126,0.00081688014,0.0025090373,0.0018353752,0.0016655552,0.0009421356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001782728,0.000098683966,0.001088625,0.00014694489,0.00022605447,0.00019627856,0.00014957914,0.56522256,0.010247216,0.02334451,0.0017524001,0.39734888],"study_design_scores_gemma":[0.000007717816,0.000029450714,0.00029819488,0.000008406478,0.000022916756,0.00007478991,0.000009852146,0.9879617,0.002288043,0.008154948,0.0011221082,0.000021892321],"about_ca_topic_score_codex":0.008372782,"about_ca_topic_score_gemma":0.0067874077,"teacher_disagreement_score":0.008372782,"about_ca_system_score_codex":0.0014819765,"about_ca_system_score_gemma":0.0011868475,"threshold_uncertainty_score":0.016648114},"labels":[],"label_agreement":null},{"id":"W2086775845","doi":"10.1016/j.inffus.2014.07.002","title":"Saliency-directed prioritization of visual data in wireless surveillance networks","year":2014,"lang":"en","type":"article","venue":"Information Fusion","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":61,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"National Research Foundation of Korea; Ministry of Education","keywords":"Computer science; Salient; Wireless sensor network; Node (physics); Real-time computing; Artificial intelligence; Computer network; Computer vision; Data mining","score_opus":0.014168177221051848,"score_gpt":0.282352775092011,"score_spread":0.26818459787095916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2086775845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097985625,0.0013108974,0.8983752,0.00024509913,0.00012889974,0.000046577356,0.000069283866,0.00027211494,0.0015663002],"genre_scores_gemma":[0.8986737,0.0005994732,0.09895871,0.00009897937,0.0001936444,0.000040022336,0.00011647103,0.000058064743,0.0012609349],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963295,0.000089527915,0.000017814493,0.00008376167,0.00011706847,0.00005900314],"domain_scores_gemma":[0.99885523,0.0006167639,0.000111003734,0.0000640106,0.00027694608,0.0000760363],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095962937,0.0006192078,0.00089251524,0.0011076935,0.00035261072,0.0007649602,0.00092067185,0.00051683804,0.00060266675],"category_scores_gemma":[0.0036058878,0.00032917183,0.0003055184,0.00095074,0.00039159,0.001232249,0.0009017341,0.0005589542,0.00011304774],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011774662,0.00029773152,0.0031824668,0.000342748,0.000109409244,0.00020769,0.00035833905,0.23478,0.086862616,0.017329447,0.0038239877,0.6515281],"study_design_scores_gemma":[0.000013751727,0.00010265497,0.0012040565,0.000008254912,0.000025436646,0.00005532636,0.000032915144,0.9865768,0.007013552,0.0043601366,0.0005983157,0.000008781118],"about_ca_topic_score_codex":0.0030522936,"about_ca_topic_score_gemma":0.0039571794,"teacher_disagreement_score":0.0030522936,"about_ca_system_score_codex":0.0005399707,"about_ca_system_score_gemma":0.00061089854,"threshold_uncertainty_score":0.006069064},"labels":[],"label_agreement":null},{"id":"W2087466987","doi":"","title":"Automated Collection of Pedestrian Data Using Computer Vision Techniques","year":2009,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pedestrian; Computer science; Artificial intelligence; Computer vision; Pedestrian detection; Feature (linguistics); Data collection; Video tracking; Field (mathematics); Frame (networking); Object (grammar); Transport engineering; Engineering","score_opus":0.03171661749784266,"score_gpt":0.31360742530924185,"score_spread":0.2818908078113992,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087466987","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12890528,0.0006023712,0.85273653,0.00007814037,0.00011319201,0.0007973994,0.0017920188,0.009889845,0.005085133],"genre_scores_gemma":[0.35886744,0.0006444453,0.63345486,0.00010624823,0.00008618164,0.00043540384,0.0029047374,0.00020557058,0.0032951103],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99935335,0.00006271925,0.000039471706,0.00020567245,0.00027469048,0.00006404991],"domain_scores_gemma":[0.99901605,0.00016139967,0.0001227296,0.000146221,0.00051042286,0.0000431103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048645542,0.0006448237,0.00087015476,0.0034330608,0.00048498609,0.0006538813,0.00067572435,0.0005071157,0.0016579712],"category_scores_gemma":[0.0011653323,0.00034298972,0.00044347203,0.00212531,0.00022448957,0.00077892875,0.0005834225,0.00041618574,0.0015811861],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003025482,0.00027353695,0.010883559,0.00037296885,0.000082070204,0.0003024856,0.0003825523,0.0075029614,0.15963858,0.0008123141,0.0065089893,0.8129374],"study_design_scores_gemma":[0.00015163035,0.001570882,0.17421502,0.00026437163,0.00025367577,0.0027978988,0.0008937753,0.43758848,0.32902855,0.005166245,0.04775933,0.00031024322],"about_ca_topic_score_codex":0.004467782,"about_ca_topic_score_gemma":0.0072002034,"teacher_disagreement_score":0.004467782,"about_ca_system_score_codex":0.00037499523,"about_ca_system_score_gemma":0.00082462514,"threshold_uncertainty_score":0.008883536},"labels":[],"label_agreement":null},{"id":"W2087655858","doi":"10.1007/s11554-014-0418-x","title":"Embedded architecture for noise-adaptive video object detection using parameter-compressed background modeling","year":2014,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Field-programmable gate array; Video processing; Real-time computing; Symmetric multiprocessor system; Embedded system; Computer hardware; Parallel computing","score_opus":0.03721048389594654,"score_gpt":0.31536633551914306,"score_spread":0.2781558516231965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087655858","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04876272,0.0003384808,0.9447354,0.00010629604,0.00011529021,0.00004595047,0.000072124756,0.004247708,0.0015760957],"genre_scores_gemma":[0.7074134,0.00026578948,0.28530812,0.00022003884,0.00006865422,0.00009283088,0.0003622211,0.00020457582,0.006064425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997813,0.000019185567,0.000013167623,0.00006828052,0.000083540974,0.000034635603],"domain_scores_gemma":[0.9997613,0.00004162484,0.000021523401,0.000045593686,0.00011271016,0.000017235889],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027514136,0.00065079075,0.00057349855,0.0004567986,0.00031265666,0.00058457913,0.0015513462,0.0006204028,0.0027239139],"category_scores_gemma":[0.000645989,0.0002709985,0.00037121872,0.00031432408,0.00016228842,0.00075064576,0.00061128783,0.00057683187,0.0011707759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011076092,0.00046646124,0.002897763,0.00017720816,0.00019829386,0.0003290668,0.00018395219,0.09766516,0.3251966,0.005629251,0.0053780577,0.56077063],"study_design_scores_gemma":[0.000023512275,0.0001688669,0.0007262082,0.000009767727,0.00004816912,0.000121950834,0.000013745569,0.94481987,0.050923437,0.00064728386,0.0024782564,0.000018947318],"about_ca_topic_score_codex":0.0033031036,"about_ca_topic_score_gemma":0.0041904794,"teacher_disagreement_score":0.0033031036,"about_ca_system_score_codex":0.0004938179,"about_ca_system_score_gemma":0.00058846176,"threshold_uncertainty_score":0.009112358},"labels":[],"label_agreement":null},{"id":"W2087678478","doi":"10.1109/ictai.2011.185","title":"Toward a Remote-Controlled Weapon-Equipped Camera Surveillance System","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Disadvantage; Public security; Computer security; Simple (philosophy); Security system; Control (management); Operations research; Artificial intelligence; Engineering","score_opus":0.058754091938292945,"score_gpt":0.26871919170481345,"score_spread":0.2099650997665205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087678478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049289268,0.00024303158,0.9438825,0.00020620316,0.000043847078,0.00018033365,0.000027442778,0.0010529819,0.0050743516],"genre_scores_gemma":[0.6345277,0.00034280453,0.35859808,0.0001548689,0.000056106124,0.00014068496,0.000081861566,0.00003187603,0.006065891],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999587,0.000101125195,0.000016306045,0.000108539105,0.00015440749,0.0000325612],"domain_scores_gemma":[0.99964905,0.00006534416,0.000061454455,0.000027232174,0.0001534312,0.000043427714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005196968,0.0005055563,0.0004341425,0.00023691812,0.00028322806,0.00076859136,0.0010044402,0.00085557334,0.0012365786],"category_scores_gemma":[0.0006744652,0.00020091124,0.00029622923,0.00017911385,0.00031040833,0.00076680887,0.00060178345,0.00068009266,0.0005251254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093607564,0.0007356169,0.004902873,0.00045707583,0.00011022431,0.0009595072,0.0004233683,0.21429782,0.36879152,0.02932277,0.0044981316,0.37456512],"study_design_scores_gemma":[0.00007395488,0.0007188665,0.0011340072,0.000030902454,0.000046146022,0.0002966388,0.000044114175,0.95640606,0.03436562,0.0014687123,0.0053806636,0.00003433038],"about_ca_topic_score_codex":0.0026753766,"about_ca_topic_score_gemma":0.002174721,"teacher_disagreement_score":0.0026753766,"about_ca_system_score_codex":0.00044108808,"about_ca_system_score_gemma":0.00068397186,"threshold_uncertainty_score":0.0053195357},"labels":[],"label_agreement":null},{"id":"W2087797322","doi":"10.1109/btas.2008.4699372","title":"A Novel Appearance Model and Adaptive Condensation Algorithm for Human Face Tracking","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Tangent space; Cascade; Computer science; Artificial intelligence; Adaptive sampling; Context (archaeology); Facial motion capture; Face (sociological concept); Affine transformation; Computer vision; Tracking (education); Sampling (signal processing); Tangent; Algorithm; Facial recognition system; Face detection; Mathematics; Pattern recognition (psychology); Filter (signal processing); Geometry","score_opus":0.11380355498594079,"score_gpt":0.3234157251169136,"score_spread":0.20961217013097277,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2087797322","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":[0.00089475996,0.000049349972,0.9986212,0.000017335668,0.000016579128,0.00001293813,0.000008861794,0.00021810703,0.00016092882],"genre_scores_gemma":[0.09864533,0.00029234297,0.8971742,0.000111245055,0.00010727061,0.00018162275,0.00019521861,0.00022761787,0.0030651835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939513,0.00009814885,0.000022474755,0.00018962579,0.00025086143,0.000043621418],"domain_scores_gemma":[0.99950993,0.00013204252,0.00005941013,0.000094614355,0.00016985994,0.00003409968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008669606,0.0007405977,0.0010536857,0.00089248596,0.00040038314,0.0006893175,0.002085609,0.001057903,0.001605128],"category_scores_gemma":[0.0021965464,0.00054073916,0.0010377112,0.0011017539,0.0007421174,0.0016721173,0.00109404,0.0011988009,0.0010623003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017459004,0.00009021551,0.00089176546,0.000115098126,0.00010995505,0.0001281166,0.00018311385,0.33792102,0.04947006,0.025600314,0.0055432715,0.5797724],"study_design_scores_gemma":[0.000007324839,0.000025568637,0.00013874668,0.000003543963,0.000009468348,0.00005302681,0.0000045468464,0.99275595,0.0032239698,0.0020962034,0.0016714073,0.000010181059],"about_ca_topic_score_codex":0.004829593,"about_ca_topic_score_gemma":0.0040041935,"teacher_disagreement_score":0.004829593,"about_ca_system_score_codex":0.0008871132,"about_ca_system_score_gemma":0.0008940128,"threshold_uncertainty_score":0.009602964},"labels":[],"label_agreement":null},{"id":"W2091185747","doi":"10.1117/12.884402","title":"A system for airport surveillance: detection of people running, abandoned objects, and pointing gestures","year":2011,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"Compute Canada; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Computer science; Artificial intelligence; Object detection; Computer vision; Codebook; Segmentation; Set (abstract data type); Gesture; Object (grammar); Mixture model; Term (time); Outlier; Metric (unit); Pattern recognition (psychology)","score_opus":0.014519248804926391,"score_gpt":0.23152051181678218,"score_spread":0.2170012630118558,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2091185747","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3861221,0.0019439611,0.4543156,0.00057265884,0.0013172774,0.0020844473,0.0152604105,0.118594885,0.01978867],"genre_scores_gemma":[0.6607127,0.0004788622,0.30529484,0.00052876846,0.00023810947,0.00088430865,0.018046435,0.0005520447,0.013263934],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995017,0.000051681356,0.000022782959,0.0002141671,0.00011114276,0.000098497265],"domain_scores_gemma":[0.9996395,0.000039356888,0.000024104198,0.0000738967,0.00012785936,0.00009527942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056513696,0.0011225273,0.0010448517,0.0013414065,0.0006567351,0.0006940554,0.00084558816,0.0013568676,0.004980577],"category_scores_gemma":[0.0006055589,0.00030854333,0.0005506625,0.0006096491,0.00020483398,0.00058416196,0.0007880522,0.00077354786,0.0042285607],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00192801,0.0008572357,0.017050853,0.00066738063,0.0003675822,0.0013466388,0.00039037157,0.004124504,0.35036805,0.0014336547,0.07321202,0.54825383],"study_design_scores_gemma":[0.00078649574,0.0040705563,0.18783,0.00030800496,0.0006214545,0.0074985195,0.0007398174,0.39593595,0.32485187,0.0021476636,0.07476925,0.00044038755],"about_ca_topic_score_codex":0.0053938073,"about_ca_topic_score_gemma":0.009603409,"teacher_disagreement_score":0.0053938073,"about_ca_system_score_codex":0.0004824088,"about_ca_system_score_gemma":0.0005553759,"threshold_uncertainty_score":0.016661644},"labels":[],"label_agreement":null},{"id":"W2094641529","doi":"10.1109/crv.2010.43","title":"Deformable Object Segmentation and Contour Tracking in Image Sequences Using Unsupervised Networks","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Cluster analysis; Segmentation; Frame (networking); Image segmentation; Object (grammar); Tracking (education); Pixel; Pattern recognition (psychology); Video tracking","score_opus":0.027924425240358375,"score_gpt":0.3145650479120968,"score_spread":0.2866406226717384,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2094641529","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024942206,0.00015172601,0.973944,0.000050253235,0.000011566613,0.000031220923,0.000017694629,0.00029473926,0.00055664557],"genre_scores_gemma":[0.3736929,0.0003316316,0.62235963,0.00006759657,0.00005931266,0.00014057601,0.0001542242,0.00009045506,0.0031037128],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997471,0.00005767338,0.000012201976,0.00009763689,0.00006372438,0.00002161494],"domain_scores_gemma":[0.99939203,0.00028974182,0.00013551321,0.000073352014,0.00008903656,0.000020378056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005481795,0.0005288641,0.00041264406,0.00069708016,0.00029029447,0.00043830584,0.00073009933,0.0007147916,0.00054988475],"category_scores_gemma":[0.0014927755,0.00032744597,0.0004604839,0.00072481023,0.00067879475,0.00090275414,0.0003746188,0.00052110234,0.00019077078],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017008433,0.00009476085,0.0017556483,0.00009038764,0.00008038615,0.00013896555,0.00019127145,0.63149035,0.04474878,0.007939292,0.00058204687,0.312718],"study_design_scores_gemma":[0.0000027842816,0.00002045415,0.0005192794,0.000003989562,0.0000056882172,0.00002719619,0.0000075345993,0.9923471,0.004692578,0.0019838698,0.00038461416,0.0000049408914],"about_ca_topic_score_codex":0.004614747,"about_ca_topic_score_gemma":0.005382142,"teacher_disagreement_score":0.004614747,"about_ca_system_score_codex":0.0007738785,"about_ca_system_score_gemma":0.0003999073,"threshold_uncertainty_score":0.009175777},"labels":[],"label_agreement":null},{"id":"W2095275517","doi":"10.1142/s0218001403002563","title":"ACTIVE HEAD TRACKING BASED ON CHROMATIC SHAPE FITTING","year":2003,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University; BC Innovation Council","funders":"","keywords":"Computer vision; Artificial intelligence; Ellipse; Computer science; Chromatic scale; Centroid; Saccade; Tracking (education); Position (finance); Orientation (vector space); Foveal; Mathematics; Eye movement; Geometry","score_opus":0.1488864731902557,"score_gpt":0.36640022337850714,"score_spread":0.21751375018825145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095275517","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021041414,0.00013854091,0.97636414,0.000040058738,0.00003792819,0.000028942492,0.00002298895,0.0009400025,0.0013859629],"genre_scores_gemma":[0.42926878,0.00033742937,0.5637049,0.000076111864,0.000087060784,0.00008827498,0.00015088545,0.00016373556,0.0061228108],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997328,0.00004377583,0.000009178153,0.00006871107,0.00012315404,0.00002242126],"domain_scores_gemma":[0.9994849,0.00017151536,0.000052770953,0.00008514904,0.00016910427,0.00003652539],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003434478,0.0003825154,0.00041017914,0.00071264664,0.00029528548,0.0005296249,0.0010349449,0.00045748905,0.0012190429],"category_scores_gemma":[0.001033145,0.00034984527,0.00045633785,0.00050625554,0.0003785742,0.00065996015,0.0006009821,0.00043741238,0.00061624637],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056713686,0.00011446317,0.0020421806,0.00012542939,0.000088593624,0.0001500612,0.00036477478,0.043102898,0.26529387,0.008249499,0.0019283572,0.6779728],"study_design_scores_gemma":[0.000058185953,0.00026210025,0.0029786932,0.000020430274,0.00006797154,0.00051150424,0.000057643912,0.8485262,0.13705616,0.0025658184,0.007828858,0.00006642295],"about_ca_topic_score_codex":0.0023584182,"about_ca_topic_score_gemma":0.0029334326,"teacher_disagreement_score":0.0023584182,"about_ca_system_score_codex":0.00038778174,"about_ca_system_score_gemma":0.0003842166,"threshold_uncertainty_score":0.0046893954},"labels":[],"label_agreement":null},{"id":"W2095317928","doi":"10.1109/itsc.2012.6338783","title":"Multiresolution based sigma-delta for motion segmentation","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"AUTO21 Network of Centres of Excellence; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Segmentation; Artificial intelligence; Computer science; Computer vision; Multiresolution analysis; Wavelet; Motion (physics); Image segmentation; Delta; Image resolution; Pattern recognition (psychology); Wavelet transform; Discrete wavelet transform; Engineering","score_opus":0.0558967396609147,"score_gpt":0.3342889829747292,"score_spread":0.2783922433138145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2095317928","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0068826685,0.00035602917,0.99171233,0.000032137526,0.000021608648,0.000018094,0.00003472662,0.00039502096,0.00054731156],"genre_scores_gemma":[0.16247797,0.00075145473,0.8345705,0.00006369806,0.000035854908,0.00004183329,0.00022186716,0.00012748795,0.0017092995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977845,0.00003659086,0.0000145117765,0.000049119775,0.00009726301,0.000024003328],"domain_scores_gemma":[0.99980384,0.00004387888,0.000026733876,0.00003906131,0.00007354212,0.000013062843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003882378,0.00055725273,0.0004958575,0.0011828103,0.00022668624,0.000608346,0.0006563347,0.0005588255,0.0017575567],"category_scores_gemma":[0.0007483652,0.00025726657,0.0006437111,0.00085618655,0.0002712239,0.00070953113,0.00046141096,0.00058923045,0.0008857846],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019725302,0.000067258945,0.0011511078,0.00027824403,0.000067291665,0.00020019105,0.00012878045,0.06994818,0.20587246,0.015904108,0.0018648978,0.7043202],"study_design_scores_gemma":[0.000013474916,0.00011551561,0.0017617077,0.00003788509,0.000046260986,0.00038181077,0.000049227376,0.9178766,0.06420093,0.005272902,0.010204645,0.000039028426],"about_ca_topic_score_codex":0.0012250481,"about_ca_topic_score_gemma":0.0020467385,"teacher_disagreement_score":0.0017575567,"about_ca_system_score_codex":0.00030819347,"about_ca_system_score_gemma":0.0004105872,"threshold_uncertainty_score":0.0058796406},"labels":[],"label_agreement":null},{"id":"W2097927395","doi":"10.1109/crv.2009.23","title":"Efficient Target Recovery Using STAGE for Mean-shift Tracking","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bhattacharyya distance; Mean-shift; Robustness (evolution); Computer science; Tracking (education); Artificial intelligence; Maximization; Algorithm; Computer vision; Pattern recognition (psychology); Mathematics; Mathematical optimization","score_opus":0.05718197967137931,"score_gpt":0.33082929625628343,"score_spread":0.27364731658490415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097927395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0093057705,0.000059401584,0.9898949,0.000024999044,0.000010159853,0.000018155028,0.000010048981,0.000337251,0.00033931213],"genre_scores_gemma":[0.3296951,0.00014204337,0.6664377,0.000052836276,0.00002042114,0.00013574802,0.00013581278,0.00012740119,0.003252894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961144,0.00006718987,0.000020229661,0.00009882273,0.00016656541,0.000035744015],"domain_scores_gemma":[0.9995121,0.00019033831,0.000056861143,0.000095548334,0.00012402204,0.000021177664],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000661762,0.0005174468,0.0008398852,0.0005223933,0.0003888923,0.0005291485,0.0011014679,0.00080911996,0.0013434297],"category_scores_gemma":[0.0019491888,0.00046648085,0.00079273136,0.0005360132,0.00043696936,0.000869459,0.0009719547,0.0007651373,0.0005680264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045704262,0.00012794098,0.0017089336,0.00014958884,0.0001252095,0.00012963405,0.00018811307,0.42376256,0.09146882,0.017162228,0.0020703315,0.46264964],"study_design_scores_gemma":[0.000014500843,0.00007647192,0.0003058454,0.0000035228413,0.00001086867,0.000057334502,0.00000461896,0.98595876,0.010421121,0.0022664852,0.00086248404,0.000018020482],"about_ca_topic_score_codex":0.00250163,"about_ca_topic_score_gemma":0.0031282355,"teacher_disagreement_score":0.00250163,"about_ca_system_score_codex":0.00047449366,"about_ca_system_score_gemma":0.0010151753,"threshold_uncertainty_score":0.004974067},"labels":[],"label_agreement":null},{"id":"W2097973940","doi":"10.1109/cvprw.2010.5543510","title":"Feedback scheme for thermal-visible video registration, sensor fusion, and people tracking","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer vision; Artificial intelligence; RANSAC; Computer science; Tracking (education); Trajectory; Affine transformation; Video tracking; Sensor fusion; Transformation (genetics); Matching (statistics); Geometric transformation; Image registration; Pixel; Fusion; Object (grammar); Image (mathematics); Mathematics","score_opus":0.02357341140208377,"score_gpt":0.2900518469937685,"score_spread":0.2664784355916847,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2097973940","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":[0.005966904,0.00009581337,0.993028,0.00004299017,0.000052290874,0.000032352913,0.000010927551,0.000502211,0.0002685218],"genre_scores_gemma":[0.5370997,0.00018354316,0.45832044,0.00017597823,0.00013711603,0.00025540564,0.00008307108,0.000093774,0.0036509384],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99861956,0.0002515214,0.00007585519,0.00036325725,0.0005890941,0.00010062827],"domain_scores_gemma":[0.99860257,0.00044240657,0.00021469168,0.00023684968,0.0004223837,0.00008123311],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014468314,0.00083709473,0.0008494452,0.0005040673,0.0007244046,0.00048122043,0.0019435514,0.001203623,0.002355307],"category_scores_gemma":[0.0036140159,0.00043661526,0.00045590915,0.00046132985,0.00073711236,0.0016160482,0.001345442,0.0009981523,0.00063522754],"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":[0.0011825785,0.00037223994,0.0009478228,0.00029963025,0.0000930024,0.00025933134,0.00055135717,0.13307442,0.17233594,0.0142764095,0.0037940152,0.67281324],"study_design_scores_gemma":[0.00008590428,0.00037360963,0.00059787516,0.000017008113,0.000032652388,0.00020836879,0.000032662538,0.9468109,0.044826884,0.0038132155,0.0031439476,0.000056905566],"about_ca_topic_score_codex":0.002094824,"about_ca_topic_score_gemma":0.0026548682,"teacher_disagreement_score":0.002355307,"about_ca_system_score_codex":0.00067817396,"about_ca_system_score_gemma":0.0006406863,"threshold_uncertainty_score":0.007879317},"labels":[],"label_agreement":null},{"id":"W2098676327","doi":"10.1109/cvpr.2005.333","title":"Statistical Cue Integration for Foveated Wide-Field Surveillance","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial intelligence; Background subtraction; Computer vision; Probabilistic logic; Field (mathematics); Heuristic; Bayesian probability; Pattern recognition (psychology); Pixel; Mathematics","score_opus":0.023432697151730544,"score_gpt":0.32163925357976847,"score_spread":0.29820655642803795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098676327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025521828,0.00018453565,0.9731073,0.000056559715,0.000012751708,0.00002129786,0.00003286162,0.0005925172,0.00047036196],"genre_scores_gemma":[0.46542898,0.0002444162,0.5326487,0.000102269245,0.0000501537,0.00006466128,0.0001641454,0.00016938806,0.0011273875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940157,0.00013261565,0.000021404794,0.00012757463,0.00026121302,0.00005554777],"domain_scores_gemma":[0.9990508,0.00034642164,0.00012447576,0.00012268122,0.00028770283,0.00006790502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009163098,0.00043350898,0.0006784717,0.0007120605,0.00026775736,0.00059301005,0.00093403074,0.00042164983,0.0010434113],"category_scores_gemma":[0.0034989517,0.0004076924,0.00039002043,0.000491459,0.00054045464,0.0009480213,0.00089696026,0.000592276,0.00033522418],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007346666,0.00016847684,0.0020167355,0.00017049011,0.000121267985,0.00013992484,0.00016264046,0.20251922,0.21699937,0.015663179,0.001975731,0.5593284],"study_design_scores_gemma":[0.000015136766,0.00009317317,0.0017671098,0.0000071952622,0.000018263598,0.000105322,0.000013751486,0.9691806,0.02233923,0.005386557,0.0010468451,0.000026771731],"about_ca_topic_score_codex":0.0027827662,"about_ca_topic_score_gemma":0.004537021,"teacher_disagreement_score":0.0027827662,"about_ca_system_score_codex":0.00071897433,"about_ca_system_score_gemma":0.0009156801,"threshold_uncertainty_score":0.005533159},"labels":[],"label_agreement":null},{"id":"W2098896964","doi":"10.5555/1769087.1769088","title":"Distributed coalition formation in visual sensor networks: a virtual vision approach","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Scalability; Node (physics); Wireless sensor network; Bidding; Visual sensor network; Artificial intelligence; Real-time computing; Distributed computing; Computer network; Key distribution in wireless sensor networks; Engineering","score_opus":0.019481174911032198,"score_gpt":0.31176325797595794,"score_spread":0.29228208306492576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2098896964","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":[0.008902113,0.00017128933,0.9870827,0.00024611596,0.000033820153,0.000035389243,0.000012182096,0.000097026976,0.0034192964],"genre_scores_gemma":[0.69620436,0.00029550877,0.2981856,0.00020339541,0.00005571227,0.00018817493,0.000068791865,0.000059816335,0.004738735],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991862,0.00039041598,0.000024756979,0.00013569853,0.00017486677,0.0000880063],"domain_scores_gemma":[0.9990632,0.00041294735,0.00011247838,0.00013633749,0.00014092997,0.00013419165],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011867998,0.0004765361,0.00064836495,0.0007051256,0.0007135257,0.0014106964,0.0017562992,0.00096667506,0.0015803605],"category_scores_gemma":[0.0021570974,0.00034391077,0.0007145597,0.0005722389,0.001447643,0.0016671458,0.0019380769,0.00090366416,0.00022193741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008524607,0.00008332735,0.0005382361,0.00007747374,0.000060418948,0.00015674444,0.00022607732,0.77600276,0.004743584,0.16426767,0.0017248631,0.0520336],"study_design_scores_gemma":[0.000016079024,0.000024485458,0.000050366634,0.0000057993125,0.0000062155027,0.000030219651,0.00004619408,0.9717549,0.00079588755,0.025401244,0.0018618462,0.000006760777],"about_ca_topic_score_codex":0.0032322432,"about_ca_topic_score_gemma":0.0031044038,"teacher_disagreement_score":0.0032322432,"about_ca_system_score_codex":0.0013103386,"about_ca_system_score_gemma":0.0012047356,"threshold_uncertainty_score":0.009507179},"labels":[],"label_agreement":null},{"id":"W2099438908","doi":"10.1109/tpami.2008.150","title":"Learning to Detect Moving Shadows in Dynamic Environments","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":86,"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":"Canadian Institute for Advanced Research; National Science Foundation","keywords":"Computer science; Artificial intelligence; Exploit; Set (abstract data type); Computer vision; Feature vector; Feature (linguistics); Pattern recognition (psychology); Machine learning","score_opus":0.020228443121420716,"score_gpt":0.28013508852401753,"score_spread":0.25990664540259684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2099438908","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06615252,0.00014441164,0.9320868,0.0000738063,0.00003058254,0.00003203761,0.000037416987,0.0007160094,0.00072640565],"genre_scores_gemma":[0.70837826,0.00021051095,0.28943154,0.000120334145,0.00012366228,0.000078540834,0.00023574465,0.00007450932,0.0013470008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958116,0.00006707484,0.000019236499,0.00013968683,0.00013031562,0.00006251538],"domain_scores_gemma":[0.99889356,0.0004127011,0.00021227535,0.00015662012,0.00025732687,0.00006746511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005839151,0.0006029719,0.00073945563,0.0010336131,0.0003309459,0.0004970211,0.0009994803,0.000569518,0.0005330599],"category_scores_gemma":[0.0024329766,0.00040218356,0.0004181012,0.00069107773,0.00057549967,0.0009667732,0.00080077385,0.00072410016,0.00031866782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021424406,0.00028777833,0.010369588,0.00011943064,0.00012175798,0.00021919384,0.0002756274,0.22154544,0.048728187,0.0024493742,0.0023910608,0.71327823],"study_design_scores_gemma":[0.000011301809,0.000081704675,0.002860322,0.000005274694,0.000014681315,0.00011680228,0.000041876934,0.9861785,0.0077312877,0.0021626896,0.0007815778,0.000013930406],"about_ca_topic_score_codex":0.0014928408,"about_ca_topic_score_gemma":0.002180878,"teacher_disagreement_score":0.0014928408,"about_ca_system_score_codex":0.00021513518,"about_ca_system_score_gemma":0.00045938976,"threshold_uncertainty_score":0.003088057},"labels":[],"label_agreement":null},{"id":"W2100291131","doi":"10.1109/icsmc.2007.4414038","title":"Real-time automatic detection of vandalism behavior in video sequences","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Government of Canada; Concordia University","funders":"","keywords":"Computer science; Graffiti; Computer vision; Artificial intelligence; Object detection; Object (grammar); Feature extraction; Sequence (biology); Phone; Video tracking; Pattern recognition (psychology)","score_opus":0.020405242568120565,"score_gpt":0.31131386597851024,"score_spread":0.2909086234103897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2100291131","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17989983,0.0013852244,0.8066605,0.00015070714,0.0002333143,0.0003105544,0.00068748934,0.0059210067,0.004751421],"genre_scores_gemma":[0.6238159,0.0007831588,0.37085474,0.00007968892,0.00017684331,0.00017425806,0.0012193562,0.0001505533,0.0027455408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927455,0.00011696457,0.000045068966,0.00016322486,0.00034187845,0.00005837132],"domain_scores_gemma":[0.9982545,0.00047976835,0.00043349207,0.00014139291,0.0005863895,0.00010435289],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005151331,0.0006618501,0.00062758656,0.0023930739,0.00022914767,0.0005863187,0.0007441963,0.00057371805,0.00081861083],"category_scores_gemma":[0.0029340254,0.00026856654,0.0002552509,0.000593526,0.00028903934,0.00085293065,0.00035969654,0.0005405505,0.0006437439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044065542,0.00017282451,0.007663306,0.00047650983,0.00009127984,0.0003306087,0.00031064387,0.0057269423,0.2918513,0.0010847843,0.0034742404,0.68837684],"study_design_scores_gemma":[0.00010119348,0.0009764523,0.08225094,0.00015593538,0.00013708229,0.0046063103,0.0003711927,0.55846643,0.32917285,0.0023844182,0.021207986,0.00016929371],"about_ca_topic_score_codex":0.0011832888,"about_ca_topic_score_gemma":0.0015616005,"teacher_disagreement_score":0.0023930739,"about_ca_system_score_codex":0.0002339555,"about_ca_system_score_gemma":0.00030045243,"threshold_uncertainty_score":0.0027385354},"labels":[],"label_agreement":null},{"id":"W2101041618","doi":"10.1109/icme.2011.6012076","title":"Dynamic workload assignment in video surveillance systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Workload; Computer science; Real-time computing; Task (project management); Cloud computing; Function (biology); Work (physics); Operating system; Engineering","score_opus":0.035910949448331106,"score_gpt":0.2682899923151548,"score_spread":0.2323790428668237,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101041618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34846136,0.0006935043,0.64605147,0.00046450886,0.00011923238,0.00025976743,0.00019421554,0.0008235591,0.002932384],"genre_scores_gemma":[0.98041266,0.0001502338,0.01800687,0.00004771737,0.00004368169,0.00009764588,0.00009298094,0.00006008851,0.0010880711],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974443,0.00088451576,0.00014515521,0.000632065,0.0004844602,0.00040951793],"domain_scores_gemma":[0.9963437,0.0016403828,0.0006191298,0.00036878494,0.0006922917,0.00033563524],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002050412,0.001212313,0.0009857335,0.00074139703,0.0006942188,0.0014710305,0.001835909,0.00084684126,0.00086372305],"category_scores_gemma":[0.00912973,0.00061503914,0.00033297198,0.0010305247,0.0006293887,0.0018548031,0.00071177783,0.00067218527,0.00028869972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034136415,0.00015311001,0.004553493,0.000079783866,0.000034256052,0.000210735,0.00023481737,0.9456895,0.0067104804,0.0049561234,0.0013145846,0.035721775],"study_design_scores_gemma":[0.000005192187,0.000021952474,0.0005701129,0.0000027691212,0.000004122093,0.00002675347,0.000023321998,0.99748874,0.0004021516,0.0012690743,0.00018051024,0.0000053520716],"about_ca_topic_score_codex":0.0068791076,"about_ca_topic_score_gemma":0.0039402456,"teacher_disagreement_score":0.0068791076,"about_ca_system_score_codex":0.0016451073,"about_ca_system_score_gemma":0.0009561422,"threshold_uncertainty_score":0.0136781335},"labels":[],"label_agreement":null},{"id":"W2101705628","doi":"10.1109/cvprw.2014.126","title":"CDnet 2014: An Expanded Change Detection Benchmark Dataset","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":76,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Benchmark (surveying); Computer science; Change detection; Artificial intelligence; Geology","score_opus":0.051263303798128754,"score_gpt":0.31692334123533067,"score_spread":0.2656600374372019,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101705628","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07049917,0.0056145038,0.023885,0.0013446432,0.0020610057,0.0014154012,0.8593891,0.020202545,0.015588586],"genre_scores_gemma":[0.02309034,0.0005394739,0.017822007,0.00024313225,0.00014513328,0.00038710271,0.9552466,0.00037595374,0.0021503328],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99755704,0.00026904923,0.00026624053,0.00071892346,0.00089253,0.00029627664],"domain_scores_gemma":[0.9975992,0.00040067206,0.00028026718,0.00052898604,0.0009480367,0.00024284914],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014383032,0.0033688722,0.0016487129,0.006579033,0.0014215492,0.0018530901,0.00374338,0.0026925448,0.0047676773],"category_scores_gemma":[0.0057582697,0.0005151716,0.0015223107,0.0053758714,0.0006284689,0.0023078355,0.0018004373,0.0022224942,0.0052610943],"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":[0.00065278774,0.0006992255,0.012038187,0.0015707307,0.00029927047,0.00046024943,0.00013019386,0.010830963,0.0065926337,0.00185415,0.8473098,0.11756174],"study_design_scores_gemma":[0.0005757526,0.0006853954,0.07563835,0.00078342156,0.00036963256,0.0022271934,0.0007453258,0.20168488,0.022928214,0.0075661987,0.6864886,0.00030701884],"about_ca_topic_score_codex":0.046498444,"about_ca_topic_score_gemma":0.081032865,"teacher_disagreement_score":0.046498444,"about_ca_system_score_codex":0.0021758606,"about_ca_system_score_gemma":0.001937731,"threshold_uncertainty_score":0.092455566},"labels":[],"label_agreement":null},{"id":"W2101956459","doi":"10.1145/2557642.2563678","title":"YawDD","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":270,"is_retracted":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; Artificial intelligence; Computer vision; Dash; Video camera; Benchmark (surveying); Front (military); Computer graphics (images); Engineering","score_opus":0.011986006007393132,"score_gpt":0.2561424033236177,"score_spread":0.24415639731622457,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2101956459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.091219224,0.008024365,0.5467531,0.0010980513,0.0050118887,0.00096610776,0.09402981,0.05632823,0.19656928],"genre_scores_gemma":[0.41983113,0.004853376,0.18528679,0.0017207188,0.00080239144,0.00074161676,0.21534105,0.004658523,0.16676441],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994019,0.000045751403,0.00003823665,0.0002940833,0.00014420414,0.000075780656],"domain_scores_gemma":[0.9995993,0.00005080051,0.000039868533,0.00012907786,0.0001547883,0.000026135951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042214576,0.0014710608,0.0007621861,0.0011060117,0.00052824547,0.0015543033,0.0009729892,0.00080722815,0.051212702],"category_scores_gemma":[0.0013735128,0.0003990866,0.00074664596,0.0007294929,0.00022539722,0.0016110996,0.0011668424,0.0006569956,0.035383966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001155424,0.00015648155,0.015401417,0.0011209766,0.00017300787,0.00066415756,0.00027135565,0.013487816,0.02658255,0.010410844,0.19747703,0.733099],"study_design_scores_gemma":[0.00014720179,0.00045300715,0.025652315,0.00031311298,0.00016513905,0.0023867053,0.0005891986,0.120675586,0.04945849,0.012025929,0.78794307,0.00019021949],"about_ca_topic_score_codex":0.0034656508,"about_ca_topic_score_gemma":0.004048241,"teacher_disagreement_score":0.051212702,"about_ca_system_score_codex":0.00035455448,"about_ca_system_score_gemma":0.00036490004,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W2102039033","doi":"10.1109/tvcg.2011.34","title":"A Framework for 3D Model-Based Visual Tracking Using a GPU-Accelerated Particle Filter","year":2011,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; McMaster University; Nvidia","keywords":"Computer science; Particle filter; CUDA; Rendering (computer graphics); Graphics processing unit; Computer vision; Frame rate; Massively parallel; General-purpose computing on graphics processing units; Artificial intelligence; Video tracking; Tracking (education); Computer graphics (images); Graphics; Filter (signal processing); Video processing; Parallel computing","score_opus":0.14597032912993718,"score_gpt":0.36433341298156297,"score_spread":0.21836308385162578,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102039033","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":[0.00017481446,0.000024400395,0.99907494,0.000011675539,0.000013332103,0.000012999887,0.000010589607,0.00043675135,0.00024044623],"genre_scores_gemma":[0.018114509,0.00014854422,0.97978413,0.000029535615,0.000028914337,0.00013179581,0.00010772485,0.00018932518,0.001465596],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953294,0.000052039562,0.000023133194,0.000086752676,0.0002687735,0.00003639647],"domain_scores_gemma":[0.99963474,0.000085963344,0.000031558076,0.00007471446,0.0001355602,0.0000373435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000628805,0.0008637165,0.00089438935,0.0009874156,0.0006583262,0.0013727034,0.0021375846,0.0013955986,0.0030781194],"category_scores_gemma":[0.001475364,0.0006796829,0.0013460648,0.00090891955,0.0005786877,0.0009246877,0.0012913457,0.0016900708,0.0018668754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000086421205,0.0001261346,0.00069272326,0.00023104124,0.00013005245,0.0003115889,0.00023361246,0.5044113,0.041801013,0.07701458,0.009921624,0.3650399],"study_design_scores_gemma":[0.000012891722,0.000022472243,0.00011040885,0.000012183459,0.000010582407,0.000090127774,0.000008392501,0.98055387,0.0035284248,0.005422111,0.010209661,0.000018891034],"about_ca_topic_score_codex":0.012773948,"about_ca_topic_score_gemma":0.0104483245,"teacher_disagreement_score":0.012773948,"about_ca_system_score_codex":0.000840277,"about_ca_system_score_gemma":0.0017292894,"threshold_uncertainty_score":0.025399208},"labels":[],"label_agreement":null},{"id":"W2102061963","doi":"10.1109/crv.2009.28","title":"A Multiple Hypothesis Tracking Method with Fragmentation Handling","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Background subtraction; Computer science; Artificial intelligence; Computer vision; Video tracking; Fragmentation (computing); Frame (networking); Subtraction; Tracking (education); Graph; Object (grammar); Imperfect; Pattern recognition (psychology); Theoretical computer science; Mathematics; Pixel","score_opus":0.03888395120321732,"score_gpt":0.3053135947662131,"score_spread":0.2664296435629958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2102061963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036672982,0.00010826286,0.9952551,0.000048370934,0.00004616991,0.000037905746,0.00002982856,0.00049905275,0.0003081226],"genre_scores_gemma":[0.1273864,0.00017547786,0.86893547,0.00011797013,0.00012539774,0.00014725525,0.00032601983,0.0002135648,0.0025725712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984384,0.00030126912,0.00008011101,0.0005616502,0.0005345032,0.00008405175],"domain_scores_gemma":[0.9971392,0.0012742454,0.00030028747,0.0005846405,0.00055345765,0.00014824506],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020451234,0.0007545875,0.0010048758,0.0018275647,0.000710561,0.0009933951,0.0022836828,0.0015390979,0.003355459],"category_scores_gemma":[0.0049071256,0.0005771041,0.0012081272,0.0011950595,0.00073717383,0.002737606,0.0014133912,0.0011051786,0.0011624858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033556134,0.00014390745,0.0027009593,0.00017639295,0.00018017122,0.00040071816,0.00024592195,0.08394142,0.024365602,0.007943825,0.004014809,0.87555087],"study_design_scores_gemma":[0.000080652404,0.00019002627,0.0013966361,0.000021360838,0.000077100696,0.00054098846,0.00003748858,0.9693412,0.01591419,0.0058625173,0.006479392,0.000058430734],"about_ca_topic_score_codex":0.0023419613,"about_ca_topic_score_gemma":0.0016396116,"teacher_disagreement_score":0.003355459,"about_ca_system_score_codex":0.00060179696,"about_ca_system_score_gemma":0.0010062222,"threshold_uncertainty_score":0.011225164},"labels":[],"label_agreement":null},{"id":"W2103198845","doi":"10.1007/978-3-642-12307-8_16","title":"Vehicle Headlights Detection Using Markov Random Fields","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Reflection (computer programming); Computer vision; Gaussian; Markov chain; Blob detection; Iterated function; Markov random field; Markov process; Pattern recognition (psychology); Image (mathematics); Image processing; Edge detection; Image segmentation; Machine learning; Mathematics; Physics; Statistics","score_opus":0.021776555399991154,"score_gpt":0.2751182808512244,"score_spread":0.25334172545123324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103198845","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03846439,0.0005045932,0.95802164,0.00008720297,0.000065916036,0.00003220706,0.000113681315,0.0011158629,0.0015944763],"genre_scores_gemma":[0.7846703,0.0005954641,0.20837554,0.000118789176,0.000093675146,0.000046234913,0.00050962146,0.00009959872,0.005490781],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963796,0.00006223559,0.000010952744,0.000098414166,0.00010872708,0.00008167758],"domain_scores_gemma":[0.9993913,0.00032651916,0.00005985688,0.00006353092,0.00012744771,0.00003130606],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064666354,0.00048874755,0.0010336413,0.0013130358,0.00029566692,0.0006687119,0.00090702705,0.00073273585,0.0010393863],"category_scores_gemma":[0.0011984817,0.00055953837,0.00080916466,0.00083313585,0.00032280618,0.0007538408,0.0006959069,0.0006494416,0.00057787856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068686914,0.00018043548,0.0058248537,0.00015190452,0.00019052277,0.00021757907,0.00008100145,0.29315495,0.04049349,0.005831143,0.0044464553,0.64874077],"study_design_scores_gemma":[0.000007932897,0.000033807813,0.0011581418,0.000006649488,0.000017968725,0.00006921581,0.0000076284905,0.9910569,0.005356516,0.0017818736,0.0004904391,0.000012932086],"about_ca_topic_score_codex":0.005196571,"about_ca_topic_score_gemma":0.0054809535,"teacher_disagreement_score":0.005196571,"about_ca_system_score_codex":0.0005243934,"about_ca_system_score_gemma":0.0005793913,"threshold_uncertainty_score":0.010332644},"labels":[],"label_agreement":null},{"id":"W2103479636","doi":"10.1007/s11760-007-0021-8","title":"Active contours for video object tracking using region, boundary and shape information","year":2007,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer vision; Artificial intelligence; Tracking (education); Video tracking; Boundary (topology); Computer science; Energy minimization; Object (grammar); Frame (networking); Energy functional; Set (abstract data type); Pattern recognition (psychology); Mathematics","score_opus":0.03412687258548105,"score_gpt":0.32308382893346743,"score_spread":0.2889569563479864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2103479636","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018974288,0.00025281575,0.997393,0.000027266811,0.00002386394,0.000013275584,0.000011763545,0.00017418229,0.00020639338],"genre_scores_gemma":[0.10287729,0.00086222886,0.89321506,0.00008265395,0.00008447003,0.00014081126,0.00015355396,0.00020527834,0.002378625],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993494,0.00016542262,0.000037372523,0.00014804848,0.0002635379,0.00003616339],"domain_scores_gemma":[0.9987226,0.000717979,0.00010455926,0.00016190918,0.00025405464,0.000038907147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016639513,0.00093583605,0.0011823998,0.0016069387,0.00049084757,0.0013378685,0.0016929368,0.0017539656,0.0014798773],"category_scores_gemma":[0.0043353434,0.0008232857,0.0009931083,0.0019401697,0.0010846711,0.0022203024,0.001008104,0.0014464891,0.0008027362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004805094,0.00012352147,0.00046285466,0.00026086514,0.0001368051,0.00019342925,0.00027162817,0.13940503,0.07322133,0.03961634,0.0029044454,0.7429232],"study_design_scores_gemma":[0.000023757353,0.00004888141,0.00027367077,0.000018980832,0.00004509867,0.00011197831,0.00001871381,0.96610194,0.018885987,0.01115641,0.0032899468,0.000024710736],"about_ca_topic_score_codex":0.0019351587,"about_ca_topic_score_gemma":0.0016890842,"teacher_disagreement_score":0.0019351587,"about_ca_system_score_codex":0.0007136576,"about_ca_system_score_gemma":0.0005177423,"threshold_uncertainty_score":0.008799911},"labels":[],"label_agreement":null},{"id":"W2104356402","doi":"10.1109/cvpr.2004.1315220","title":"Variational mixture smoothing for non-linear dynamical systems","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Smoothing; Kalman filter; Maximum a posteriori estimation; Trajectory; Computer science; Algorithm; Linear dynamical system; Applied mathematics; Mathematical optimization; Mathematics; Linear system; Artificial intelligence; Computer vision","score_opus":0.02200294992386898,"score_gpt":0.2996070007091928,"score_spread":0.2776040507853238,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2104356402","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00056686625,0.000039531806,0.9991516,0.00002528005,0.0000071418485,0.000006156412,0.0000072025873,0.00008735489,0.000108926004],"genre_scores_gemma":[0.10495947,0.00031682267,0.89127433,0.000076616634,0.00006545536,0.00020938124,0.00020210065,0.00025447,0.0026414043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914324,0.00029644993,0.00004857857,0.0001475976,0.00030489248,0.000059150312],"domain_scores_gemma":[0.9967217,0.0024222815,0.00019252348,0.00023841637,0.0003372949,0.000087810666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025414,0.0009809504,0.0013498019,0.0011217662,0.00066511513,0.0012731211,0.0020589563,0.0015678424,0.0023748747],"category_scores_gemma":[0.010755043,0.0010150002,0.0016054002,0.0011653483,0.0012216008,0.0018715916,0.0018564484,0.0027759497,0.0008048406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000037446745,0.000021011312,0.00042316844,0.000071156515,0.00007139605,0.00004215349,0.00009892283,0.8670421,0.0012798252,0.08353661,0.00072002463,0.046656262],"study_design_scores_gemma":[0.0000022460122,0.000003397693,0.000032425483,0.0000026386115,0.0000026485116,0.000004886923,0.0000020807863,0.98746127,0.00013448644,0.011935963,0.00041393182,0.000003998542],"about_ca_topic_score_codex":0.011074458,"about_ca_topic_score_gemma":0.009401503,"teacher_disagreement_score":0.011074458,"about_ca_system_score_codex":0.001377219,"about_ca_system_score_gemma":0.0016324422,"threshold_uncertainty_score":0.022019982},"labels":[],"label_agreement":null},{"id":"W2106339446","doi":"10.1109/icassp.2008.4517714","title":"Performance evaluation for tracking algorithms using object labels","year":2008,"lang":"en","type":"article","venue":"Proceedings of the ... IEEE International Conference on Acoustics, Speech, and Signal Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Tracking (education); Video tracking; Object (grammar); Artificial intelligence; Algorithm; Computer vision; Machine learning; Object detection; Data mining; Pattern recognition (psychology)","score_opus":0.1448584783456764,"score_gpt":0.3548129219904209,"score_spread":0.20995444364474453,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2106339446","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":[0.18689397,0.0054511293,0.79180914,0.00040228217,0.0006214248,0.0005133886,0.00083663967,0.0063504316,0.0071215965],"genre_scores_gemma":[0.7208943,0.0010103952,0.27102742,0.00023142273,0.00026418368,0.00038101818,0.0030351891,0.00068109937,0.0024750012],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9815044,0.005952318,0.0018876564,0.002183495,0.007782819,0.00068938377],"domain_scores_gemma":[0.929569,0.048365343,0.0058365758,0.0043577612,0.01086372,0.0010076555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012776575,0.0019320393,0.0015442976,0.0045640594,0.0012812988,0.002946695,0.0019149983,0.0023842687,0.0013977041],"category_scores_gemma":[0.06422266,0.00033378878,0.0008988964,0.0030840337,0.0010186601,0.0039868364,0.0011739028,0.0014382517,0.0011874139],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0031561288,0.0011418224,0.041888356,0.0012362791,0.0008498159,0.0002001177,0.00044768036,0.2323755,0.044788603,0.006031329,0.007961055,0.6599233],"study_design_scores_gemma":[0.00010644421,0.002227071,0.02045361,0.00011731128,0.00024367833,0.00063422235,0.00028663434,0.8810023,0.085893,0.003363157,0.005444795,0.00022777026],"about_ca_topic_score_codex":0.0035787686,"about_ca_topic_score_gemma":0.0022450716,"teacher_disagreement_score":0.012776575,"about_ca_system_score_codex":0.002012218,"about_ca_system_score_gemma":0.0012889795,"threshold_uncertainty_score":0.06756973},"labels":[],"label_agreement":null},{"id":"W2107176413","doi":"10.1109/icip.2008.4712436","title":"Background estimation for microscopic cellular images","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Robustness (evolution); Image segmentation; Segmentation; Residual; Estimation; Tracking (education); Pattern recognition (psychology); Algorithm","score_opus":0.052360587372933094,"score_gpt":0.3161771584562538,"score_spread":0.26381657108332074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2107176413","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":[0.0075145145,0.000521684,0.99062705,0.000051081584,0.00002163533,0.00001366771,0.000042189702,0.0006779326,0.0005302446],"genre_scores_gemma":[0.28120333,0.0026405128,0.70936036,0.00013515292,0.000084445564,0.000084218686,0.0008498197,0.0005879555,0.0050542373],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996605,0.00005627027,0.000013526944,0.00008539399,0.00015082936,0.000033380962],"domain_scores_gemma":[0.99961865,0.00011925568,0.00005865077,0.00007030076,0.00010815288,0.000024984098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000519264,0.00066847994,0.00067976257,0.0013094853,0.0003666266,0.0009776726,0.0009001271,0.0007599019,0.0011294462],"category_scores_gemma":[0.0020620576,0.000401944,0.00058664515,0.0008492412,0.00042031994,0.0008274213,0.00070002564,0.00084729685,0.00082875916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023263493,0.000048013888,0.0019332722,0.000391708,0.00009817222,0.00032084645,0.00018572035,0.16576293,0.30299562,0.017370228,0.0039555198,0.50670534],"study_design_scores_gemma":[0.000011779822,0.00005464447,0.0020596832,0.000032336684,0.000039650273,0.0004113619,0.000054076445,0.89927936,0.07961768,0.0076967264,0.010708423,0.00003432214],"about_ca_topic_score_codex":0.0042061084,"about_ca_topic_score_gemma":0.003643876,"teacher_disagreement_score":0.0042061084,"about_ca_system_score_codex":0.0006202624,"about_ca_system_score_gemma":0.00049700023,"threshold_uncertainty_score":0.0083633065},"labels":[],"label_agreement":null},{"id":"W2108194865","doi":"10.1109/crv.2012.55","title":"A Metaheuristic Bat-Inspired Algorithm for Full Body Human Pose Estimation","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Metaheuristic; Particle swarm optimization; Ant colony optimization algorithms; Firefly algorithm; Parallel metaheuristic; Computer science; Mathematical optimization; Optimization problem; Particle filter; Multi-swarm optimization; Population; Algorithm; Filter (signal processing); Artificial intelligence; Mathematics; Computer vision","score_opus":0.03636191628798006,"score_gpt":0.3360088104941333,"score_spread":0.2996468942061532,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108194865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004358029,0.00029784004,0.99383813,0.00006968258,0.000051308685,0.000020214991,0.000015427628,0.00024156609,0.0011077487],"genre_scores_gemma":[0.17233057,0.00048336704,0.82302403,0.00017584603,0.000062141335,0.00018078445,0.00012635071,0.00011484117,0.0035020371],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998099,0.000053767493,0.000008403466,0.000037927584,0.00007580878,0.000014145497],"domain_scores_gemma":[0.999703,0.0001447837,0.000036193473,0.000022626087,0.00007792312,0.000015381785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046856518,0.0005699242,0.0007641303,0.00055306347,0.0003821314,0.0005237632,0.0007988772,0.001021545,0.0012763226],"category_scores_gemma":[0.0013489042,0.00029532262,0.00058828207,0.000635517,0.0003723829,0.0006029874,0.00042793935,0.0007189699,0.00047809127],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008533185,0.000054262473,0.00073285296,0.000112116555,0.000110067835,0.000090690555,0.000095477684,0.68479824,0.011945205,0.011231089,0.0028673261,0.2878773],"study_design_scores_gemma":[0.000007412075,0.000021437183,0.000099689256,0.000006089249,0.000006069189,0.000042398933,0.0000056876816,0.99650216,0.0008230728,0.001147289,0.0013330557,0.000005724304],"about_ca_topic_score_codex":0.0033784357,"about_ca_topic_score_gemma":0.0026180795,"teacher_disagreement_score":0.0033784357,"about_ca_system_score_codex":0.00035318514,"about_ca_system_score_gemma":0.00056540366,"threshold_uncertainty_score":0.006717503},"labels":[],"label_agreement":null},{"id":"W2108841919","doi":"10.1109/ccece.2003.1226111","title":"A novel zoom invariant video object tracking algorithm (ZIVOTA)","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Zoom; Computer vision; Affine transformation; Artificial intelligence; Affine shape adaptation; Invariant (physics); Video tracking; Computer science; Affine combination; Algorithm; Object (grammar); Mathematics; Geometry","score_opus":0.03444978283359156,"score_gpt":0.2857319413818838,"score_spread":0.2512821585482922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2108841919","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":[0.0026192209,0.00032163088,0.9949827,0.00003716744,0.0000701528,0.000046106987,0.000049602146,0.0011363409,0.0007371042],"genre_scores_gemma":[0.044515003,0.0004446074,0.95138174,0.00013145315,0.00005658778,0.000114309376,0.0005108837,0.00019550165,0.0026497992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994192,0.000039448685,0.00002341313,0.00017707853,0.00029188272,0.00004896208],"domain_scores_gemma":[0.9996176,0.00007616846,0.000058081285,0.000070024405,0.00014895406,0.000029193632],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005025978,0.0007255165,0.0007468765,0.0014694863,0.00038435857,0.00095976016,0.0016852586,0.0008328712,0.0018214353],"category_scores_gemma":[0.0013170306,0.0004954323,0.000777891,0.0012059215,0.0003249429,0.0014309862,0.0009434776,0.0011443457,0.0014039563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015869325,0.0000646884,0.0011217342,0.0001506837,0.00011140119,0.00007596553,0.000078965975,0.023060592,0.08293425,0.008631941,0.006789629,0.87682146],"study_design_scores_gemma":[0.00007697857,0.00016591317,0.002089012,0.000034477674,0.00007790973,0.000707553,0.000029481287,0.88224566,0.07013497,0.0034710893,0.040890057,0.000076938784],"about_ca_topic_score_codex":0.004570449,"about_ca_topic_score_gemma":0.005028075,"teacher_disagreement_score":0.004570449,"about_ca_system_score_codex":0.00064675964,"about_ca_system_score_gemma":0.00092587434,"threshold_uncertainty_score":0.009087682},"labels":[],"label_agreement":null},{"id":"W2110629368","doi":"10.5539/cis.v4n2p152","title":"Target Tracking Based on Mean Shift and KALMAN Filter with Kernel Histogram Filtering","year":2011,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Mean-shift; Artificial intelligence; Kalman filter; Computer vision; Video tracking; Histogram; Tracking (education); Kernel (algebra); Active appearance model; Object (grammar); Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.032444076738868885,"score_gpt":0.2512403195701079,"score_spread":0.218796242831239,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110629368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011749391,0.00036507004,0.98630756,0.000047286776,0.00006249134,0.000021553547,0.000022136863,0.0007225596,0.000701976],"genre_scores_gemma":[0.49528584,0.0008838972,0.49845868,0.00008933184,0.00011595844,0.00013465161,0.00021189883,0.00016006782,0.004659737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918073,0.0001231545,0.000042471936,0.00022866148,0.00035009786,0.00007487971],"domain_scores_gemma":[0.99933165,0.00023274162,0.000051457464,0.00009178395,0.00026310427,0.000029229626],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008830219,0.00053826795,0.0010813494,0.00083098165,0.00056235335,0.0006641716,0.0009400639,0.00091251946,0.0011158905],"category_scores_gemma":[0.0019454382,0.00043861594,0.0009139024,0.0010769423,0.0004010834,0.0018655199,0.00068839185,0.0007059224,0.00042760329],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041291435,0.00016532796,0.003387425,0.00024681483,0.00024617522,0.00012231126,0.00025293912,0.19667342,0.044102713,0.012405873,0.0027128204,0.7392713],"study_design_scores_gemma":[0.000023452925,0.0000860606,0.0011082991,0.000005753474,0.00005373381,0.00007879009,0.000012542796,0.98317873,0.011575647,0.0019315592,0.001901867,0.00004350426],"about_ca_topic_score_codex":0.010795724,"about_ca_topic_score_gemma":0.00592902,"teacher_disagreement_score":0.010795724,"about_ca_system_score_codex":0.00058806903,"about_ca_system_score_gemma":0.0009823289,"threshold_uncertainty_score":0.021465778},"labels":[],"label_agreement":null},{"id":"W2110671801","doi":"","title":"Incremental Learning for Visual Tracking","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":288,"is_retracted":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":"Particle filter; Artificial intelligence; Computer science; Eye tracking; Tracking (education); Video tracking; Computer vision; Subspace topology; Representation (politics); Inference; Active appearance model; Markov chain Monte Carlo; Task (project management); Pattern recognition (psychology); Object (grammar); Kalman filter; Bayesian probability; Image (mathematics)","score_opus":0.0371558157009693,"score_gpt":0.3422921101543846,"score_spread":0.3051362944534153,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2110671801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0033571983,0.00025636636,0.99437654,0.000058684767,0.000037786423,0.000033648408,0.000040912466,0.0009281322,0.0009108365],"genre_scores_gemma":[0.43264326,0.0008879277,0.5606862,0.00023519094,0.00019779862,0.00044811078,0.0005730758,0.0002523536,0.0040760934],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993561,0.00014592386,0.000028257515,0.00017960944,0.00022534517,0.00006476196],"domain_scores_gemma":[0.997931,0.0011634411,0.00015191601,0.00032593665,0.00035658074,0.000071175666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010723267,0.0009016181,0.0011667368,0.00096520764,0.0005379992,0.00086209964,0.0024208783,0.0009025154,0.0028000746],"category_scores_gemma":[0.0061806175,0.00060903816,0.0007437599,0.0010107938,0.0007658259,0.0016913667,0.0014678087,0.001477162,0.0009950323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016508381,0.00013703233,0.0010858995,0.00017810987,0.000079191086,0.000112224705,0.000105584535,0.4313043,0.0047056153,0.02962303,0.0053440398,0.5271598],"study_design_scores_gemma":[0.000009550943,0.000024668516,0.00012153275,0.0000058936344,0.000009263521,0.000029371404,0.000004224509,0.98516685,0.0011236883,0.012298645,0.0011988865,0.0000073639453],"about_ca_topic_score_codex":0.005654857,"about_ca_topic_score_gemma":0.005119538,"teacher_disagreement_score":0.005654857,"about_ca_system_score_codex":0.0008492221,"about_ca_system_score_gemma":0.0008623444,"threshold_uncertainty_score":0.011243939},"labels":[],"label_agreement":null},{"id":"W2112186419","doi":"","title":"DTV: Detection, Tracking and Validation Framework for Unique People Count","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Tracking (education); Computer vision; Field (mathematics); Artificial intelligence; Novelty; Trajectory; Field of view; Pedestrian; Transport engineering; Mathematics; Engineering","score_opus":0.024809532409438645,"score_gpt":0.3024518606985454,"score_spread":0.27764232828910673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2112186419","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":[0.0042378064,0.00021435061,0.9922742,0.00004107607,0.00004255533,0.0000858171,0.00033818284,0.0024233935,0.00034262915],"genre_scores_gemma":[0.18560302,0.0003794468,0.807661,0.0001312702,0.000086224165,0.000475118,0.003295973,0.00030480773,0.0020631207],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99813783,0.00041387812,0.00012678435,0.0005598623,0.0005981283,0.00016348074],"domain_scores_gemma":[0.99855715,0.00041679016,0.00020341674,0.00023769082,0.0004830753,0.000101875004],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002678688,0.0012331106,0.0013959084,0.002550466,0.00075058575,0.0011210409,0.002645062,0.001241866,0.0011576876],"category_scores_gemma":[0.004986084,0.0004740158,0.0010784941,0.0013188513,0.00055263063,0.0015521374,0.0018074336,0.0013239541,0.0008768619],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003984633,0.00027873254,0.009242558,0.0002818547,0.00020518391,0.000387694,0.0002735722,0.14844203,0.016413968,0.015844325,0.016808001,0.7914237],"study_design_scores_gemma":[0.000019812398,0.000075822114,0.0013891783,0.000029309072,0.000025341047,0.0002439277,0.000051184907,0.9812459,0.0056384187,0.0050071958,0.006240664,0.000033226705],"about_ca_topic_score_codex":0.020791173,"about_ca_topic_score_gemma":0.014987529,"teacher_disagreement_score":0.020791173,"about_ca_system_score_codex":0.0010607606,"about_ca_system_score_gemma":0.0019224909,"threshold_uncertainty_score":0.04134029},"labels":[],"label_agreement":null},{"id":"W2113315588","doi":"10.1109/icpr.2006.602","title":"Generic Real-Time Tracking Method on Semi-Dynamic Scenes","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Tracking (education); A priori and a posteriori; Sequence (biology); Track (disk drive); Video tracking; Image (mathematics); Object (grammar)","score_opus":0.02052737767909437,"score_gpt":0.3087753756377873,"score_spread":0.28824799795869294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113315588","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":[0.0024770482,0.0000842033,0.99593616,0.000010746049,0.000022321781,0.000016009155,0.000022756481,0.0005546372,0.00087609695],"genre_scores_gemma":[0.09791435,0.0004365885,0.89531213,0.00008043784,0.000077558056,0.00009121227,0.00032382229,0.00027484735,0.0054890453],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995152,0.000061782914,0.0000202865,0.00018009947,0.00017410469,0.000048631053],"domain_scores_gemma":[0.99969816,0.000053885604,0.00003493199,0.0001097025,0.00007590091,0.000027388955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005558533,0.0006986261,0.0008703516,0.00062996324,0.0004176903,0.0006851729,0.0013875385,0.0008963937,0.0019128942],"category_scores_gemma":[0.00073095213,0.00037902847,0.0009339284,0.0007602709,0.0005679814,0.0013750101,0.00129228,0.0006511789,0.0012707309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020719362,0.0000806081,0.0007267401,0.00037887163,0.00013689153,0.0003296901,0.00026188506,0.20426556,0.1369776,0.036779005,0.006171824,0.6136841],"study_design_scores_gemma":[0.000023024648,0.00009678155,0.00092502235,0.000028746412,0.00003734914,0.000679569,0.000026191085,0.9399036,0.030826023,0.0070628957,0.02034035,0.000050369606],"about_ca_topic_score_codex":0.0011235378,"about_ca_topic_score_gemma":0.0016548447,"teacher_disagreement_score":0.0019128942,"about_ca_system_score_codex":0.00042045937,"about_ca_system_score_gemma":0.00050727377,"threshold_uncertainty_score":0.006399274},"labels":[],"label_agreement":null},{"id":"W2113885531","doi":"10.1109/ical.2008.4636704","title":"A robust traffic state parameters extract approach based on video for traffic surveillance","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Dalhousie University","keywords":"Computer vision; Artificial intelligence; Computer science; Background subtraction; Robustness (evolution); Wavelet transform; Kalman filter; Histogram; Edge detection; Wavelet; Image processing; Pixel; Image (mathematics)","score_opus":0.0782510818460488,"score_gpt":0.274472017810302,"score_spread":0.19622093596425322,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2113885531","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020645294,0.00026170598,0.9764647,0.00003851992,0.000061499566,0.0000450692,0.00010461318,0.0013241048,0.0010545766],"genre_scores_gemma":[0.41487852,0.0004788498,0.5803311,0.00007166323,0.00013370537,0.00016343631,0.0007147747,0.00013726696,0.003090746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997429,0.000027635955,0.000016302398,0.000086829416,0.000103837694,0.000022480388],"domain_scores_gemma":[0.99978584,0.0000408731,0.000037534464,0.000027795259,0.00009358829,0.000014368897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002815467,0.00066892576,0.00068940024,0.001514229,0.00023824508,0.0005697523,0.00053547474,0.0004686487,0.0010521701],"category_scores_gemma":[0.00086968514,0.0002676152,0.0004379882,0.0007158881,0.00016786516,0.00069727417,0.0003427694,0.0004045852,0.0005635996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020540933,0.00010494305,0.0013726624,0.000104532905,0.00007887368,0.00011524529,0.000049225186,0.029974964,0.11243555,0.0020748714,0.0019251489,0.85155857],"study_design_scores_gemma":[0.00003134921,0.00020459728,0.003960212,0.000015395026,0.00009326988,0.0003080069,0.000040462597,0.9278241,0.06072017,0.001091947,0.005657372,0.000053043677],"about_ca_topic_score_codex":0.0024106645,"about_ca_topic_score_gemma":0.0025789938,"teacher_disagreement_score":0.0024106645,"about_ca_system_score_codex":0.00029039333,"about_ca_system_score_gemma":0.00034417084,"threshold_uncertainty_score":0.0047932267},"labels":[],"label_agreement":null},{"id":"W2114508695","doi":"10.1109/tits.2008.915647","title":"Multilevel Framework to Detect and Handle Vehicle Occlusion","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":111,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Inter frame; Occlusion; Artificial intelligence; Computer vision; Computer science; Tracking (education); Cluster analysis; Emphasis (telecommunications); Frame (networking); Reference frame","score_opus":0.04673748613794341,"score_gpt":0.29508108441611236,"score_spread":0.24834359827816896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2114508695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008396111,0.0001970482,0.9891072,0.000040848627,0.000020280868,0.000046360365,0.000105506806,0.0013810726,0.0007055571],"genre_scores_gemma":[0.1679953,0.0002970712,0.8280592,0.00009171339,0.00006666268,0.00013188175,0.0007638797,0.0002561395,0.0023382276],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911696,0.00008909087,0.000031735715,0.00017628119,0.00042650267,0.00015951185],"domain_scores_gemma":[0.99935347,0.00011442812,0.00010001122,0.00012915276,0.0002506341,0.000052285744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007019516,0.0008636371,0.0012536478,0.0026381263,0.00071069377,0.001180666,0.0019808288,0.001102326,0.0030836277],"category_scores_gemma":[0.0016462337,0.00058910664,0.0016935718,0.0012115343,0.00042544917,0.0011827337,0.0017613732,0.0011465433,0.0011318577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035311136,0.00021765973,0.0034290757,0.00022808283,0.00028715032,0.00027822278,0.0003489905,0.10788666,0.06501554,0.00860822,0.008994619,0.80435264],"study_design_scores_gemma":[0.000022483926,0.00010156616,0.0018010521,0.000022967122,0.00009288424,0.00022369919,0.00006325016,0.97300476,0.015703551,0.002845845,0.0060781245,0.000039805946],"about_ca_topic_score_codex":0.01400208,"about_ca_topic_score_gemma":0.017425511,"teacher_disagreement_score":0.01400208,"about_ca_system_score_codex":0.00085908925,"about_ca_system_score_gemma":0.0011979383,"threshold_uncertainty_score":0.02784115},"labels":[],"label_agreement":null},{"id":"W2115094435","doi":"10.1109/cec.2008.4631366","title":"GPU based extraction of moving objects without shadows under intensity changes","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Computer vision; Shadow (psychology); Acceleration; Process (computing); Feature extraction; Subtraction; Component (thermodynamics); Image (mathematics); General-purpose computing on graphics processing units; Computer graphics (images); Pixel; Graphics; Mathematics","score_opus":0.061942721255117086,"score_gpt":0.3073836505862156,"score_spread":0.2454409293310985,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115094435","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":[0.05100372,0.00041052286,0.94311565,0.000076801494,0.000062474435,0.00003948045,0.00007834578,0.0022254097,0.0029876046],"genre_scores_gemma":[0.20453677,0.00043303566,0.78973657,0.00006345286,0.000033697248,0.00003770549,0.00037030104,0.00029381245,0.004494728],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983907,0.000018048328,0.0000074254076,0.000028470658,0.000082286475,0.000024718618],"domain_scores_gemma":[0.99982625,0.000034784698,0.000018887798,0.00002815157,0.0000751093,0.00001690647],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014912088,0.00044291606,0.00065166806,0.0010980347,0.00036101742,0.0008471208,0.0005940019,0.00042637446,0.0014192193],"category_scores_gemma":[0.0006408454,0.00034671804,0.00038323132,0.000831241,0.0002017838,0.00065881386,0.00051449245,0.0004034102,0.0006705055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024327409,0.000063436426,0.0020231232,0.00012413926,0.000060757997,0.00023393902,0.00013709601,0.019371241,0.32458976,0.0033196812,0.0024056889,0.64742786],"study_design_scores_gemma":[0.000035186615,0.00009604668,0.005430919,0.000025648658,0.00005387967,0.0008475997,0.00007807825,0.7534393,0.21977152,0.0025672435,0.01760979,0.00004479323],"about_ca_topic_score_codex":0.0028345913,"about_ca_topic_score_gemma":0.0034183594,"teacher_disagreement_score":0.0028345913,"about_ca_system_score_codex":0.0003285949,"about_ca_system_score_gemma":0.0004198542,"threshold_uncertainty_score":0.005636215},"labels":[],"label_agreement":null},{"id":"W2115664630","doi":"10.1109/icce.2008.4587949","title":"An Efficient Region of Interest Generation Technique for Far-Infrared Pedestrian Detection","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Pedestrian detection; Automotive industry; Advanced driver assistance systems; Computer science; Emphasis (telecommunications); Pedestrian; Night vision; Automotive engineering; Embedded system; Artificial intelligence; Systems engineering; Computer vision; Real-time computing; Engineering; Telecommunications; Aerospace engineering; Transport engineering","score_opus":0.13553533854667155,"score_gpt":0.3270321092191541,"score_spread":0.19149677067248255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2115664630","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0067136674,0.0003837388,0.99060667,0.000056446712,0.00005766013,0.00005156809,0.00008694463,0.0012163421,0.0008270629],"genre_scores_gemma":[0.07476344,0.0003751535,0.921221,0.00013255209,0.00007356754,0.000109560446,0.0004326263,0.00015912837,0.002733044],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995993,0.0000854885,0.00001454177,0.00009675039,0.00015954107,0.000044371664],"domain_scores_gemma":[0.99958307,0.00011740709,0.000041927797,0.00007494044,0.00015984762,0.00002286034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005644264,0.00073512644,0.0006428797,0.001289039,0.00034295832,0.00037270787,0.00090673944,0.000755361,0.0026333104],"category_scores_gemma":[0.0010210264,0.00038696706,0.00078899454,0.0010141742,0.00024683672,0.0005568418,0.0005827022,0.00082169235,0.0029370722],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003012307,0.000113053655,0.00070504163,0.00015891842,0.000061065104,0.00031030795,0.00010284949,0.0072456603,0.29077664,0.0027019377,0.006949385,0.690574],"study_design_scores_gemma":[0.00007214825,0.00045699487,0.008026516,0.00005024617,0.00017725489,0.0043590814,0.00006533423,0.62472475,0.3169486,0.0038278091,0.04117517,0.00011604555],"about_ca_topic_score_codex":0.0007798036,"about_ca_topic_score_gemma":0.0017112528,"teacher_disagreement_score":0.0026333104,"about_ca_system_score_codex":0.00020849745,"about_ca_system_score_gemma":0.0003200489,"threshold_uncertainty_score":0.0088092685},"labels":[],"label_agreement":null},{"id":"W2116013276","doi":"10.1109/crv.2008.7","title":"Robust Real-Time Bi-Layer Video Segmentation Using Infrared Video","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Segmentation; Pipeline (software); Image segmentation; Video processing; Cut; Graph; Mixture model; Pattern recognition (psychology)","score_opus":0.09025955124820395,"score_gpt":0.30809274307580614,"score_spread":0.2178331918276022,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116013276","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019278113,0.0002330598,0.9781807,0.000036516252,0.000034440676,0.000025957495,0.000044322976,0.0015480223,0.0006188314],"genre_scores_gemma":[0.16736378,0.00024849048,0.8304123,0.000052459523,0.00004001768,0.000039558174,0.0002195256,0.0002227658,0.0014010386],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956876,0.000057410027,0.000020005597,0.00012648896,0.00017756842,0.00004973074],"domain_scores_gemma":[0.99969995,0.000082533654,0.00005620507,0.000056046047,0.00008269967,0.000022573475],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004160917,0.00065190846,0.0005504838,0.0010490577,0.0002628872,0.00074661395,0.0010055437,0.000661629,0.0010174424],"category_scores_gemma":[0.00090161513,0.00042520105,0.00048508833,0.0005869161,0.00034788862,0.0011785922,0.0005460513,0.00058928994,0.00062222604],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040991986,0.00007295596,0.0010110745,0.00015595825,0.000065126194,0.00010236119,0.00013153355,0.027548911,0.4778874,0.002213122,0.0012757177,0.48912597],"study_design_scores_gemma":[0.000023602099,0.00013494286,0.0031756645,0.000020885327,0.000053591917,0.00037602213,0.000053872984,0.67759144,0.31175503,0.0014442632,0.005312281,0.000058337646],"about_ca_topic_score_codex":0.0018739126,"about_ca_topic_score_gemma":0.0020688628,"teacher_disagreement_score":0.0018739126,"about_ca_system_score_codex":0.0004897329,"about_ca_system_score_gemma":0.0003322015,"threshold_uncertainty_score":0.0037260056},"labels":[],"label_agreement":null},{"id":"W2116194520","doi":"10.1117/12.718446","title":"A system to automatically track humans and vehicles with a PTZ camera","year":2007,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Computer science; Track (disk drive); Zoom; Object detection; Computer vision; Artificial intelligence; Tracking (education); Video tracking; Tracking system; Software; Object (grammar); Cognitive neuroscience of visual object recognition; Real-time computing; Kalman filter; Pattern recognition (psychology)","score_opus":0.011819811899079356,"score_gpt":0.2469784440790189,"score_spread":0.23515863217993954,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2116194520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037814055,0.00020788616,0.9397308,0.00010512486,0.0001296095,0.00037074916,0.00069093367,0.016784502,0.004166291],"genre_scores_gemma":[0.2480637,0.0003167204,0.73674524,0.00025812487,0.000085333944,0.0005878868,0.0022171724,0.0003532591,0.011372511],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999688,0.000030628995,0.0000117963045,0.000119033415,0.00012036317,0.000030213834],"domain_scores_gemma":[0.9997292,0.000046760382,0.000036285768,0.000057630674,0.0000925513,0.000037555325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040144584,0.0006557707,0.00048599986,0.0010349103,0.000300252,0.0005370045,0.0009260747,0.0007160389,0.0060051437],"category_scores_gemma":[0.0007454832,0.00029976462,0.00028086975,0.00047522964,0.00028629616,0.0009064163,0.0008648072,0.0004570069,0.0037122634],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000626075,0.0001505778,0.005561573,0.00032930457,0.000100010715,0.00028177592,0.00021936122,0.00513355,0.26579618,0.0029572109,0.014874834,0.70396954],"study_design_scores_gemma":[0.00037390695,0.0017047392,0.0369564,0.00012366696,0.00028138515,0.0059847143,0.00018987186,0.4984576,0.33875108,0.0036645578,0.11331827,0.00019380971],"about_ca_topic_score_codex":0.0017859463,"about_ca_topic_score_gemma":0.0018463192,"teacher_disagreement_score":0.0060051437,"about_ca_system_score_codex":0.0003062816,"about_ca_system_score_gemma":0.0004411373,"threshold_uncertainty_score":0.02008921},"labels":[],"label_agreement":null},{"id":"W2117095208","doi":"10.5772/56603","title":"Surrounding Moving Obstacle Detection for Autonomous Driving Using Stereo Vision","year":2013,"lang":"en","type":"article","venue":"International Journal of Advanced Robotic Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Computer vision; Artificial intelligence; Computer science; Stereopsis; Obstacle; Robustness (evolution); Particle filter; Robotics; Stereo camera; Stereo cameras; Mobile robot; Robot; Filter (signal processing)","score_opus":0.027921548286338655,"score_gpt":0.3274232583189346,"score_spread":0.29950171003259596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117095208","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031519514,0.00016996053,0.9667581,0.000038391612,0.000020514208,0.000034682478,0.000038795333,0.00049229054,0.0009277707],"genre_scores_gemma":[0.6383347,0.00027836673,0.36003572,0.000060525643,0.000034375327,0.00007208267,0.00019026914,0.000033749562,0.00096021267],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99977785,0.000024472442,0.000006466211,0.0000335026,0.00013104247,0.00002664255],"domain_scores_gemma":[0.99982965,0.000035042332,0.000027539187,0.00002173389,0.00006962117,0.00001641402],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027586185,0.00030319765,0.00040319702,0.0008273791,0.00023111895,0.00033927898,0.00067497836,0.0004650058,0.0005963871],"category_scores_gemma":[0.00063572725,0.0002482256,0.00041309258,0.00045462028,0.00021447243,0.0004174825,0.00059526804,0.00031857062,0.00025227788],"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":[0.00020350357,0.00016301974,0.002530931,0.00015014273,0.00007262845,0.0002951591,0.00017432233,0.120209746,0.12533621,0.006478071,0.0028652237,0.741521],"study_design_scores_gemma":[0.000017707891,0.000059877555,0.0013232222,0.000005379039,0.000013616752,0.00013226354,0.000022322005,0.98688847,0.008483042,0.0018892256,0.0011513267,0.000013530617],"about_ca_topic_score_codex":0.006202332,"about_ca_topic_score_gemma":0.0063241106,"teacher_disagreement_score":0.006202332,"about_ca_system_score_codex":0.00032937998,"about_ca_system_score_gemma":0.000870282,"threshold_uncertainty_score":0.012332499},"labels":[],"label_agreement":null},{"id":"W2117316380","doi":"10.1007/978-3-642-13681-8_59","title":"3D Head Trajectory Using a Single Camera","year":2010,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer vision; Trajectory; Artificial intelligence; Head (geology); Particle filter; Computer science; Track (disk drive); Ellipsoid; Computer graphics (images); Filter (signal processing); Geography; Geodesy; Physics; Geology","score_opus":0.04710556304365567,"score_gpt":0.3000020744309095,"score_spread":0.25289651138725383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117316380","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010065606,0.00013801806,0.98248404,0.000081787344,0.00006418619,0.000045707046,0.0005699656,0.0032665336,0.0032842134],"genre_scores_gemma":[0.2227487,0.0007527125,0.76542807,0.0000764586,0.00007023352,0.000117146374,0.0014658667,0.00061713706,0.008723669],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997607,0.000026331627,0.000008826907,0.00008174458,0.00009856555,0.000023847018],"domain_scores_gemma":[0.9996531,0.000057896534,0.000031851283,0.000098121294,0.00012505797,0.00003408798],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022446453,0.0010284602,0.0008378772,0.000976549,0.0004204311,0.0009857297,0.0009247881,0.00092533417,0.010077612],"category_scores_gemma":[0.000851411,0.0009751674,0.00075680454,0.0013089565,0.00028875287,0.000869495,0.0013896213,0.0007395796,0.0059966366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066313887,0.00007241983,0.0028197046,0.00040257344,0.00018503812,0.0007429382,0.00052237394,0.08240795,0.12963523,0.0047282674,0.013048493,0.7647718],"study_design_scores_gemma":[0.00006988543,0.00023228965,0.00901941,0.0000866706,0.00010927203,0.002517678,0.0002517175,0.89139336,0.07426882,0.00615149,0.015765822,0.00013346068],"about_ca_topic_score_codex":0.008774646,"about_ca_topic_score_gemma":0.013487823,"teacher_disagreement_score":0.010077612,"about_ca_system_score_codex":0.00035938303,"about_ca_system_score_gemma":0.0008914095,"threshold_uncertainty_score":0.033713043},"labels":[],"label_agreement":null},{"id":"W2117893974","doi":"10.1109/cccrv.2004.1301415","title":"The extension of statistical face detection to face tracking","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Facial motion capture; Initialization; Face detection; Face (sociological concept); Tracking (education); Template matching; Object-class detection; Facial recognition system; Matching (statistics); Particle filter; Pattern recognition (psychology); Mathematics; Kalman filter; Image (mathematics)","score_opus":0.03181200509260562,"score_gpt":0.3206919961374517,"score_spread":0.2888799910448461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2117893974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052146753,0.0001890909,0.99073875,0.00009795071,0.00007560344,0.000036043504,0.00004257835,0.0016061496,0.0019990855],"genre_scores_gemma":[0.21257548,0.00052241207,0.7821318,0.00024783757,0.00019236334,0.0001534042,0.00018263907,0.00014907632,0.0038450882],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99938047,0.000106968844,0.000024534162,0.00013052936,0.00031199216,0.00004545218],"domain_scores_gemma":[0.99866164,0.0005776839,0.00008002971,0.0002269078,0.0004174289,0.00003635476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00096149446,0.00039141835,0.00043292335,0.0007734413,0.0002586448,0.00056125084,0.0007913317,0.00045124284,0.0016303221],"category_scores_gemma":[0.002546592,0.00038408552,0.00044348155,0.000549146,0.00033692856,0.0006266557,0.0005388352,0.0005782474,0.0010873206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010041064,0.00010261824,0.0018061035,0.00009343141,0.000044088607,0.00011916851,0.000071912065,0.04419058,0.06981317,0.014641129,0.004042368,0.8649751],"study_design_scores_gemma":[0.000015358968,0.00024912207,0.0028854657,0.00002130032,0.00003447726,0.0005862101,0.00001279964,0.93051875,0.03701533,0.010107612,0.018505458,0.000048097838],"about_ca_topic_score_codex":0.0029030764,"about_ca_topic_score_gemma":0.0029884982,"teacher_disagreement_score":0.0029030764,"about_ca_system_score_codex":0.0004068916,"about_ca_system_score_gemma":0.00081987854,"threshold_uncertainty_score":0.005772412},"labels":[],"label_agreement":null},{"id":"W2119345721","doi":"10.1109/tbme.2007.895747","title":"Extended-Hungarian-JPDA: Exact Single-Frame Stem Cell Tracking","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of British Columbia; University of Waterloo","keywords":"Probabilistic logic; Tracking (education); Frame (networking); Computer science; Stem cell; Gaussian; Algorithm; Mathematics; Mathematical optimization; Artificial intelligence; Biology","score_opus":0.020319074127616833,"score_gpt":0.25121968067685685,"score_spread":0.23090060654924002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119345721","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":[0.0016426864,0.00005097266,0.9978727,0.000022023696,0.00000936415,0.000006689519,0.00001778414,0.00020100048,0.00017670103],"genre_scores_gemma":[0.13043386,0.0001852042,0.8664998,0.00010739026,0.000033168257,0.00010000541,0.0002355143,0.00016355442,0.0022415246],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993405,0.00017874697,0.000039913288,0.00015931975,0.00022637425,0.000055179655],"domain_scores_gemma":[0.99894017,0.0005047967,0.00009778015,0.00020688865,0.00019824845,0.00005214498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015130817,0.0005977027,0.001470324,0.0006171736,0.0003674389,0.0009906448,0.0017208157,0.0010576522,0.0017024981],"category_scores_gemma":[0.0036890113,0.0007231595,0.0007962343,0.0012116693,0.00070226006,0.0014626683,0.0013699365,0.0010672082,0.0007339229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019222415,0.00007018307,0.00095077895,0.00011669341,0.00012910883,0.00013216754,0.00009724869,0.6951589,0.007318138,0.01651911,0.002333588,0.27698192],"study_design_scores_gemma":[0.000005073752,0.000010430171,0.00007814695,0.0000015100399,0.0000040761515,0.000024838873,0.0000022328395,0.99553466,0.00093926035,0.0028352283,0.00055848894,0.0000060293914],"about_ca_topic_score_codex":0.0042520687,"about_ca_topic_score_gemma":0.0042304723,"teacher_disagreement_score":0.0042520687,"about_ca_system_score_codex":0.00063727563,"about_ca_system_score_gemma":0.0017130336,"threshold_uncertainty_score":0.00845468},"labels":[],"label_agreement":null},{"id":"W2119958346","doi":"10.1109/icme.2011.6012164","title":"A decision support engine for video surveillance systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Guard (computer science); Computer science; Action (physics); Operator (biology); Computer security; Security guard; Space (punctuation); Risk analysis (engineering); Decision support system; Human–computer interaction; Artificial intelligence","score_opus":0.0549161278468418,"score_gpt":0.2931495966971822,"score_spread":0.2382334688503404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2119958346","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":[0.029011942,0.0002758162,0.95542616,0.0002508975,0.000067085944,0.00035491813,0.00044527662,0.011767574,0.0024002395],"genre_scores_gemma":[0.4296369,0.00032452276,0.5640254,0.0002923761,0.00006766909,0.0002932862,0.0010961824,0.00021319267,0.00405052],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991934,0.00014225594,0.00015600248,0.00016936043,0.0002805312,0.000058420428],"domain_scores_gemma":[0.99910825,0.00042802817,0.00007701264,0.00011214521,0.0002171855,0.00005726309],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012726936,0.0007086996,0.00058958237,0.0007237083,0.0003209726,0.0018991025,0.0012175773,0.0008718827,0.0022375325],"category_scores_gemma":[0.0031564375,0.0003558226,0.00058913184,0.0003337172,0.00038124304,0.0013521468,0.00072665786,0.0007855645,0.0009119018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011977208,0.0008977981,0.0047521316,0.000867715,0.0003044713,0.0013120717,0.00049190497,0.27875644,0.093218766,0.043234933,0.011462441,0.5635036],"study_design_scores_gemma":[0.000073322444,0.00014493926,0.00042472113,0.000032534124,0.00005934888,0.00018870573,0.00003439334,0.95274585,0.027725905,0.0067255753,0.011816569,0.000028130842],"about_ca_topic_score_codex":0.0019151387,"about_ca_topic_score_gemma":0.0011713682,"teacher_disagreement_score":0.0022375325,"about_ca_system_score_codex":0.00043342632,"about_ca_system_score_gemma":0.0006494911,"threshold_uncertainty_score":0.0074852705},"labels":[],"label_agreement":null},{"id":"W2120944404","doi":"10.1109/crv.2006.21","title":"Collaborative Multi-Camera Surveillance with Automated Person Detection","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Remote sensing; Computer graphics (images); Geology","score_opus":0.013155136027345114,"score_gpt":0.2647905180583571,"score_spread":0.251635382031012,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2120944404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036558505,0.00021999411,0.95980823,0.000081143684,0.000037577018,0.00007758461,0.00003489114,0.0014616223,0.0017204399],"genre_scores_gemma":[0.48641607,0.00012667538,0.5109034,0.0000962495,0.000106116044,0.00013185201,0.00013017912,0.00008062365,0.0020088009],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.997863,0.00047480097,0.000075984084,0.0008224074,0.000603173,0.00016053463],"domain_scores_gemma":[0.9978661,0.00059709756,0.00027538667,0.00072188996,0.00037739167,0.00016211766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012809169,0.0008863921,0.0013099593,0.00079974096,0.00067703903,0.0009142578,0.0022584607,0.0009981565,0.0013505239],"category_scores_gemma":[0.0027745164,0.0006757733,0.0006437203,0.0006732434,0.0005189167,0.0018811347,0.0021024183,0.0010115621,0.00061711273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008526303,0.0006748663,0.009693921,0.00019497609,0.00029983404,0.00046423354,0.00058677624,0.14095311,0.090513736,0.005974604,0.0045640212,0.74522734],"study_design_scores_gemma":[0.000050318547,0.00027427825,0.0032252911,0.000016480048,0.00004622552,0.00042362142,0.000071586204,0.9532289,0.03439609,0.0036093222,0.004624294,0.000033650347],"about_ca_topic_score_codex":0.0026538847,"about_ca_topic_score_gemma":0.004135745,"teacher_disagreement_score":0.0026538847,"about_ca_system_score_codex":0.0006068673,"about_ca_system_score_gemma":0.0006366461,"threshold_uncertainty_score":0.0067742467},"labels":[],"label_agreement":null},{"id":"W2121115638","doi":"10.1109/tsmcb.2006.883423","title":"An Active Vision System for Multitarget Surveillance in Dynamic Environments","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Amorfix (Canada)","funders":"","keywords":"Computer science; Active vision; A priori and a posteriori; Control reconfiguration; Computer vision; Artificial intelligence; Orientation (vector space); Position (finance); Real-time computing; Embedded system","score_opus":0.01469768275098797,"score_gpt":0.28501057497515253,"score_spread":0.27031289222416455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121115638","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009400177,0.0005595211,0.9853107,0.00008747584,0.00011022006,0.00006255314,0.00002656154,0.0009859783,0.003456836],"genre_scores_gemma":[0.39820608,0.0007398825,0.59400034,0.00023816428,0.0001270207,0.00021805511,0.00014113836,0.0000672758,0.006261998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999759,0.000039335024,0.000009788629,0.000062439205,0.00010972948,0.000019720504],"domain_scores_gemma":[0.9997943,0.00005855599,0.000022749437,0.000030145722,0.0000696882,0.000024563404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032959986,0.00041751313,0.00039175505,0.00040595786,0.0003544017,0.0006314014,0.0009897943,0.0007926754,0.0018586507],"category_scores_gemma":[0.0004925334,0.00021610864,0.00024800608,0.0002153205,0.00024014116,0.0008303203,0.00053468294,0.00078882114,0.00068874226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002793051,0.00027824417,0.000777286,0.0002965916,0.00006901675,0.00022272466,0.00020352237,0.026102329,0.25857598,0.017625485,0.0049383477,0.6906312],"study_design_scores_gemma":[0.00012044845,0.00094169757,0.0020392886,0.000057143836,0.00010358757,0.0009339834,0.00007568813,0.7941503,0.11739645,0.006373382,0.07773523,0.00007277669],"about_ca_topic_score_codex":0.00071088545,"about_ca_topic_score_gemma":0.00092999305,"teacher_disagreement_score":0.0018586507,"about_ca_system_score_codex":0.00034128298,"about_ca_system_score_gemma":0.0004092342,"threshold_uncertainty_score":0.0062178373},"labels":[],"label_agreement":null},{"id":"W2121180856","doi":"10.1109/iembs.2006.260829","title":"Monocular 3D Head Tracking to Detect Falls of Elderly People","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":170,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut Universitaire de Gériatrie de Montréal; Université de Montréal","funders":"","keywords":"Monocular; Computer science; Computer vision; Head (geology); Tracking (education); Trajectory; Artificial intelligence; Elderly people; Population; Physical medicine and rehabilitation; Medicine; Psychology; Gerontology","score_opus":0.022266131733378762,"score_gpt":0.29095329169029416,"score_spread":0.2686871599569154,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121180856","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48912606,0.0042970944,0.49478748,0.00043429693,0.00025241607,0.00011794341,0.0011055407,0.002741405,0.007137745],"genre_scores_gemma":[0.8541502,0.0012038653,0.14135742,0.00019855397,0.00010648332,0.00005586245,0.00045850474,0.000050610914,0.0024184731],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981993,0.000039311017,0.000009648326,0.000036840356,0.00007329718,0.000020967595],"domain_scores_gemma":[0.9996257,0.00008757288,0.000070576076,0.000028833641,0.00016161863,0.000025750283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022555437,0.0002862727,0.00033266356,0.00089196896,0.00015447086,0.00019423927,0.00023109757,0.00030617797,0.0008551784],"category_scores_gemma":[0.0009853631,0.00016938265,0.00017768447,0.0005436235,0.000089871355,0.00020955438,0.00023813768,0.00015685323,0.00037499255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008051235,0.00015542496,0.029541349,0.00030464833,0.00015574248,0.00033058686,0.00022809482,0.0154879205,0.17491297,0.0006209515,0.008762078,0.7686952],"study_design_scores_gemma":[0.00012988623,0.0009051489,0.16989994,0.00010251454,0.00022861054,0.002681388,0.00018333706,0.71666414,0.09860923,0.0015934198,0.008913934,0.00008844345],"about_ca_topic_score_codex":0.003988045,"about_ca_topic_score_gemma":0.007409264,"teacher_disagreement_score":0.003988045,"about_ca_system_score_codex":0.00018134581,"about_ca_system_score_gemma":0.00025467938,"threshold_uncertainty_score":0.007929683},"labels":[],"label_agreement":null},{"id":"W2121434603","doi":"10.1109/cvpr.2008.4587447","title":"Kernel-based learning of cast shadows from a physical model of light sources and surfaces for low-level segmentation","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer vision; Silhouette; Computer science; RGB color model; Background subtraction; Segmentation; Kernel (algebra); Shadow (psychology); Surface (topology); Computer graphics (images); Mathematics; Pixel; Geometry","score_opus":0.058401693580486795,"score_gpt":0.29262943711267037,"score_spread":0.23422774353218356,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2121434603","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031575065,0.000072720344,0.96736544,0.000037653994,0.000005145299,0.000011179489,0.000021000378,0.0006713987,0.000240404],"genre_scores_gemma":[0.71931565,0.00016909663,0.27844304,0.000040155697,0.000025106085,0.00004589197,0.00028652,0.00024088456,0.0014336306],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996871,0.00008232749,0.000017662358,0.00008263214,0.00008056661,0.000049784936],"domain_scores_gemma":[0.99910945,0.00031621024,0.00011389409,0.00023962677,0.00016555413,0.00005529816],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070853205,0.0005157824,0.0009010007,0.000627504,0.00028279255,0.0009055664,0.0012500433,0.00069521356,0.0011133713],"category_scores_gemma":[0.0023500859,0.00047325963,0.0007253969,0.0007000183,0.000598594,0.0013280476,0.00087674416,0.0010165181,0.00052592583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026226172,0.0001534758,0.0021403779,0.00010063836,0.0000803198,0.000052482912,0.00014628892,0.7290487,0.02508668,0.007327387,0.0010052507,0.23459616],"study_design_scores_gemma":[0.0000016954904,0.0000051756824,0.00015561677,8.541901e-7,0.000002097554,0.0000052583055,0.0000030732592,0.9977881,0.0009550737,0.0010074115,0.0000730907,0.0000026025214],"about_ca_topic_score_codex":0.005162233,"about_ca_topic_score_gemma":0.0049978457,"teacher_disagreement_score":0.005162233,"about_ca_system_score_codex":0.00079658534,"about_ca_system_score_gemma":0.0007092313,"threshold_uncertainty_score":0.010264397},"labels":[],"label_agreement":null},{"id":"W2122917109","doi":"10.1109/icme.2011.6012207","title":"Towards optimal placement of surveillance cameras in a bus","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Winnipeg","funders":"","keywords":"Computer science; Doors; Intuition; Obstacle; Computer vision; Artificial intelligence; Single camera; Real-time computing; Geography","score_opus":0.04858918121488674,"score_gpt":0.2870122599207414,"score_spread":0.23842307870585466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122917109","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042922482,0.00030469862,0.95442355,0.000113652415,0.00003108142,0.000073704025,0.00005826677,0.00051920826,0.0015533903],"genre_scores_gemma":[0.35198408,0.00040802226,0.6458173,0.000050823495,0.00003977034,0.00012545871,0.00020518429,0.00013450066,0.0012348474],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953604,0.00016547815,0.000020769736,0.00012439935,0.00009656352,0.0000568624],"domain_scores_gemma":[0.9992005,0.0003596941,0.00014583491,0.00006895834,0.00015529881,0.00006968614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006841355,0.0018406975,0.0014006537,0.0010245343,0.00050800276,0.001132521,0.00084323506,0.0014437855,0.0016273445],"category_scores_gemma":[0.0030866708,0.0010617474,0.00074996473,0.0006511562,0.00059352507,0.0008131612,0.0010208227,0.0007507324,0.0005063407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002494348,0.000081371254,0.0013308929,0.00016404089,0.000042096573,0.00018308533,0.00015395673,0.8882201,0.01951418,0.0038849143,0.0017688494,0.08440707],"study_design_scores_gemma":[0.000012974876,0.00005260586,0.00021550554,0.000008870765,0.0000064736096,0.000037252303,0.000035059773,0.9961528,0.001991686,0.0010963769,0.00038166315,0.000008704485],"about_ca_topic_score_codex":0.006541676,"about_ca_topic_score_gemma":0.0057155434,"teacher_disagreement_score":0.006541676,"about_ca_system_score_codex":0.00091429084,"about_ca_system_score_gemma":0.0011475262,"threshold_uncertainty_score":0.013007164},"labels":[],"label_agreement":null},{"id":"W2122996330","doi":"10.1109/icpr.2002.1048492","title":"Automated feature registration for robust tracking methods","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada)","funders":"","keywords":"Robustness (evolution); Computer science; Artificial intelligence; Computer vision; Salient; A priori and a posteriori; Matching (statistics); Tracking (education); Feature (linguistics); Independence (probability theory); Pixel; Feature tracking; Feature extraction; Pattern recognition (psychology); Mathematics","score_opus":0.0757341079316728,"score_gpt":0.382496002578169,"score_spread":0.3067618946464962,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2122996330","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":[0.0010156956,0.00006748453,0.9977708,0.000023280052,0.00001360881,0.000017940414,0.000018418148,0.00076907565,0.00030364288],"genre_scores_gemma":[0.07594545,0.00023163442,0.9206746,0.000054335178,0.000080388934,0.0002187535,0.00032216532,0.00043752513,0.0020352488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978048,0.0005504533,0.00012244914,0.0006947263,0.00068094966,0.00014667436],"domain_scores_gemma":[0.9976674,0.00080008997,0.00025366741,0.00081236527,0.00041203434,0.000054533255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020769443,0.001256116,0.0014743438,0.0020888387,0.0007823655,0.0014108589,0.0023413529,0.0020588336,0.005262804],"category_scores_gemma":[0.009048215,0.0009670463,0.0012608465,0.0018375218,0.0010656827,0.0026287169,0.0024820322,0.001688195,0.0043926095],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021419986,0.000095314026,0.00056463754,0.00020026397,0.00009677616,0.0001336794,0.00012576353,0.15749793,0.03953533,0.047654197,0.005418603,0.7484633],"study_design_scores_gemma":[0.0000359784,0.000085435866,0.00043574016,0.000022174108,0.000019161213,0.00017581147,0.000023242186,0.9408287,0.016013231,0.033408776,0.008920392,0.000031490763],"about_ca_topic_score_codex":0.0014281118,"about_ca_topic_score_gemma":0.0012151529,"teacher_disagreement_score":0.005262804,"about_ca_system_score_codex":0.00074079714,"about_ca_system_score_gemma":0.0011099936,"threshold_uncertainty_score":0.017605782},"labels":[],"label_agreement":null},{"id":"W2123273512","doi":"10.1109/iccv.2009.5459454","title":"Realtime background subtraction from dynamic scenes","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Background subtraction; Inference; Margin (machine learning); Component (thermodynamics); Dynamic programming; Generalization; Graphics; Artificial intelligence; Object (grammar); Mistake; Machine learning; Algorithm; Pixel; Computer graphics (images)","score_opus":0.026655648252651692,"score_gpt":0.3196686135725282,"score_spread":0.2930129653198765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2123273512","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":[0.030674191,0.00044012538,0.96489024,0.00010208896,0.00008180532,0.000022325292,0.00007981053,0.0013900176,0.0023193795],"genre_scores_gemma":[0.4084649,0.0009896046,0.58367395,0.000166755,0.00014462443,0.000055780765,0.00066784077,0.00044661545,0.005389888],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995658,0.000066147695,0.000013017926,0.00011632567,0.00018490817,0.00005379929],"domain_scores_gemma":[0.99964345,0.00013544106,0.000042484553,0.00007312381,0.000081166705,0.00002423062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005758467,0.0007242213,0.00093787926,0.0009842212,0.0002854431,0.0009986593,0.00092032016,0.00058481743,0.001712533],"category_scores_gemma":[0.0015259265,0.00041634406,0.00048679128,0.0008404608,0.0003457247,0.0011578557,0.0009833464,0.00092627225,0.00080432964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032078166,0.00010046391,0.0011448606,0.00017263879,0.000066234046,0.00022127108,0.00015275998,0.056534328,0.12850338,0.0057641147,0.0030846565,0.8039346],"study_design_scores_gemma":[0.000029407045,0.000114884046,0.0031477646,0.000024133653,0.000038462294,0.000691359,0.00009942604,0.9039008,0.07519723,0.007710306,0.009018187,0.000028167698],"about_ca_topic_score_codex":0.0014654101,"about_ca_topic_score_gemma":0.0022677472,"teacher_disagreement_score":0.001712533,"about_ca_system_score_codex":0.00036745938,"about_ca_system_score_gemma":0.00049824535,"threshold_uncertainty_score":0.00572896},"labels":[],"label_agreement":null},{"id":"W2124082557","doi":"10.1109/tcsvt.2007.906935","title":"Statistical background subtraction using spatial cues","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":102,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; Université de Sherbrooke","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Pixel; Computer vision; Frame (networking); Statistical model; Series (stratigraphy); Pattern recognition (psychology); Noise (video); Image (mathematics)","score_opus":0.05991418926184172,"score_gpt":0.33159182118265745,"score_spread":0.27167763192081573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124082557","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006675543,0.00028351124,0.99010336,0.000075866876,0.000042282005,0.00001599043,0.000059649592,0.0010475302,0.0016963573],"genre_scores_gemma":[0.24947482,0.0012765543,0.7433246,0.00026005227,0.00016084236,0.000063643965,0.0006011364,0.0004401234,0.004398335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994287,0.00008195071,0.00002441055,0.00011595574,0.00027983246,0.00006913286],"domain_scores_gemma":[0.9993325,0.00018890132,0.00006355941,0.000109550216,0.00027305182,0.000032376007],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006475476,0.0009386064,0.00067068887,0.0015895322,0.0003182267,0.0011011332,0.0010400758,0.0005943658,0.002002975],"category_scores_gemma":[0.0023631724,0.0004396918,0.00076095044,0.0015319502,0.0003899498,0.0014891188,0.0013404028,0.00077391684,0.0010288444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020972006,0.00007712324,0.001004378,0.00020281476,0.00010964308,0.00015920965,0.000090483714,0.103102185,0.09981993,0.020569783,0.0033986385,0.7712561],"study_design_scores_gemma":[0.000017309383,0.000068173795,0.001598536,0.00002467434,0.00005659125,0.00030883765,0.00003547593,0.91369605,0.061727196,0.012075703,0.010346563,0.00004477121],"about_ca_topic_score_codex":0.003171546,"about_ca_topic_score_gemma":0.004310026,"teacher_disagreement_score":0.003171546,"about_ca_system_score_codex":0.0005294954,"about_ca_system_score_gemma":0.0010235299,"threshold_uncertainty_score":0.006700635},"labels":[],"label_agreement":null},{"id":"W2124798821","doi":"10.1007/s11760-008-0055-6","title":"Feature-based detection and correction of occlusions and split of video objects","year":2008,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Communications Research Centre Canada","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Feature (linguistics); Segmentation; Tracking (education); Object detection; Video tracking; Superposition principle; Occlusion; Pattern recognition (psychology); Object (grammar); Mathematics","score_opus":0.016707728904526174,"score_gpt":0.27032170181526105,"score_spread":0.2536139729107349,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2124798821","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":[0.071544565,0.00032262443,0.9258371,0.000047280355,0.00007551908,0.00003298171,0.00012615263,0.0012793741,0.00073440775],"genre_scores_gemma":[0.47856173,0.00038514772,0.51740557,0.00005658657,0.000072099836,0.000062291496,0.00081069977,0.00035267454,0.0022931315],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994134,0.00005356564,0.000025516954,0.00015354565,0.00026633815,0.00008766173],"domain_scores_gemma":[0.9987212,0.00024658258,0.0001646914,0.00029272298,0.00051254197,0.00006224105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006613765,0.0006583893,0.0010845533,0.0014065546,0.0004649608,0.00077457645,0.00082267565,0.0006727227,0.0013712817],"category_scores_gemma":[0.0024913833,0.00042008678,0.0005164662,0.0012143765,0.00034622638,0.0009886463,0.00075453904,0.000797363,0.0006491177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069352874,0.00015099345,0.005431536,0.0001175158,0.000088513414,0.00017793413,0.00017861572,0.011187208,0.27100703,0.0016638967,0.0019961896,0.707307],"study_design_scores_gemma":[0.000044822307,0.00024991296,0.032727536,0.000029878265,0.00019168218,0.0012333815,0.00008927576,0.676711,0.2796284,0.0024142452,0.0066252314,0.000054703345],"about_ca_topic_score_codex":0.00174562,"about_ca_topic_score_gemma":0.0026575134,"teacher_disagreement_score":0.00174562,"about_ca_system_score_codex":0.00033627855,"about_ca_system_score_gemma":0.0006616697,"threshold_uncertainty_score":0.004587412},"labels":[],"label_agreement":null},{"id":"W2125521982","doi":"10.1109/icme.2011.6012253","title":"Fusion of face networks through the surveillance of public spaces to address sociological security recommendations","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Face (sociological concept); Port (circuit theory); Computer science; Computer security; Space (punctuation); Key (lock); Aggregate (composite); Corporate governance; Public space; Sociology; Business; Engineering; Social science","score_opus":0.134012858188585,"score_gpt":0.3410861067395522,"score_spread":0.2070732485509672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125521982","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30888408,0.0003656497,0.68184114,0.0008060731,0.000081728576,0.00020509776,0.00045777185,0.00072984357,0.0066286456],"genre_scores_gemma":[0.9059681,0.00015064709,0.09201915,0.000056196845,0.00005240061,0.00006622516,0.00037324431,0.000023006463,0.0012910528],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9988092,0.00038692608,0.000050553594,0.00025771133,0.0003858249,0.00010981788],"domain_scores_gemma":[0.9979582,0.00086844404,0.000221215,0.00031362232,0.00056017353,0.00007835026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001627625,0.0006686357,0.00077592285,0.002155739,0.00046269133,0.001007094,0.0008132891,0.0009630729,0.00092920294],"category_scores_gemma":[0.005704517,0.00029806016,0.00067118125,0.0011603577,0.0002814807,0.002375382,0.0008403591,0.0007607949,0.0003490695],"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":[0.0006138029,0.0005245385,0.044933345,0.00016349067,0.00040637507,0.00027045334,0.0013382954,0.27602628,0.02055552,0.010937719,0.004413137,0.63981706],"study_design_scores_gemma":[0.000005263675,0.00006797105,0.0080790315,0.000010991972,0.00005266379,0.000053541,0.00022791684,0.9824211,0.0034878042,0.0044134087,0.0011631565,0.000017224464],"about_ca_topic_score_codex":0.006331246,"about_ca_topic_score_gemma":0.009638171,"teacher_disagreement_score":0.006331246,"about_ca_system_score_codex":0.00084793085,"about_ca_system_score_gemma":0.00042399528,"threshold_uncertainty_score":0.012588739},"labels":[],"label_agreement":null},{"id":"W2125949781","doi":"10.1109/tip.2010.2052824","title":"Activity Based Matching in Distributed Camera Networks","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Scale-invariant feature transform; Computer science; Artificial intelligence; Computer vision; Matching (statistics); Feature extraction; Independence (probability theory); Pattern recognition (psychology); Mathematics","score_opus":0.014435744573932117,"score_gpt":0.28760040855960617,"score_spread":0.27316466398567407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2125949781","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014492711,0.0001734471,0.9841412,0.000080641694,0.00001928622,0.00003579696,0.000031181036,0.00037774778,0.0006479075],"genre_scores_gemma":[0.6242544,0.0003659818,0.37162986,0.00009686858,0.00008306528,0.00017136396,0.00027772813,0.00011255013,0.0030082543],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99783653,0.00066009426,0.00009590517,0.0006797469,0.00056340377,0.00016443823],"domain_scores_gemma":[0.9975006,0.0011565784,0.00041124295,0.00046289133,0.00035353264,0.000115203584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018485781,0.00077815756,0.0016972197,0.0022943781,0.0007884845,0.0014402906,0.0024261454,0.0018047875,0.0016477722],"category_scores_gemma":[0.00835994,0.0008262123,0.0007185456,0.0030039558,0.00097470154,0.0035944632,0.0021380577,0.00086027005,0.0006235276],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033079006,0.0001464491,0.002512849,0.00010116208,0.00010900873,0.0003137065,0.00022191301,0.6766972,0.004894695,0.025866995,0.0016950906,0.28711006],"study_design_scores_gemma":[0.000023236822,0.000031758907,0.00039925403,0.000006122621,0.000009033903,0.00008527199,0.00004392508,0.97829574,0.0017038339,0.018493434,0.0008983267,0.000010063102],"about_ca_topic_score_codex":0.003377752,"about_ca_topic_score_gemma":0.002563575,"teacher_disagreement_score":0.003377752,"about_ca_system_score_codex":0.0013133497,"about_ca_system_score_gemma":0.0008115221,"threshold_uncertainty_score":0.009776354},"labels":[],"label_agreement":null},{"id":"W2126069365","doi":"10.1002/atr.1327","title":"Computer vision approach for the classification of bike type (motorized versus non‐motorized) during busy traffic in the city of Shanghai","year":2015,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Intersection (aeronautics); Robustness (evolution); Transport engineering; Computer science; Binary classification; Artificial intelligence; Data collection; Machine learning; Engineering; Simulation; Support vector machine; Statistics","score_opus":0.060852335218280255,"score_gpt":0.3311882727294067,"score_spread":0.27033593751112645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126069365","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":[0.92336637,0.00035307975,0.07373683,0.00008444888,0.000037756134,0.000059883903,0.00045229413,0.00039965173,0.0015096788],"genre_scores_gemma":[0.9814736,0.0000810113,0.017295469,0.000017233437,0.00001412495,0.000024153778,0.0004253151,0.00000790078,0.0006611001],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9997454,0.00003934323,0.000016216458,0.00008898093,0.000042451058,0.00006765834],"domain_scores_gemma":[0.999703,0.000058867885,0.000045914872,0.00002067707,0.00013264197,0.000038925697],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00050196575,0.0005837899,0.0004548025,0.0038605602,0.000334764,0.0007846977,0.00044374223,0.0004162635,0.0006087413],"category_scores_gemma":[0.0007219621,0.0001732928,0.0004580015,0.0011371678,0.00020063517,0.00022648986,0.00033568073,0.00025885846,0.00027056452],"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":[0.0011279745,0.0005945306,0.2712909,0.0003027678,0.00035399894,0.0005977587,0.0009105002,0.12674558,0.050674077,0.0016278515,0.0043172888,0.54145676],"study_design_scores_gemma":[0.000018604407,0.0001337744,0.16320877,0.000016521148,0.0000828673,0.00010180845,0.0004737051,0.8305729,0.00411793,0.0005389883,0.0007029273,0.000031153224],"about_ca_topic_score_codex":0.035166617,"about_ca_topic_score_gemma":0.030234566,"teacher_disagreement_score":0.035166617,"about_ca_system_score_codex":0.00073027296,"about_ca_system_score_gemma":0.00071035046,"threshold_uncertainty_score":0.06992388},"labels":[],"label_agreement":null},{"id":"W2126361274","doi":"10.1109/crv.2005.30","title":"Coordination of Multiple Agents for Probabilistic Object Tracking","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Object (grammar); Video tracking; Tracking (education); Task (project management); Artificial intelligence; Probabilistic logic; Computer vision; Zoom; Event (particle physics); Focus (optics); Set (abstract data type); Action (physics); Distributed object; Multi-agent system; Field (mathematics); Human–computer interaction; Distributed computing; Engineering; Mathematics","score_opus":0.05854914563693866,"score_gpt":0.32918365233948255,"score_spread":0.2706345067025439,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126361274","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004421274,0.0001391298,0.9937557,0.00009388995,0.00003058483,0.000022260278,0.000010674814,0.0001525597,0.0013738527],"genre_scores_gemma":[0.5323146,0.0005405134,0.46054295,0.0001251441,0.00016032848,0.00030853765,0.00013411463,0.00014760534,0.005726201],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987835,0.00043279913,0.000056426306,0.00031917752,0.00030981007,0.000098242504],"domain_scores_gemma":[0.9982268,0.0010488863,0.00022726803,0.00022132458,0.00015902934,0.000116694726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019231315,0.0007765012,0.00087162305,0.0004273232,0.0006797198,0.0009424274,0.0014823363,0.00095474394,0.0019337907],"category_scores_gemma":[0.00417006,0.00046302844,0.0006592815,0.00044633064,0.0008915217,0.0013704116,0.0015490265,0.001090137,0.00042822497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001265389,0.0000501554,0.0006470082,0.000078574216,0.00005398756,0.00016207539,0.00013445213,0.878911,0.004698461,0.064629816,0.0013463992,0.049161535],"study_design_scores_gemma":[0.00001748558,0.00003224179,0.00008963317,0.000004001739,0.000010268621,0.000029659817,0.000011737965,0.9844289,0.0007437291,0.012998971,0.0016264573,0.0000068635086],"about_ca_topic_score_codex":0.002959191,"about_ca_topic_score_gemma":0.0020540191,"teacher_disagreement_score":0.002959191,"about_ca_system_score_codex":0.0008548696,"about_ca_system_score_gemma":0.0009268368,"threshold_uncertainty_score":0.010170579},"labels":[],"label_agreement":null},{"id":"W2126465631","doi":"10.1109/iscas.2011.5937978","title":"A chaotic motion controller for camera networks","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Controller (irrigation); Chaotic; Computer vision; Artificial intelligence; Field of view; Smart camera; Motion controller; Image sensor; Real-time computing; Motion control; Robot","score_opus":0.05842085289050353,"score_gpt":0.27683068484474593,"score_spread":0.2184098319542424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2126465631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.041811284,0.00043596936,0.94788074,0.00020829214,0.0001177047,0.00008014051,0.000037723363,0.00041673268,0.009011442],"genre_scores_gemma":[0.9706125,0.00021973338,0.025726838,0.00005035874,0.000026625356,0.00008929599,0.000032423937,0.000014601559,0.0032276132],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998324,0.000026904881,0.000010027365,0.000056842706,0.000052115658,0.000021723277],"domain_scores_gemma":[0.9997613,0.00006752784,0.00005583656,0.00001960644,0.00007856628,0.000017139246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020754464,0.0004834245,0.00025844268,0.00027195306,0.00031319098,0.00043205675,0.0004771995,0.00039521634,0.0010943454],"category_scores_gemma":[0.0008171896,0.0001038766,0.00021346236,0.00018695254,0.00037782194,0.00037813542,0.00036190232,0.00036164347,0.00012862057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021528127,0.00005773978,0.0011591773,0.0001757227,0.0000614557,0.00034110912,0.00019335805,0.8003467,0.04491638,0.035795555,0.002445718,0.114291914],"study_design_scores_gemma":[0.000022970908,0.000098270924,0.00022001726,0.000007738878,0.00000994893,0.000072138784,0.000009596331,0.9927764,0.0029777437,0.0019424048,0.0018535733,0.0000092348555],"about_ca_topic_score_codex":0.0023393075,"about_ca_topic_score_gemma":0.0014831518,"teacher_disagreement_score":0.0023393075,"about_ca_system_score_codex":0.0004715446,"about_ca_system_score_gemma":0.00038885768,"threshold_uncertainty_score":0.0046513677},"labels":[],"label_agreement":null},{"id":"W2127074665","doi":"10.1109/crv.2012.31","title":"Robust Body-Height Estimation for Applications in Automotive Industry","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Automotive industry; Computer vision; Grayscale; Computer science; Artificial intelligence; Position (finance); Road surface; Robustness (evolution); Engineering; Image (mathematics)","score_opus":0.058485285291507726,"score_gpt":0.3292949798855463,"score_spread":0.2708096945940386,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127074665","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":[0.03108894,0.0008132084,0.96423155,0.00006464832,0.000045739023,0.00003675886,0.00014101274,0.0021108857,0.0014673168],"genre_scores_gemma":[0.5726397,0.0006909829,0.42263484,0.00007786167,0.00007780652,0.00006814254,0.0005946971,0.0001480575,0.0030678299],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999765,0.00004182236,0.000009455526,0.00005961016,0.000099223806,0.000024850562],"domain_scores_gemma":[0.99978834,0.000054209166,0.000030548745,0.00004059363,0.000074747106,0.00001164141],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024181932,0.0004898237,0.00040771283,0.0006366279,0.00018755585,0.00030303493,0.0004849427,0.0005703399,0.0020105077],"category_scores_gemma":[0.0009332894,0.0002370277,0.00027751006,0.0005793886,0.00013219618,0.00031394252,0.00047275392,0.0003224156,0.0013111813],"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":[0.00022501856,0.00007830522,0.0024680423,0.00016006838,0.000051394873,0.00013978865,0.000061894985,0.055495184,0.22307275,0.0019567963,0.0035649643,0.71272576],"study_design_scores_gemma":[0.000028828234,0.00015729772,0.011319508,0.000028860088,0.000034568322,0.00042162387,0.00004576559,0.8839522,0.09337621,0.0025645292,0.008014942,0.000055667013],"about_ca_topic_score_codex":0.0013791925,"about_ca_topic_score_gemma":0.0015423879,"teacher_disagreement_score":0.0020105077,"about_ca_system_score_codex":0.00015815336,"about_ca_system_score_gemma":0.00022450367,"threshold_uncertainty_score":0.006725788},"labels":[],"label_agreement":null},{"id":"W2127260155","doi":"10.1109/icma.2005.1626718","title":"Active-vision system for multi-target surveillance","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; A priori and a posteriori; Computer vision; Artificial intelligence; Active vision; Object detection; Object (grammar); Cognitive neuroscience of visual object recognition; Line (geometry); Real-time computing; Pattern recognition (psychology)","score_opus":0.030527947649538235,"score_gpt":0.31593254680893373,"score_spread":0.2854045991593955,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127260155","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":[0.004476,0.00080040644,0.9866388,0.00010095998,0.00016068606,0.00008386384,0.000059422164,0.0025409395,0.0051389853],"genre_scores_gemma":[0.30126518,0.0009223192,0.68455607,0.00032555035,0.0001788526,0.00028911312,0.00037281236,0.00013866708,0.011951473],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99957997,0.0000811518,0.00001722969,0.000081060294,0.00021007724,0.000030443041],"domain_scores_gemma":[0.99968374,0.00007097023,0.00002404722,0.00005379497,0.00013582488,0.000031635198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005238047,0.00046277032,0.0005082563,0.00055653346,0.0003572396,0.000811318,0.0015851092,0.0009439884,0.0040432992],"category_scores_gemma":[0.0007305559,0.00022777298,0.00029242953,0.0002935102,0.0002559869,0.0009229062,0.0006936802,0.001023879,0.002013244],"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":[0.00042047582,0.00035222768,0.0007591814,0.00038290714,0.000093577255,0.00020739847,0.0001738167,0.023596538,0.13666445,0.030275173,0.014938921,0.7921354],"study_design_scores_gemma":[0.00014057405,0.0006618382,0.0013147942,0.00006739293,0.000086701424,0.0006064526,0.000054547636,0.75997573,0.09675713,0.011603289,0.1286547,0.00007693036],"about_ca_topic_score_codex":0.0010080814,"about_ca_topic_score_gemma":0.0012610513,"teacher_disagreement_score":0.0040432992,"about_ca_system_score_codex":0.00042959733,"about_ca_system_score_gemma":0.00047783792,"threshold_uncertainty_score":0.013526201},"labels":[],"label_agreement":null},{"id":"W2127844049","doi":"10.1109/icip.2007.4379317","title":"Total Occlusion Correction using Invariantwavelet Features","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Occlusion; Artificial intelligence; Computer vision; Wavelet; Invariant (physics); Computer science; Wavelet transform; Pattern recognition (psychology); Robustness (evolution); Mathematics; Medicine","score_opus":0.023407678443751696,"score_gpt":0.3059704841610796,"score_spread":0.2825628057173279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2127844049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02580199,0.00035343738,0.9713127,0.00005469988,0.00010138403,0.000041078765,0.00006218267,0.0013865321,0.00088604057],"genre_scores_gemma":[0.25857127,0.000999505,0.73468125,0.00009852406,0.00018466146,0.00006438306,0.0006880211,0.00054422894,0.004168151],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991418,0.000067377325,0.000041330077,0.00016306939,0.0004919256,0.00009447602],"domain_scores_gemma":[0.99872786,0.00025163477,0.00025806687,0.00026491238,0.00045904727,0.000038340666],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006879112,0.0008062676,0.0010252729,0.0020535053,0.0004384646,0.0009499739,0.0009975072,0.0007473682,0.0014464505],"category_scores_gemma":[0.003017618,0.00046551158,0.00084216916,0.00143254,0.00035647702,0.0017460556,0.0007871085,0.0008961906,0.0010132409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018707725,0.00008999208,0.0019476819,0.00013542664,0.000096425385,0.00020836128,0.00017910585,0.015540979,0.115482144,0.0026654694,0.0028843007,0.860583],"study_design_scores_gemma":[0.000052856387,0.0003640405,0.010749775,0.00005403213,0.00028167648,0.0015590925,0.00013831737,0.6611475,0.2906205,0.003609412,0.031311322,0.00011148816],"about_ca_topic_score_codex":0.0020435804,"about_ca_topic_score_gemma":0.0017860748,"teacher_disagreement_score":0.0020535053,"about_ca_system_score_codex":0.0003962368,"about_ca_system_score_gemma":0.0005357767,"threshold_uncertainty_score":0.004838884},"labels":[],"label_agreement":null},{"id":"W2128027845","doi":"","title":"Adaptive Discriminative Generative Model and Its Applications","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Discriminative model; Generative model; Artificial intelligence; Generative grammar; Computer science; Context (archaeology); Pattern recognition (psychology); Active appearance model; Video tracking; Generative Design; Probabilistic logic; Object (grammar); Machine learning; Computer vision; Image (mathematics); Engineering; Metric (unit)","score_opus":0.06194467764540884,"score_gpt":0.31748802102984275,"score_spread":0.2555433433844339,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128027845","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":[0.0026101184,0.00030274453,0.9954926,0.00013481194,0.000026987778,0.0000079881,0.000043033782,0.00025265935,0.0011290216],"genre_scores_gemma":[0.538315,0.0017730255,0.44472295,0.00050168595,0.0003395477,0.00017128245,0.0006731905,0.0005107521,0.012992587],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993299,0.00022260996,0.000024717694,0.00019230628,0.00017121783,0.00005932499],"domain_scores_gemma":[0.99864846,0.0008190946,0.000087522574,0.00021412878,0.00017811982,0.000052749678],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011754021,0.00069786585,0.0009697492,0.0009677555,0.0004393627,0.00074801047,0.0016477376,0.0010883766,0.0028543237],"category_scores_gemma":[0.0040378207,0.0005437813,0.0011100951,0.0011133564,0.0010593726,0.0012198299,0.0014021405,0.0016055454,0.0010233993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008562762,0.00006511461,0.0015144793,0.00012634961,0.00008838973,0.00024621814,0.00020633619,0.5599875,0.0055269194,0.21837951,0.004602491,0.20917113],"study_design_scores_gemma":[0.0000044270937,0.000010127217,0.00017560592,0.0000062181393,0.000008422036,0.00009548903,0.0000068193917,0.9507778,0.0004962119,0.046430852,0.0019757252,0.00001227556],"about_ca_topic_score_codex":0.0049759313,"about_ca_topic_score_gemma":0.003611587,"teacher_disagreement_score":0.0049759313,"about_ca_system_score_codex":0.00090576836,"about_ca_system_score_gemma":0.0006142193,"threshold_uncertainty_score":0.009893954},"labels":[],"label_agreement":null},{"id":"W2128254691","doi":"10.1109/rose.2011.6058509","title":"RAT: Robust animal tracking","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Tracking (education); Computer vision; Artificial intelligence; Computer science; Track (disk drive); Motion (physics); Robustness (evolution); Pattern recognition (psychology)","score_opus":0.12906107176438597,"score_gpt":0.2856724117713865,"score_spread":0.15661134000700055,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128254691","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":[0.015818372,0.000513577,0.9501336,0.00007320598,0.00011253958,0.00017171746,0.0006839388,0.030130887,0.0023622583],"genre_scores_gemma":[0.18248573,0.0004885496,0.8030608,0.0002296882,0.00009868842,0.0004635146,0.0025842795,0.0010927141,0.009495978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991716,0.000113229806,0.00003685469,0.00024801007,0.00035042505,0.00007992509],"domain_scores_gemma":[0.999012,0.00016318074,0.00020485376,0.0003586118,0.00020342751,0.0000578955],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010845425,0.0009934206,0.0009310153,0.0010332351,0.00022555566,0.00057482644,0.0018035544,0.00092366803,0.0034103307],"category_scores_gemma":[0.0018724647,0.00045388908,0.0007087242,0.0006127669,0.00043804667,0.0008427703,0.0011872567,0.00080493453,0.0035394751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071675685,0.00021145103,0.0039886185,0.00048938603,0.00031050228,0.0003137148,0.00007521242,0.060466584,0.24890718,0.005857382,0.019321939,0.6593413],"study_design_scores_gemma":[0.00013840107,0.0006675228,0.006975476,0.000055424007,0.00013060607,0.0014343923,0.000026718433,0.83868015,0.1223281,0.0039384807,0.02545729,0.00016747187],"about_ca_topic_score_codex":0.001960662,"about_ca_topic_score_gemma":0.0028759022,"teacher_disagreement_score":0.0034103307,"about_ca_system_score_codex":0.0003171902,"about_ca_system_score_gemma":0.00061037764,"threshold_uncertainty_score":0.011408746},"labels":[],"label_agreement":null},{"id":"W2128453830","doi":"10.1109/avss.2011.6027311","title":"Multi-tasking smart cameras for intelligent video surveillance systems","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Zoom; Set (abstract data type); Smart camera; Real-time computing","score_opus":0.10854699348579258,"score_gpt":0.31216704797209455,"score_spread":0.20362005448630197,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2128453830","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055719677,0.00059202575,0.9392686,0.00021316849,0.000053364085,0.000079109566,0.000030733976,0.0006503058,0.003392992],"genre_scores_gemma":[0.647456,0.0004749767,0.34908548,0.0001414214,0.000042913718,0.00009966441,0.0000791469,0.000036754456,0.0025836772],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981135,0.0000401873,0.000009469122,0.000052638432,0.0000684455,0.000018057846],"domain_scores_gemma":[0.999808,0.00005548828,0.000032188043,0.000031217398,0.0000487504,0.000024328625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027403736,0.00032019906,0.0002573892,0.00017632668,0.00020915753,0.00042003152,0.0005015623,0.00035548795,0.0011316432],"category_scores_gemma":[0.00051194575,0.00013436274,0.00016977784,0.00013411095,0.00025911623,0.00066169904,0.00036370396,0.0004127405,0.00026067236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041434015,0.00024383624,0.002648533,0.00025944153,0.000063608604,0.00024176452,0.0002662785,0.14329484,0.4859038,0.01998632,0.00309277,0.34358463],"study_design_scores_gemma":[0.000050853796,0.00033514795,0.0020448526,0.000023350187,0.000027068098,0.00019852768,0.00006286623,0.87412244,0.10713308,0.0052527725,0.0107232155,0.000025851994],"about_ca_topic_score_codex":0.00085786957,"about_ca_topic_score_gemma":0.0015263958,"teacher_disagreement_score":0.0011316432,"about_ca_system_score_codex":0.0002866056,"about_ca_system_score_gemma":0.0002559903,"threshold_uncertainty_score":0.0037857294},"labels":[],"label_agreement":null},{"id":"W2129620074","doi":"10.1109/icip.2009.5414294","title":"Tracking of multiple interacting objects using a novel prediction model","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Outlier; Exploit; Tracking (education); Artificial intelligence; Task (project management); Object (grammar); Field (mathematics); Multiple Models; Video tracking; Machine learning; Algorithm; Computer vision; Data mining; Mathematics","score_opus":0.08666341485300891,"score_gpt":0.3334541046507352,"score_spread":0.2467906897977263,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2129620074","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":[0.011088691,0.00010222348,0.98797613,0.00011494764,0.000037666287,0.000015085471,0.000021598658,0.00035389268,0.00028982837],"genre_scores_gemma":[0.63616484,0.0004823223,0.35874113,0.00020826381,0.00018406525,0.00015508897,0.0003987851,0.00010642584,0.0035590343],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990121,0.00013388236,0.000045267356,0.00037491095,0.00034963642,0.000084143365],"domain_scores_gemma":[0.99863404,0.0006084001,0.00023084162,0.00020015168,0.00025093422,0.0000755759],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017911973,0.00079013547,0.0013456829,0.0005856892,0.00046218903,0.0010334245,0.0026737307,0.0016054475,0.00051080633],"category_scores_gemma":[0.003343482,0.0005895891,0.0006849878,0.00094027433,0.00062629295,0.0022483328,0.0012330525,0.0018344694,0.00029972263],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021502793,0.00017371461,0.004838094,0.00007479456,0.00010410107,0.00024890967,0.00012920484,0.78242743,0.012548468,0.010324486,0.002028505,0.18688723],"study_design_scores_gemma":[0.0000055294067,0.00001669168,0.00014792544,0.0000011787548,0.000006280619,0.000026199963,0.0000016911506,0.99842453,0.00046720807,0.00072078867,0.00017765615,0.0000044479807],"about_ca_topic_score_codex":0.0050828867,"about_ca_topic_score_gemma":0.0037709915,"teacher_disagreement_score":0.0050828867,"about_ca_system_score_codex":0.0006352869,"about_ca_system_score_gemma":0.00088998716,"threshold_uncertainty_score":0.010106564},"labels":[],"label_agreement":null},{"id":"W2130026429","doi":"10.1109/iccvw.2015.79","title":"The Visual Object Tracking VOT2015 Challenge Results","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":705,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Javna Agencija za Raziskovalno Dejavnost RS; European Commission","keywords":"BitTorrent tracker; Computer science; Benchmark (surveying); Artificial intelligence; Eye tracking; Object (grammar); Computer vision; Video tracking; Frame (networking); Tracking (education); Bounding overwatch; Term (time); Annotation; Visualization; Minimum bounding box; Object detection; Pattern recognition (psychology); Image (mathematics); Geography","score_opus":0.09512999012652176,"score_gpt":0.3695303862370754,"score_spread":0.27440039611055367,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2130026429","genre_codex":"dataset","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11551385,0.03861845,0.23567957,0.0052240454,0.024999429,0.0069464347,0.38604102,0.094235,0.09274223],"genre_scores_gemma":[0.09588922,0.0019955053,0.0724057,0.0015065263,0.0011658059,0.0015459943,0.79445136,0.0033259671,0.02771393],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9874462,0.002518661,0.0010838329,0.0034667694,0.0041757217,0.0013088629],"domain_scores_gemma":[0.9917036,0.0017897803,0.00043795665,0.0022924603,0.00287627,0.0008998254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010952344,0.0064106355,0.0034262403,0.0035346143,0.0021924784,0.005358669,0.0042304085,0.005151418,0.013896073],"category_scores_gemma":[0.023895307,0.000672654,0.00298534,0.002579789,0.0011328937,0.003795097,0.005370838,0.0029517005,0.014968001],"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":[0.0011766839,0.00062383054,0.002994181,0.0019502172,0.00054405606,0.00038762088,0.00014300467,0.016148731,0.0061712214,0.002984457,0.7857848,0.18109119],"study_design_scores_gemma":[0.0015299349,0.0028802156,0.027028443,0.0018885339,0.0008117647,0.0036344565,0.00083396694,0.2900465,0.03593654,0.02731468,0.60757774,0.00051720266],"about_ca_topic_score_codex":0.031201066,"about_ca_topic_score_gemma":0.03390812,"teacher_disagreement_score":0.031201066,"about_ca_system_score_codex":0.0030305898,"about_ca_system_score_gemma":0.0035370726,"threshold_uncertainty_score":0.0620389},"labels":[],"label_agreement":null},{"id":"W2131857708","doi":"10.1109/iscas.2008.4541820","title":"Thermo-visual video fusion using probabilistic graphical model for human tracking","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Graphical model; Artificial intelligence; Exploit; Probabilistic logic; Video tracking; Computer vision; Statistical model; Bayesian inference; Sensor fusion; Tracking (education); Data modeling; Process (computing); Maximization; Calibration; Expectation–maximization algorithm; Bayesian probability; Inference; Data mining; Pattern recognition (psychology); Object (grammar); Maximum likelihood; Mathematics","score_opus":0.13003189519864947,"score_gpt":0.36640237418232974,"score_spread":0.23637047898368027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2131857708","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":[0.005158118,0.00012438749,0.9935961,0.000093775554,0.000016452195,0.000010252898,0.00006102703,0.00040015974,0.0005396981],"genre_scores_gemma":[0.69258124,0.0005704198,0.30375725,0.00019214346,0.00007682107,0.00011515832,0.00060071005,0.000115277544,0.0019911744],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99914336,0.00032113944,0.000030741674,0.00021796628,0.00022292463,0.00006383335],"domain_scores_gemma":[0.99934644,0.0003197673,0.00009484241,0.00010311515,0.000105568004,0.000030254076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001187458,0.0006331456,0.0008321989,0.0012070385,0.00026061843,0.00090301904,0.0010513569,0.000948742,0.0012973968],"category_scores_gemma":[0.0034772987,0.0003738012,0.0013851969,0.0010395829,0.00052075944,0.0014896011,0.00096027146,0.0007990082,0.00055641145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029748192,0.00014404353,0.0016964071,0.0001226452,0.0001900095,0.00018263748,0.00014139469,0.7342264,0.010820036,0.042854823,0.0021328307,0.2071913],"study_design_scores_gemma":[0.000008246422,0.000023956394,0.00034471392,0.0000058243745,0.000018171488,0.00004912894,0.000006664375,0.9837234,0.0013495439,0.013840003,0.00061347114,0.000016860815],"about_ca_topic_score_codex":0.0038339854,"about_ca_topic_score_gemma":0.003750511,"teacher_disagreement_score":0.0038339854,"about_ca_system_score_codex":0.00083951466,"about_ca_system_score_gemma":0.00056519726,"threshold_uncertainty_score":0.007623315},"labels":[],"label_agreement":null},{"id":"W2132284553","doi":"10.1109/icpr.2008.4761488","title":"Real-time foreground segmentation on GPUs using local online learning and global graph cut optimization","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Pixel; Graphics processing unit; Graphics; Segmentation; Artificial intelligence; Graph; Perspective (graphical); Cut; Image segmentation; General-purpose computing on graphics processing units; Computer vision; Machine learning; Computer graphics (images); Theoretical computer science; Parallel computing","score_opus":0.09196735871716002,"score_gpt":0.3315644954189243,"score_spread":0.23959713670176427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2132284553","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":[0.013038914,0.00010324958,0.9822191,0.00009451178,0.000018469113,0.00002519575,0.00003252535,0.0036244583,0.00084347284],"genre_scores_gemma":[0.26980722,0.00010540864,0.72732496,0.000105075516,0.000034503028,0.00006975895,0.0002465624,0.00059371296,0.0017128377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942976,0.0001329021,0.000021520904,0.00015900943,0.00017795006,0.00007876982],"domain_scores_gemma":[0.9991843,0.0003328483,0.00008181128,0.00016001824,0.00017694138,0.00006396266],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065879285,0.0009970289,0.0011964724,0.001045462,0.00040902823,0.0014415435,0.0021710785,0.0012387189,0.0025584116],"category_scores_gemma":[0.001878574,0.00058411964,0.00053707743,0.0011014227,0.00056253537,0.0015758496,0.0011008522,0.0012863667,0.00081470737],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036490645,0.00013329081,0.0008986553,0.000072992356,0.00007336999,0.000108797816,0.00008165203,0.40690222,0.02996012,0.0036888036,0.003320175,0.554395],"study_design_scores_gemma":[0.0000066715684,0.000010549237,0.00010049908,0.0000014069767,0.0000028148886,0.000012402827,0.0000055826886,0.996212,0.0023267705,0.0010760812,0.00024258361,0.0000027024505],"about_ca_topic_score_codex":0.009238249,"about_ca_topic_score_gemma":0.013471551,"teacher_disagreement_score":0.009238249,"about_ca_system_score_codex":0.0010734055,"about_ca_system_score_gemma":0.0010230283,"threshold_uncertainty_score":0.01836896},"labels":[],"label_agreement":null},{"id":"W2133886074","doi":"10.1109/vr.2012.6180895","title":"3DTown: The automatic urban awareness project","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Zoom; Context (archaeology); Projection (relational algebra); Computer graphics (images); Geography; Engineering","score_opus":0.060313476983668526,"score_gpt":0.34202538110792347,"score_spread":0.28171190412425495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2133886074","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":[0.11584435,0.001508426,0.66597307,0.0013054428,0.0010285729,0.0016406839,0.01990366,0.101164244,0.09163158],"genre_scores_gemma":[0.3335557,0.00062287756,0.55043966,0.00040868932,0.00015956716,0.0012355292,0.052459437,0.0051704873,0.055947993],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932206,0.00018551548,0.000012802582,0.00021345475,0.0001765245,0.000089720044],"domain_scores_gemma":[0.9995453,0.00006867054,0.000018410035,0.00013298276,0.00012976144,0.00010483497],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012748174,0.0011830854,0.00076041004,0.0008609954,0.00073118275,0.0012138145,0.0009369556,0.0007499283,0.011202767],"category_scores_gemma":[0.00083982793,0.0003565188,0.00044481986,0.0007380444,0.0004889115,0.0013506949,0.0019290881,0.0008213428,0.0041784826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015618821,0.00072483305,0.009003702,0.0003726274,0.0001404782,0.0005545465,0.00076781004,0.02235582,0.07344875,0.023720022,0.16871054,0.6986389],"study_design_scores_gemma":[0.00083916186,0.000949246,0.011702485,0.0001058453,0.00011328501,0.0007580495,0.0006385076,0.4007705,0.06253831,0.019662706,0.5017893,0.00013271318],"about_ca_topic_score_codex":0.0065222546,"about_ca_topic_score_gemma":0.008062544,"teacher_disagreement_score":0.011202767,"about_ca_system_score_codex":0.0004971919,"about_ca_system_score_gemma":0.0013438981,"threshold_uncertainty_score":0.037476957},"labels":[],"label_agreement":null},{"id":"W2134288846","doi":"10.1109/icpr.2008.4761133","title":"Robust region-based background subtraction and shadow removing using color and gradient information","year":2008,"lang":"en","type":"article","venue":"Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Background subtraction; Robustness (evolution); Artificial intelligence; Computer vision; Shadow (psychology); Computer science; Subtraction; Foreground detection; Pixel; Pattern recognition (psychology); Mathematics","score_opus":0.20474901223181774,"score_gpt":0.31002091248184044,"score_spread":0.1052719002500227,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2134288846","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021496769,0.0008109024,0.9739948,0.00004094978,0.00007512857,0.000041101117,0.000046933805,0.0025363152,0.0009571262],"genre_scores_gemma":[0.16215155,0.00068287284,0.833753,0.00009290094,0.00007923121,0.00005158649,0.0002906134,0.00028303373,0.002615241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950314,0.000047476682,0.000021467584,0.00011529975,0.00025883678,0.000053793043],"domain_scores_gemma":[0.99960333,0.00008995738,0.00005383606,0.00006519465,0.00016093583,0.000026721837],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004735863,0.0008669256,0.0010543725,0.0013122814,0.00036495694,0.00075147644,0.001224606,0.00066239026,0.0015262004],"category_scores_gemma":[0.00095457706,0.00048741695,0.00071608706,0.00080949394,0.00031658204,0.0009950977,0.0005881388,0.0006226897,0.0014614069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028898517,0.00010248649,0.001311046,0.00026276617,0.0001217575,0.00022616966,0.000115721254,0.010946834,0.425985,0.0022683996,0.0015534092,0.5568175],"study_design_scores_gemma":[0.0000661304,0.0002551881,0.005201822,0.000036941765,0.00015348793,0.0020230743,0.00007133231,0.36696932,0.6059435,0.0018971269,0.017265478,0.00011652939],"about_ca_topic_score_codex":0.0016086211,"about_ca_topic_score_gemma":0.0020530734,"teacher_disagreement_score":0.0016086211,"about_ca_system_score_codex":0.00032692036,"about_ca_system_score_gemma":0.0006359772,"threshold_uncertainty_score":0.0051056743},"labels":[],"label_agreement":null},{"id":"W2136115617","doi":"10.1049/iet-cvi.2010.0115","title":"Depth space partitioning for omni-stereo object tracking","year":2012,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Science Foundation","keywords":"Computer vision; Artificial intelligence; Catadioptric system; Stereo imaging; Computer science; Stereo camera; Stereo cameras; Computer stereo vision; Stereopsis; Pixel; Cut; Tracking (education); Image (mathematics); Image segmentation","score_opus":0.04680923992843832,"score_gpt":0.34299673546023945,"score_spread":0.2961874955318011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136115617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01432015,0.00014141676,0.9845771,0.00002029173,0.000010732806,0.000029104816,0.00003767456,0.00022426964,0.0006391136],"genre_scores_gemma":[0.2615738,0.00019741993,0.7360215,0.00004661664,0.000017490545,0.0001037427,0.00032156677,0.000083178,0.0016346698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964094,0.000054628414,0.000018104356,0.00008891904,0.00015051877,0.00004688279],"domain_scores_gemma":[0.99977654,0.00007217248,0.00002914119,0.000037906324,0.00006786452,0.000016340056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029915594,0.00054233614,0.00051954424,0.0008796142,0.00034120327,0.0005060259,0.00081019546,0.0005263489,0.001222359],"category_scores_gemma":[0.0007599001,0.0003208738,0.00048575623,0.0006380782,0.00022546214,0.0007344028,0.0007739478,0.0004295048,0.00032188886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033523765,0.000116136114,0.0010948143,0.0001112743,0.000046380283,0.00005982898,0.00020544659,0.16393057,0.07103206,0.010091044,0.0017072094,0.75127006],"study_design_scores_gemma":[0.000021710326,0.00009494345,0.0010688623,0.000008063469,0.000014382064,0.000117814154,0.00004211443,0.9695142,0.02178865,0.0043378603,0.0029731423,0.000018247612],"about_ca_topic_score_codex":0.00326242,"about_ca_topic_score_gemma":0.003340628,"teacher_disagreement_score":0.00326242,"about_ca_system_score_codex":0.00060556154,"about_ca_system_score_gemma":0.000518557,"threshold_uncertainty_score":0.0064868927},"labels":[],"label_agreement":null},{"id":"W2136545936","doi":"10.1109/mlsp.2005.1532899","title":"Video Object Segmentation and Tracking Using Probabilistic Fuzzy C-Means","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Initialization; Video tracking; Probabilistic logic; Pattern recognition (psychology); Cluster analysis; Segmentation; Image segmentation; Motion estimation; Scale-space segmentation; Object (grammar)","score_opus":0.032385834963516553,"score_gpt":0.2956303207025642,"score_spread":0.26324448573904763,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136545936","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0026825734,0.00007649096,0.99660057,0.000025653413,0.000008096692,0.000020795404,0.000013224567,0.00029317872,0.00027942884],"genre_scores_gemma":[0.10580938,0.00024043578,0.8926636,0.00007195279,0.000031983836,0.000108120796,0.000121811594,0.000080050195,0.000872561],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898916,0.00016685732,0.00005040238,0.00028492874,0.0004461809,0.0000623866],"domain_scores_gemma":[0.9991416,0.00033047172,0.00010781762,0.0001337148,0.00025950058,0.000026857411],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012183981,0.00068784365,0.000919065,0.0016740343,0.0006931809,0.0009672858,0.001587553,0.0012467068,0.0008239689],"category_scores_gemma":[0.0028397415,0.0005933894,0.00094825483,0.0012609637,0.0008905631,0.001543586,0.00078218034,0.0008833206,0.00042207493],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015082095,0.000070825474,0.0010258416,0.00015451512,0.00012293305,0.00009014506,0.00015903258,0.41179886,0.038641952,0.017824356,0.0021187689,0.52784187],"study_design_scores_gemma":[0.000007080846,0.00002329156,0.00043667303,0.000009343081,0.000011566039,0.000048022448,0.000008746963,0.9864334,0.006412248,0.005391925,0.001191444,0.000026245509],"about_ca_topic_score_codex":0.010637515,"about_ca_topic_score_gemma":0.008750507,"teacher_disagreement_score":0.010637515,"about_ca_system_score_codex":0.001175144,"about_ca_system_score_gemma":0.0013875748,"threshold_uncertainty_score":0.021151185},"labels":[],"label_agreement":null},{"id":"W2136740852","doi":"10.1109/isda.2009.12","title":"Intelligent Cooperative Tracking in Multi-camera Systems","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Tracking (education); Zoom; Camera auto-calibration; Tracking system; Event (particle physics); Camera resectioning; Smart camera; Particle filter; Video tracking; Kalman filter; Video processing; Engineering","score_opus":0.08120889871929997,"score_gpt":0.35717364832101495,"score_spread":0.27596474960171496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136740852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016609166,0.00055063475,0.98133713,0.00007488543,0.00003880617,0.00002406223,0.0000057818233,0.00027823655,0.0010813227],"genre_scores_gemma":[0.7600184,0.0006094046,0.2360683,0.00009425443,0.00011402112,0.00009116749,0.00004858025,0.00003817532,0.0029177868],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989466,0.00028697462,0.00005561655,0.00029147716,0.00031766953,0.00010159721],"domain_scores_gemma":[0.9988695,0.00044013545,0.00019627782,0.00018207502,0.00024304268,0.000068958696],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012692526,0.00058669545,0.0008385613,0.00060943706,0.00049923966,0.0010151317,0.0010705512,0.0010779261,0.0005790151],"category_scores_gemma":[0.0021788431,0.00042574253,0.0004796967,0.00058591366,0.000672497,0.0014157278,0.0010956633,0.00067388825,0.00026076633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033158984,0.00018618269,0.0031415173,0.00022595604,0.00023860618,0.0006267734,0.0006928301,0.6034199,0.044935483,0.026516235,0.0016062025,0.3180788],"study_design_scores_gemma":[0.000018887857,0.0000877438,0.0005465904,0.00000911461,0.000025012514,0.00010214263,0.000035867477,0.9881819,0.004407824,0.0044755293,0.0020924436,0.000017093824],"about_ca_topic_score_codex":0.0025962044,"about_ca_topic_score_gemma":0.002028221,"teacher_disagreement_score":0.0025962044,"about_ca_system_score_codex":0.00061542494,"about_ca_system_score_gemma":0.00041972368,"threshold_uncertainty_score":0.0067124963},"labels":[],"label_agreement":null},{"id":"W2136806931","doi":"10.1109/crv.2012.33","title":"Robust Background Subtraction Using Geodesic Active Contours in ICA Subspace for Video Surveillance Applications","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Solink (Canada); Institut National de la Recherche Scientifique; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Background subtraction; Artificial intelligence; Computer science; Subspace topology; Computer vision; Foreground detection; Geodesic; Subtraction; Background image; Process (computing); Image subtraction; Pattern recognition (psychology); Robustness (evolution); Change detection; Image (mathematics); Image processing; Pixel; Mathematics; Binary image","score_opus":0.12868408583625313,"score_gpt":0.350465346501661,"score_spread":0.22178126066540788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2136806931","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":[0.008388233,0.00016749091,0.9900775,0.00004474458,0.000019564877,0.000026146692,0.00002232905,0.00061415735,0.0006399199],"genre_scores_gemma":[0.17143944,0.0005735603,0.8254312,0.000065692155,0.000041197916,0.00007091807,0.0002615815,0.00028872097,0.0018277604],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996427,0.00008594451,0.000015139224,0.00006971632,0.00015967601,0.000026782951],"domain_scores_gemma":[0.9996247,0.00012702239,0.00003555607,0.000055743545,0.00013514848,0.000021873208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00084240595,0.00079891796,0.0006416674,0.0011111218,0.00031383688,0.00083665136,0.0010811578,0.00066357496,0.0012500922],"category_scores_gemma":[0.001819296,0.0003698256,0.0007552055,0.0011189539,0.00040750203,0.0011020908,0.0006802178,0.00079610344,0.0006947215],"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":[0.00026405815,0.00012389223,0.000994979,0.00013889067,0.00009442543,0.0001352787,0.00017048055,0.19372126,0.08724803,0.010967705,0.0028568315,0.7032841],"study_design_scores_gemma":[0.000009273303,0.000040254665,0.00052357104,0.000009114701,0.000015740352,0.00007077221,0.000019787423,0.97635764,0.017395547,0.0032442722,0.0022979595,0.000016054079],"about_ca_topic_score_codex":0.0030790004,"about_ca_topic_score_gemma":0.0035429767,"teacher_disagreement_score":0.0030790004,"about_ca_system_score_codex":0.00048592547,"about_ca_system_score_gemma":0.0007230359,"threshold_uncertainty_score":0.006122172},"labels":[],"label_agreement":null},{"id":"W2137917905","doi":"10.1109/icma.2005.1626676","title":"Tracking of rigid-bodies for autonomous surveillance","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer vision; Artificial intelligence; Pose; Computer science; Cube (algebra); Kalman filter; Tracking (education); Optical flow; Orientation (vector space); Video tracking; Image plane; Computer graphics (images); Object (grammar); Mathematics; Image (mathematics)","score_opus":0.025480487684434228,"score_gpt":0.2903751039768669,"score_spread":0.26489461629243266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2137917905","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":[0.016608644,0.00038173082,0.97964865,0.00006179108,0.000041306044,0.00002703301,0.00007925494,0.0010943287,0.0020571428],"genre_scores_gemma":[0.5267018,0.0008699845,0.46864718,0.000085842774,0.000045533525,0.00010279513,0.0005432627,0.00017547215,0.0028281405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975246,0.000046736946,0.000007839983,0.00006184063,0.00011158128,0.000019574414],"domain_scores_gemma":[0.9996486,0.00009804256,0.00006128423,0.00009492837,0.00007989846,0.000017367307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003019667,0.000394849,0.00046285897,0.0005352491,0.00033630055,0.00048563097,0.0006103456,0.00055377773,0.0017122248],"category_scores_gemma":[0.0016531361,0.00034396554,0.00043029102,0.0005258817,0.00040170923,0.0007286949,0.0006626172,0.0004646916,0.0007960564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024165977,0.00006985382,0.0029204593,0.00019863134,0.00008110259,0.00014013698,0.00020007978,0.34126163,0.09881367,0.016631663,0.005014879,0.53442615],"study_design_scores_gemma":[0.000013848395,0.00004959698,0.0015460196,0.000016333992,0.000012867219,0.000113061964,0.000032007443,0.96982735,0.016529633,0.0061942986,0.005649814,0.000015268912],"about_ca_topic_score_codex":0.004686073,"about_ca_topic_score_gemma":0.0041376664,"teacher_disagreement_score":0.004686073,"about_ca_system_score_codex":0.0004916715,"about_ca_system_score_gemma":0.00047557545,"threshold_uncertainty_score":0.0093176365},"labels":[],"label_agreement":null},{"id":"W2139047213","doi":"10.1007/s11263-007-0075-7","title":"Incremental Learning for Robust Visual Tracking","year":2007,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3101,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Honda Research Institute, USA","keywords":"Computer science; Artificial intelligence; Tracking (education); Subspace topology; Computer vision; Representation (politics); Active appearance model; Principal component analysis; Pattern recognition (psychology); Eye tracking; Forgetting; Video tracking; Range (aeronautics); Object (grammar); Image (mathematics)","score_opus":0.032102459077935054,"score_gpt":0.3744627854844399,"score_spread":0.3423603264065048,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139047213","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":[0.006183305,0.00031021755,0.9923286,0.000052103762,0.000047900547,0.000023422648,0.000033250948,0.00066847046,0.0003527816],"genre_scores_gemma":[0.37799305,0.0005842917,0.61474824,0.00024042506,0.0001818559,0.00025746424,0.00059076835,0.0004252536,0.0049786502],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990742,0.00019941131,0.000059904753,0.00029810547,0.00026935796,0.000098899516],"domain_scores_gemma":[0.9965886,0.0019601993,0.00022992412,0.00060770725,0.0005226413,0.00009097088],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020098877,0.0010897482,0.0020904217,0.0013866877,0.0005269422,0.0009994226,0.0031310814,0.0017296458,0.0028840895],"category_scores_gemma":[0.008665325,0.0011463823,0.0010859945,0.0013851,0.0009305462,0.0021892253,0.002189293,0.002139969,0.0010232632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046930579,0.00016017015,0.0008459025,0.00018698235,0.00013953619,0.000102416154,0.00008593845,0.2559152,0.011083611,0.012533825,0.0045230924,0.71395403],"study_design_scores_gemma":[0.00001354849,0.00003749294,0.00017106009,0.0000052264127,0.000015001146,0.000028716939,0.0000036359422,0.9924098,0.0019627959,0.004823727,0.00052108796,0.000007923154],"about_ca_topic_score_codex":0.0050570276,"about_ca_topic_score_gemma":0.004592802,"teacher_disagreement_score":0.0050570276,"about_ca_system_score_codex":0.0008211816,"about_ca_system_score_gemma":0.0008910609,"threshold_uncertainty_score":0.0106294155},"labels":[],"label_agreement":null},{"id":"W2139141650","doi":"10.1109/ccnc.2011.5766610","title":"V2Eye: Enhancement of visual perception from V2V communication","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Modalities; Perception; Human–computer interaction; Vehicular communication systems; Visualization; Artificial intelligence; Telecommunications; Vehicular ad hoc network; Psychology; Wireless ad hoc network; Wireless; Neuroscience","score_opus":0.057668542550730915,"score_gpt":0.32298445804895193,"score_spread":0.26531591549822103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139141650","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02932496,0.0018655311,0.94304556,0.00041482304,0.0004322069,0.00011841961,0.00028982013,0.0043187006,0.0201901],"genre_scores_gemma":[0.55623156,0.0021254893,0.42055067,0.0006105839,0.00038674494,0.0001587307,0.0014786336,0.00047774985,0.017979871],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999629,0.00007849664,0.000010063277,0.00006316893,0.00015095614,0.000068300986],"domain_scores_gemma":[0.9997695,0.000043395186,0.00001172727,0.000057669942,0.00009319816,0.000024541605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046492848,0.0006922296,0.0004982169,0.00043944083,0.00022491527,0.0009593497,0.0009334067,0.0008194818,0.0038189942],"category_scores_gemma":[0.00087183784,0.00018520596,0.0003107817,0.00034014144,0.00034355273,0.0016231827,0.0017060783,0.0006637043,0.001146669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006886156,0.00023499229,0.0014448814,0.00035733427,0.000111676105,0.000485842,0.00021924548,0.0125509845,0.2224539,0.01964599,0.023680948,0.71812564],"study_design_scores_gemma":[0.00019499377,0.0013373944,0.007360971,0.00019402197,0.00015108478,0.0030143247,0.00035917657,0.44079068,0.29480478,0.03929729,0.21226935,0.00022601873],"about_ca_topic_score_codex":0.0010939529,"about_ca_topic_score_gemma":0.0016419447,"teacher_disagreement_score":0.0038189942,"about_ca_system_score_codex":0.00016717146,"about_ca_system_score_gemma":0.0002340684,"threshold_uncertainty_score":0.012775779},"labels":[],"label_agreement":null},{"id":"W2139440051","doi":"10.1109/vecims.2011.6053832","title":"Bringing virtual events into real life in Second Life home automation system","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Home automation; Event (particle physics); Computer science; Automation; Human–computer interaction; Bridge (graph theory); Interface (matter); Embedded system; Living space; Engineering; Operating system","score_opus":0.031207570009920565,"score_gpt":0.26484849247818615,"score_spread":0.2336409224682656,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139440051","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13439162,0.00035635752,0.8478308,0.0002982941,0.0001707477,0.00020941027,0.00007657305,0.005765872,0.010900307],"genre_scores_gemma":[0.8043055,0.00026978712,0.1858562,0.0001998268,0.000069309666,0.00016273357,0.00018322942,0.00019495409,0.008758473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993523,0.00022169713,0.000036217505,0.00012591353,0.00018919967,0.000074680596],"domain_scores_gemma":[0.999405,0.00015322723,0.000053694126,0.0001708459,0.00010221181,0.000114952156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054522906,0.00046911486,0.00043062627,0.00036415795,0.0003834451,0.0015894626,0.0008818254,0.0007812748,0.003801405],"category_scores_gemma":[0.0010702421,0.00027891187,0.0003896835,0.00012492419,0.0005659094,0.0018219652,0.0013774931,0.0005543901,0.000680844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017807259,0.0014808903,0.013422415,0.0008428707,0.00023861411,0.0031362574,0.008198592,0.028579203,0.33890018,0.04418958,0.013437492,0.5457931],"study_design_scores_gemma":[0.00033540517,0.0030352438,0.018353155,0.00020422095,0.00038953865,0.005096063,0.0018468655,0.45236415,0.26425803,0.018987767,0.2347695,0.0003600315],"about_ca_topic_score_codex":0.00044314156,"about_ca_topic_score_gemma":0.0005096071,"teacher_disagreement_score":0.003801405,"about_ca_system_score_codex":0.0002327514,"about_ca_system_score_gemma":0.00022629624,"threshold_uncertainty_score":0.012716949},"labels":[],"label_agreement":null},{"id":"W2139881014","doi":"10.1109/icsipa.2009.5478601","title":"Online body tracking by a PTZ camera in IP surveillance system","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Optical flow; Frame rate; Frame (networking); Tracking system; Diagonal; Feature (linguistics); Tracking (education); Feature extraction; Image (mathematics); Mathematics; Filter (signal processing)","score_opus":0.018387835540195626,"score_gpt":0.28953892240419893,"score_spread":0.2711510868640033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2139881014","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19895922,0.0005215835,0.7936989,0.00015376233,0.000109296176,0.00013469683,0.00008831074,0.00154047,0.00479369],"genre_scores_gemma":[0.7825258,0.00026267517,0.21370776,0.00010685458,0.000055436212,0.00006810524,0.000081412945,0.000040287345,0.0031517763],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963605,0.000073592775,0.000015367823,0.00007363503,0.0001729573,0.000028414648],"domain_scores_gemma":[0.99974424,0.00006580309,0.000036562647,0.00003163277,0.000100978614,0.000020738531],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003310677,0.0002858238,0.00045570257,0.00043342778,0.0001723415,0.00028721776,0.00045713966,0.00050571683,0.0012110837],"category_scores_gemma":[0.00065189664,0.0001640235,0.00017309678,0.0002130754,0.00019451264,0.00057507795,0.00032798274,0.00027338133,0.00042117762],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011201858,0.00013099382,0.008284571,0.00021145659,0.00004359498,0.00056853826,0.00023015011,0.008533855,0.40586486,0.0008694866,0.00288846,0.57125396],"study_design_scores_gemma":[0.00014785533,0.0013657056,0.03331522,0.000059074515,0.0001455178,0.0038008005,0.0001556335,0.70702547,0.24499927,0.00075367646,0.008157727,0.000074117386],"about_ca_topic_score_codex":0.0010986998,"about_ca_topic_score_gemma":0.00126593,"teacher_disagreement_score":0.0012110837,"about_ca_system_score_codex":0.00020428824,"about_ca_system_score_gemma":0.00013977409,"threshold_uncertainty_score":0.004051447},"labels":[],"label_agreement":null},{"id":"W2143508068","doi":"","title":"Public video data set for road transportation applications","year":2014,"lang":"en","type":"article","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Ground truth; Variety (cybernetics); Software; Data science; Reading (process); Data mining; Transport engineering; Machine learning; Artificial intelligence; Engineering","score_opus":0.04983239245495157,"score_gpt":0.2952403059707706,"score_spread":0.24540791351581903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143508068","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016928164,0.00048690106,0.00393911,0.00028817463,0.00025522267,0.00057531666,0.9683966,0.003369267,0.0057613007],"genre_scores_gemma":[0.0073119006,0.00011672316,0.0029122743,0.000032899898,0.000027379923,0.00028099757,0.9882635,0.00007736975,0.0009769514],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99850535,0.00016251247,0.00017891775,0.00039834474,0.0005115538,0.00024337383],"domain_scores_gemma":[0.99755883,0.00021595837,0.00020144379,0.00058266794,0.0011673776,0.00027360607],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093475997,0.0021212043,0.0011852583,0.004276781,0.0012022812,0.0010993639,0.0026201284,0.0021182226,0.009833179],"category_scores_gemma":[0.002824052,0.00034190438,0.0010702274,0.0049539316,0.0004666808,0.0014554565,0.0013959251,0.001754334,0.014220133],"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":[0.000830434,0.00089063426,0.006005804,0.0017403149,0.00014255977,0.00048203307,0.00018657121,0.004167209,0.0071366034,0.0017554435,0.8996255,0.07703704],"study_design_scores_gemma":[0.00048597535,0.00056425086,0.100574695,0.0006798374,0.00020024578,0.0011588883,0.0014000215,0.029356506,0.017778426,0.0028774228,0.84463924,0.00028464923],"about_ca_topic_score_codex":0.05089065,"about_ca_topic_score_gemma":0.06696509,"teacher_disagreement_score":0.05089065,"about_ca_system_score_codex":0.0017960619,"about_ca_system_score_gemma":0.0022686557,"threshold_uncertainty_score":0.1011889},"labels":[],"label_agreement":null},{"id":"W2143516822","doi":"10.1007/978-3-642-21593-3_32","title":"Real-Time People Detection in Videos Using Geometrical Features and Adaptive Boosting","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec en Outaouais","funders":"","keywords":"Silhouette; Computer science; Boosting (machine learning); AdaBoost; Artificial intelligence; Pedestrian detection; Computer vision; Pattern recognition (psychology); Feature (linguistics); Object detection; Pedestrian; Support vector machine","score_opus":0.032860726538263794,"score_gpt":0.2736301474736156,"score_spread":0.24076942093535178,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2143516822","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028400822,0.0005888846,0.96851707,0.000066733824,0.00009650879,0.000047746937,0.00005166004,0.0008779444,0.0013526712],"genre_scores_gemma":[0.48256478,0.00064715795,0.5134548,0.00010666031,0.00015421184,0.00006928833,0.0002949172,0.00014279956,0.002565324],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993781,0.000118531105,0.00002309558,0.00016823196,0.00021084337,0.00010111727],"domain_scores_gemma":[0.9994593,0.0001985146,0.000050633895,0.000067852496,0.00018235357,0.000041242314],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013647343,0.0008851337,0.0016132441,0.001280001,0.0003222045,0.0006423426,0.0012933563,0.0008148355,0.0011848724],"category_scores_gemma":[0.0015935124,0.0005252363,0.00096369284,0.0010929877,0.0004634702,0.0010342933,0.0009280157,0.0007544786,0.00089092297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005357619,0.00016025935,0.0019347004,0.00014600634,0.00012558488,0.000088203065,0.000058878024,0.06806536,0.08506744,0.0025668559,0.0029763668,0.83827454],"study_design_scores_gemma":[0.0000147101355,0.00013553182,0.002678082,0.000010108267,0.00005099466,0.00021250833,0.000015007321,0.9726309,0.020856095,0.0021132615,0.0012658498,0.000016901657],"about_ca_topic_score_codex":0.0014215641,"about_ca_topic_score_gemma":0.0018726421,"teacher_disagreement_score":0.0016132441,"about_ca_system_score_codex":0.00040378261,"about_ca_system_score_gemma":0.0003723261,"threshold_uncertainty_score":0.0072175264},"labels":[],"label_agreement":null},{"id":"W2144913102","doi":"10.1109/wacv.2011.5711560","title":"Classification of traffic video based on a spatiotemporal orientation analysis","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Computer science; Histogram; Robustness (evolution); Artificial intelligence; Segmentation; Orientation (vector space); Computer vision; Pattern recognition (psychology); Support vector machine; Data mining; Mathematics","score_opus":0.07012738943117656,"score_gpt":0.3084220560783405,"score_spread":0.23829466664716392,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2144913102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41421017,0.000506821,0.5718103,0.00024348618,0.00015898894,0.00027261098,0.0027174081,0.0031937463,0.00688646],"genre_scores_gemma":[0.8506541,0.0005052347,0.14425106,0.000062740335,0.0001299715,0.00007859299,0.0023363412,0.000071991584,0.0019099974],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997632,0.000019442585,0.000016326847,0.000067655856,0.00009067984,0.000042635318],"domain_scores_gemma":[0.99959403,0.000046174464,0.000075989694,0.00003950095,0.00019423237,0.00005006807],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023389907,0.0003538418,0.0003920971,0.003587028,0.00022219954,0.0007111121,0.00030488902,0.00027163926,0.0008799478],"category_scores_gemma":[0.0010431166,0.00010314775,0.00031601934,0.001631441,0.00019707826,0.00054664264,0.00024448652,0.0003128424,0.0006961255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056147494,0.0003269406,0.038997114,0.00010423939,0.00006773769,0.0002535752,0.00011068471,0.020380026,0.121022135,0.00366206,0.004924075,0.8095899],"study_design_scores_gemma":[0.000020621248,0.0002018597,0.052390344,0.000032196032,0.0000764999,0.0004937686,0.00018447723,0.89930624,0.03918762,0.0028392384,0.0052205157,0.000046633588],"about_ca_topic_score_codex":0.0051030223,"about_ca_topic_score_gemma":0.004790495,"teacher_disagreement_score":0.0051030223,"about_ca_system_score_codex":0.00041104932,"about_ca_system_score_gemma":0.00031958788,"threshold_uncertainty_score":0.010146618},"labels":[],"label_agreement":null},{"id":"W2145160840","doi":"10.1109/icip.2007.4379279","title":"A Multi-Camera Surveillance System that Estimates Quality-of-View Measurement","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Measure (data warehouse); Artificial intelligence; Single camera; Multi camera; Stability (learning theory); Camera resectioning; Camera auto-calibration; Selection (genetic algorithm); Quality (philosophy); Data mining; Machine learning","score_opus":0.1655921072386748,"score_gpt":0.3711307589605989,"score_spread":0.2055386517219241,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145160840","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":[0.019850964,0.00048476056,0.97686917,0.0000592105,0.00006801517,0.0000985345,0.000114816925,0.0014917941,0.00096268335],"genre_scores_gemma":[0.2772326,0.0003573706,0.7199796,0.000127632,0.000107348875,0.00017596998,0.0003979598,0.00012251164,0.0014989984],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987239,0.00022069228,0.000072123315,0.0004602415,0.00045144357,0.00007149642],"domain_scores_gemma":[0.9972338,0.0006623793,0.00044973343,0.00044685067,0.0009936788,0.00021354751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013361851,0.0007839866,0.0012866717,0.0013957423,0.00045552148,0.00094221416,0.001360954,0.0010548115,0.0017536033],"category_scores_gemma":[0.0031648895,0.00048417432,0.00047544466,0.0008089226,0.00029060713,0.0014067596,0.00091493054,0.0009101736,0.0010710893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007032607,0.00025457292,0.013078292,0.00036908683,0.00021022356,0.00017081766,0.00019383774,0.015640084,0.1648164,0.0027963913,0.0045516985,0.7972152],"study_design_scores_gemma":[0.00014602765,0.0010213014,0.027300349,0.000085181884,0.0002056482,0.0013973307,0.000119659664,0.81698936,0.13786271,0.002166047,0.012526618,0.00017980389],"about_ca_topic_score_codex":0.0017328176,"about_ca_topic_score_gemma":0.0021368861,"teacher_disagreement_score":0.0017536033,"about_ca_system_score_codex":0.0004977116,"about_ca_system_score_gemma":0.00057570485,"threshold_uncertainty_score":0.0070664883},"labels":[],"label_agreement":null},{"id":"W2145536551","doi":"10.1109/tcsvt.2008.928888","title":"Human Activity Recognition Based on Silhouette Directionality","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Silhouette; Artificial intelligence; Computer vision; Computer science; Activity recognition; Cluster analysis; Feature vector; Directionality; Pattern recognition (psychology); Background subtraction; Zoom; Feature extraction; Motion (physics); Feature (linguistics); Pixel; Engineering","score_opus":0.06953694203647971,"score_gpt":0.29758080311798496,"score_spread":0.22804386108150526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2145536551","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18474789,0.00067229517,0.8084013,0.00007423139,0.000045598892,0.000072786606,0.0004588475,0.001904492,0.0036226641],"genre_scores_gemma":[0.7569281,0.0007284517,0.2394378,0.00003197695,0.00004284305,0.000045216817,0.0007792848,0.00013564689,0.0018706599],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980134,0.000025455995,0.00001071372,0.00006069047,0.000077171964,0.000024741053],"domain_scores_gemma":[0.99946827,0.0001193506,0.000104161416,0.000060010618,0.00019817268,0.000050084927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021365417,0.00037040637,0.00034676283,0.0016662173,0.00012993031,0.0005952851,0.00025190486,0.00023430042,0.0011400839],"category_scores_gemma":[0.0013845895,0.000196117,0.0002597195,0.00088556774,0.00024999076,0.0005409254,0.00024834182,0.00025702728,0.000763012],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053211075,0.0000494723,0.012704429,0.0001723364,0.00005901942,0.00013582139,0.00024031126,0.017884208,0.29355356,0.0020955903,0.0017442523,0.6708289],"study_design_scores_gemma":[0.000036143338,0.00028947296,0.08309427,0.000052672363,0.0000849659,0.0014956807,0.00020776136,0.7065383,0.19517386,0.0049650692,0.007964865,0.000096973316],"about_ca_topic_score_codex":0.0011685994,"about_ca_topic_score_gemma":0.0017483325,"teacher_disagreement_score":0.0016662173,"about_ca_system_score_codex":0.00021055677,"about_ca_system_score_gemma":0.00016823677,"threshold_uncertainty_score":0.003813982},"labels":[],"label_agreement":null},{"id":"W2147718983","doi":"10.1109/crv.2008.25","title":"Detection and Tracking of Multiple Moving Objects in Real-World Scenarios using Attributed Relational Graph","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Graph; Pattern recognition (psychology); Object detection; Video tracking; Block (permutation group theory); Tracking (education); Object (grammar); Mathematics; Theoretical computer science","score_opus":0.08398953751496105,"score_gpt":0.30219127132991536,"score_spread":0.2182017338149543,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147718983","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01825051,0.000119511424,0.9801209,0.00005739369,0.00001142057,0.00002485314,0.000076368866,0.0008913634,0.000447591],"genre_scores_gemma":[0.4204236,0.00032009472,0.5768081,0.000059522852,0.000020787813,0.000050497554,0.0005686021,0.00012306022,0.0016258024],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99932504,0.0001094953,0.000035226236,0.00025730184,0.00021995202,0.00005298706],"domain_scores_gemma":[0.99894375,0.00033655125,0.00023901512,0.00021214334,0.00020202609,0.00006643834],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006080446,0.0005288601,0.00065548107,0.0024550648,0.0004948617,0.0010827829,0.0016489704,0.0007026015,0.0008030124],"category_scores_gemma":[0.0021456473,0.0003162858,0.0005965186,0.0017999104,0.0006210876,0.0022527548,0.0009862662,0.0005790361,0.00038396637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046585916,0.00017053155,0.009470927,0.00024110105,0.00020956666,0.00058277394,0.00046227124,0.27225608,0.035366736,0.032209445,0.0029581846,0.6456066],"study_design_scores_gemma":[0.0000121678995,0.00004070401,0.0016336353,0.000008272921,0.00003405199,0.00021755489,0.00008448871,0.97782713,0.008347171,0.00939021,0.002381996,0.000022542046],"about_ca_topic_score_codex":0.0075844163,"about_ca_topic_score_gemma":0.007685542,"teacher_disagreement_score":0.0075844163,"about_ca_system_score_codex":0.00096917234,"about_ca_system_score_gemma":0.00051127543,"threshold_uncertainty_score":0.015080571},"labels":[],"label_agreement":null},{"id":"W2147995604","doi":"10.1109/tsmca.2010.2041655","title":"Adaptive Appearance Model and Condensation Algorithm for Robust Face Tracking","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"University of Toronto","keywords":"Computer science; Face (sociological concept); Context (archaeology); Tangent space; Facial motion capture; Cascade; Adaptive sampling; Artificial intelligence; Tracking (education); Affine transformation; Algorithm; Tangent; Computer vision; Sampling (signal processing); Mathematics; Face detection; Facial recognition system; Filter (signal processing); Pattern recognition (psychology); Statistics","score_opus":0.05424696606680169,"score_gpt":0.2748290150191633,"score_spread":0.2205820489523616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2147995604","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012984133,0.000051958425,0.998201,0.000016593865,0.000012240463,0.000012435059,0.000007776394,0.0002190479,0.0001806152],"genre_scores_gemma":[0.13287856,0.00025716744,0.8631425,0.000116503936,0.000087516266,0.00017904947,0.00019010268,0.00021822835,0.0029303643],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927765,0.00014481446,0.000027311118,0.00020728724,0.00029008847,0.00005289286],"domain_scores_gemma":[0.99941766,0.00019450209,0.000070953305,0.0001253646,0.00016146147,0.000030092551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010803358,0.00071251765,0.0009986522,0.0009198214,0.00037923234,0.0005568789,0.0017651594,0.00092655787,0.0018816914],"category_scores_gemma":[0.0026059626,0.0005358942,0.0010881962,0.0009982431,0.00075943855,0.001360025,0.0011549405,0.001198206,0.0011009423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020672979,0.000078148434,0.0007472534,0.00009138377,0.000115413975,0.00011900246,0.00017756062,0.41028807,0.043876175,0.023258492,0.0040261834,0.51701564],"study_design_scores_gemma":[0.000007868367,0.000023784582,0.000121122466,0.0000031152883,0.000008081291,0.00004058267,0.0000037200018,0.9926069,0.0038774884,0.00207105,0.0012271677,0.000009095038],"about_ca_topic_score_codex":0.0038986169,"about_ca_topic_score_gemma":0.0031869884,"teacher_disagreement_score":0.0038986169,"about_ca_system_score_codex":0.0008511803,"about_ca_system_score_gemma":0.0008971363,"threshold_uncertainty_score":0.007751882},"labels":[],"label_agreement":null},{"id":"W2148900105","doi":"10.1109/iccd.2008.4751870","title":"Acceleration of a 3D target tracking algorithm using an application specific instruction set processor","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Speedup; Acceleration; Instruction set; Set (abstract data type); Tracking (education); Multiprocessing; Extensibility; Algorithm; Algorithm design; Embedded system; Real-time computing; Computer hardware; Parallel computing; Operating system","score_opus":0.09469152121329405,"score_gpt":0.32500416225727374,"score_spread":0.2303126410439797,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2148900105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4444707,0.00047790815,0.52380866,0.00023734529,0.00015528493,0.00016826931,0.0002885096,0.015643304,0.014749978],"genre_scores_gemma":[0.63761204,0.0002428505,0.35275504,0.00008825517,0.000021201753,0.00012725579,0.00077317364,0.00024312519,0.008137087],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998746,0.000015254254,0.000010734367,0.000027067103,0.000046950667,0.00002534009],"domain_scores_gemma":[0.9998066,0.000060863284,0.000012875893,0.000030118275,0.000075304415,0.000014269098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000180646,0.00048293374,0.0002411659,0.00033344372,0.00020662925,0.00043060037,0.00075841486,0.00030969977,0.004770837],"category_scores_gemma":[0.0004948308,0.00017380613,0.00022785942,0.00043046087,0.00014652124,0.00037550877,0.00027612815,0.00049213006,0.0011280887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017604032,0.00039428004,0.0074343015,0.00029120836,0.000102125035,0.00067562424,0.0002628892,0.08838515,0.3299801,0.0076442147,0.013217638,0.5498521],"study_design_scores_gemma":[0.00013506426,0.0004966792,0.0030618666,0.000022756778,0.00007056916,0.00032937434,0.00004891624,0.8109064,0.17059316,0.0010258141,0.0132855065,0.000023939228],"about_ca_topic_score_codex":0.0017349619,"about_ca_topic_score_gemma":0.0017347009,"teacher_disagreement_score":0.004770837,"about_ca_system_score_codex":0.0003145921,"about_ca_system_score_gemma":0.0005590695,"threshold_uncertainty_score":0.015960038},"labels":[],"label_agreement":null},{"id":"W2149454580","doi":"10.1109/crv.2006.3","title":"A feature-based tracking algorithm for vehicles in intersections","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":226,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Feature (linguistics); Computer science; Tracking (education); Intelligent transportation system; Track (disk drive); Computer vision; Algorithm; Field (mathematics); Artificial intelligence; Extension (predicate logic); Vehicle tracking system; Engineering; Transport engineering; Mathematics; Segmentation","score_opus":0.02344892529384042,"score_gpt":0.29320227875562616,"score_spread":0.26975335346178575,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149454580","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":[0.009087094,0.00014304392,0.9883836,0.000030033343,0.000037496775,0.00006192009,0.00007375913,0.0017004098,0.00048266895],"genre_scores_gemma":[0.08878284,0.00015096604,0.9087187,0.000039880582,0.000046206595,0.00013404654,0.00036180625,0.000087413944,0.0016780844],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994702,0.000043025302,0.000026437265,0.00018230182,0.00023086817,0.000047108628],"domain_scores_gemma":[0.9993861,0.00013099099,0.00007264705,0.00007873169,0.00030508006,0.000026404881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005814975,0.00065112236,0.0008212235,0.0013607382,0.0009203307,0.00062741135,0.0015023935,0.0011217571,0.0018054036],"category_scores_gemma":[0.001559034,0.00034788513,0.00043828328,0.0016527773,0.0003851294,0.0013237599,0.00065888517,0.0008934039,0.0012725749],"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":[0.00017196292,0.00014622221,0.0023532503,0.00006561633,0.000052580348,0.000073099225,0.00008922782,0.037582085,0.024836019,0.0026751722,0.0044310717,0.92752373],"study_design_scores_gemma":[0.000071428425,0.0003310928,0.0036318568,0.000022703856,0.00008735533,0.00065533625,0.0000367649,0.9468797,0.032995824,0.0028815733,0.012338777,0.00006755166],"about_ca_topic_score_codex":0.004351756,"about_ca_topic_score_gemma":0.0043544197,"teacher_disagreement_score":0.004351756,"about_ca_system_score_codex":0.0005465249,"about_ca_system_score_gemma":0.00076582155,"threshold_uncertainty_score":0.008652866},"labels":[],"label_agreement":null},{"id":"W2149951699","doi":"","title":"Latent Maximum Margin Clustering","year":2013,"lang":"en","type":"article","venue":"Neural Information Processing Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Cluster analysis; Margin (machine learning); Computer science; Latent variable; Artificial intelligence; Probabilistic latent semantic analysis; Correlation clustering; Data mining; Pattern recognition (psychology); Machine learning","score_opus":0.02517951356659484,"score_gpt":0.25911974721805403,"score_spread":0.2339402336514592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2149951699","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.001642092,0.00018851398,0.99709785,0.00010654799,0.000020892172,0.00003453418,0.00011034907,0.000312947,0.0004863022],"genre_scores_gemma":[0.19719721,0.0006030928,0.79270524,0.00042986052,0.00025424032,0.00053675025,0.0024481844,0.0005565984,0.005268889],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99384284,0.0028011815,0.00034724246,0.0014369239,0.0012237778,0.00034811813],"domain_scores_gemma":[0.99401253,0.0023932864,0.0007076376,0.0015988295,0.0010679263,0.00021978527],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0047586877,0.0017307518,0.002461494,0.002183508,0.0013882722,0.0031385336,0.0048398715,0.002663194,0.004331397],"category_scores_gemma":[0.015617069,0.0008893023,0.002073575,0.002987542,0.002028418,0.004773577,0.00396661,0.0032589734,0.0031931296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046964572,0.0003235375,0.0036821365,0.0007499454,0.000491192,0.00024905332,0.00068801537,0.44164014,0.008173625,0.12947233,0.019215858,0.39484456],"study_design_scores_gemma":[0.000018854942,0.000049543258,0.00036610026,0.000042175976,0.0000252104,0.00007269577,0.000053579613,0.91993606,0.0023871257,0.072951466,0.0040655066,0.00003172686],"about_ca_topic_score_codex":0.0019308075,"about_ca_topic_score_gemma":0.0024574497,"teacher_disagreement_score":0.0048398715,"about_ca_system_score_codex":0.0014273457,"about_ca_system_score_gemma":0.0019642827,"threshold_uncertainty_score":0.02516663},"labels":[],"label_agreement":null},{"id":"W2152155012","doi":"10.1109/itcc.2004.1286567","title":"Content description servers for networked video surveillance","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Server; Computer science; Metadata; Video tracking; Video processing; XML; Uncompressed video; Multimedia; Real-time computing; Computer network; Computer vision; World Wide Web","score_opus":0.0887400294364003,"score_gpt":0.2857691351130771,"score_spread":0.1970291056766768,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152155012","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0064279255,0.001277574,0.96505785,0.00056586455,0.00023513145,0.00039599597,0.00036741115,0.012298303,0.013373899],"genre_scores_gemma":[0.1673524,0.0024635424,0.79564756,0.0005716415,0.00034273491,0.0010241879,0.0037185822,0.0024855116,0.02639382],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831414,0.0004511654,0.00016791507,0.00020864472,0.00073500746,0.00012312808],"domain_scores_gemma":[0.99680567,0.0007880092,0.00022811622,0.0012772965,0.0006809951,0.00021986145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015873195,0.0010549527,0.00080333505,0.0013235202,0.00127139,0.0036434985,0.002652744,0.0022889224,0.010111179],"category_scores_gemma":[0.004843704,0.00080132077,0.0005477575,0.001996404,0.0013971432,0.004786366,0.0023994881,0.0024135583,0.0070804674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048702944,0.00033550465,0.0014409276,0.00066567515,0.000064455206,0.00090167334,0.0007406304,0.02779425,0.054494075,0.5272374,0.069736615,0.3161017],"study_design_scores_gemma":[0.00016783079,0.00018042144,0.00077500707,0.0002866963,0.00009077162,0.0010865562,0.00024351379,0.37930337,0.06596319,0.13861091,0.4131663,0.00012540564],"about_ca_topic_score_codex":0.0020562822,"about_ca_topic_score_gemma":0.0012581792,"teacher_disagreement_score":0.010111179,"about_ca_system_score_codex":0.0020558615,"about_ca_system_score_gemma":0.0013913506,"threshold_uncertainty_score":0.03382528},"labels":[],"label_agreement":null},{"id":"W2152363040","doi":"10.1109/rose.2009.5355997","title":"Active people tracking by a PTZ camera in IP surveillance system","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Optical flow; Frame rate; Frame (networking); Classifier (UML); Feature extraction; Histogram; Tracking system; Image (mathematics)","score_opus":0.012774830244938208,"score_gpt":0.2689872605571156,"score_spread":0.2562124303121774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152363040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35638246,0.0005678773,0.6351994,0.00017120526,0.0000962912,0.00015624611,0.00011555826,0.0016847503,0.0056262664],"genre_scores_gemma":[0.8391482,0.0002446565,0.1575329,0.00010014422,0.00005144659,0.000066932385,0.00010368362,0.000033256925,0.0027188335],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999608,0.00006523806,0.000016104552,0.00009431075,0.00018545428,0.00003094087],"domain_scores_gemma":[0.9997508,0.00006158039,0.000039636892,0.000025764855,0.00009653739,0.000025663503],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036598757,0.00028250233,0.00044454983,0.0005933704,0.0002139102,0.00036928215,0.0004904094,0.0004751898,0.0009655497],"category_scores_gemma":[0.00072781596,0.00020606791,0.00019382706,0.00029017328,0.00020093442,0.00076095987,0.0003370559,0.00030365816,0.0003149417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015318256,0.00018258001,0.015520759,0.00024965702,0.00008034918,0.0006024965,0.00038307294,0.01119387,0.41278848,0.0011238982,0.0027966008,0.5535465],"study_design_scores_gemma":[0.0001756939,0.0012772213,0.050441995,0.000069717986,0.0002337282,0.0030171443,0.00019142896,0.6944269,0.2413661,0.0008124655,0.00790962,0.00007806146],"about_ca_topic_score_codex":0.0016232342,"about_ca_topic_score_gemma":0.0016791319,"teacher_disagreement_score":0.0016232342,"about_ca_system_score_codex":0.00027750555,"about_ca_system_score_gemma":0.00014380884,"threshold_uncertainty_score":0.0032300353},"labels":[],"label_agreement":null},{"id":"W2152404412","doi":"10.1109/cvprw.2014.96","title":"Fast LBP Face Detection on Low-Power SIMD Architectures","year":2014,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"SIMD; Video Graphics Array; Computer science; Face detection; Reuse; Face (sociological concept); Parallelism (grammar); Exploit; Artificial intelligence; Feature extraction; Parallel computing; Facial recognition system; Embedded system; Field-programmable gate array","score_opus":0.009808355271883817,"score_gpt":0.2576159805947832,"score_spread":0.24780762532289938,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2152404412","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12186946,0.00059896696,0.85101485,0.0002837836,0.00016641397,0.00017354374,0.00029653686,0.011576377,0.014020085],"genre_scores_gemma":[0.5659867,0.00025410173,0.42109665,0.00028808345,0.000067522495,0.00018764102,0.0004557449,0.00040652248,0.011256985],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999602,0.000040228417,0.000015314616,0.00008538387,0.00020134074,0.000055594213],"domain_scores_gemma":[0.99957925,0.00011600042,0.00003543881,0.00009121578,0.00015118446,0.000026917625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030842892,0.0005812875,0.0003750599,0.00068374997,0.00031171791,0.0006143683,0.0015669486,0.00030184814,0.012862863],"category_scores_gemma":[0.0009976308,0.00033791838,0.00020774441,0.0006163932,0.00028201847,0.00090431573,0.0006269057,0.00046338895,0.0034429973],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014042124,0.00020497949,0.0028285505,0.0003422382,0.0000633028,0.00031954577,0.00021005045,0.013144726,0.36158645,0.008505365,0.014742722,0.59664786],"study_design_scores_gemma":[0.00020762159,0.00068283005,0.0033073789,0.000078847115,0.00006664124,0.0006217885,0.00010196824,0.64835,0.3084387,0.0052191163,0.03284952,0.0000756024],"about_ca_topic_score_codex":0.0013846607,"about_ca_topic_score_gemma":0.002503531,"teacher_disagreement_score":0.012862863,"about_ca_system_score_codex":0.0006174987,"about_ca_system_score_gemma":0.0005601542,"threshold_uncertainty_score":0.04303056},"labels":[],"label_agreement":null},{"id":"W2153757740","doi":"10.1007/11585978_14","title":"Linear Programming Matching and Appearance-Adaptive Object Tracking","year":2005,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Matching (statistics); Video tracking; Linear programming; Artificial intelligence; Tracking (education); Relaxation (psychology); Scheme (mathematics); Template; Set (abstract data type); Graph; Algorithm; Template matching; Pattern recognition (psychology); Object (grammar); Computer vision; Theoretical computer science; Mathematics; Image (mathematics)","score_opus":0.02891550542285065,"score_gpt":0.28608242247904864,"score_spread":0.257166917056198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153757740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0009856249,0.0003247945,0.99730325,0.000059615213,0.000027037046,0.000011101379,0.00001595726,0.00023106489,0.001041581],"genre_scores_gemma":[0.11616049,0.001483621,0.85379434,0.00023326403,0.00018196409,0.00019461874,0.0002833401,0.00034993942,0.027318377],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990977,0.00020743262,0.00003903421,0.0002927591,0.00028868436,0.00007436978],"domain_scores_gemma":[0.99925953,0.0003654138,0.00006825302,0.00010913035,0.00017315468,0.000024455043],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008937462,0.0006488424,0.0012963761,0.0010139782,0.0004364028,0.0017163171,0.0021022162,0.0014304557,0.0054280194],"category_scores_gemma":[0.0035304741,0.0009910011,0.00077947456,0.0025482422,0.0008501829,0.0021222066,0.0017043639,0.0015281251,0.002941161],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013304575,0.00013508642,0.00035406128,0.0002353115,0.00008746332,0.00009374053,0.00007581962,0.18025777,0.012533227,0.059609734,0.0067414436,0.73974335],"study_design_scores_gemma":[0.00000987742,0.000028163036,0.00019574878,0.000009935531,0.000015403428,0.000087258624,0.00001442386,0.95714575,0.0050043534,0.034198888,0.00327523,0.000015097434],"about_ca_topic_score_codex":0.0034679275,"about_ca_topic_score_gemma":0.003054234,"teacher_disagreement_score":0.0054280194,"about_ca_system_score_codex":0.0008065638,"about_ca_system_score_gemma":0.00077408686,"threshold_uncertainty_score":0.018158495},"labels":[],"label_agreement":null},{"id":"W2153775233","doi":"10.1109/icdsc.2009.5289362","title":"Unsupervised camera network structure estimation based on activity","year":2009,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"National Science Foundation","keywords":"Computer science; Artificial intelligence; Zoom; Homography; Computer vision; Matching (statistics); Calibration; Scalability; Unsupervised learning; Similarity (geometry); Segmentation; Distance matrix; Fundamental matrix (linear differential equation); Pattern recognition (psychology); Image (mathematics); Mathematics; Algorithm","score_opus":0.01578045336116086,"score_gpt":0.28139220531214143,"score_spread":0.2656117519509806,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2153775233","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02767381,0.0000973916,0.9712385,0.0000714216,0.000008079428,0.000024210896,0.00004957838,0.00019416756,0.00064296945],"genre_scores_gemma":[0.7591954,0.00038991065,0.23723441,0.00005959986,0.00006309106,0.00010242584,0.00057187094,0.00010310226,0.0022802404],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993654,0.00017537615,0.000024439145,0.00020678103,0.00017073244,0.000057293288],"domain_scores_gemma":[0.9983942,0.0007361215,0.00034438138,0.00024117109,0.00022976063,0.00005437676],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055617554,0.000615405,0.00080237945,0.0012733665,0.00032944247,0.00083385897,0.0012124047,0.0006762887,0.00076287886],"category_scores_gemma":[0.0052415836,0.0005563866,0.00046131312,0.0010838934,0.0007245382,0.0022121968,0.0008315852,0.0008119866,0.0003186153],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000109335386,0.000053162465,0.0040654014,0.00007360184,0.00006462087,0.000080870974,0.00012515142,0.85426563,0.006303914,0.012470036,0.0008278202,0.12156047],"study_design_scores_gemma":[0.000003855659,0.000009201244,0.0007022368,0.0000046244263,0.0000037075235,0.000034382425,0.000013677714,0.9928357,0.0013955133,0.0046885484,0.00030287151,0.0000056853546],"about_ca_topic_score_codex":0.0033371812,"about_ca_topic_score_gemma":0.004302165,"teacher_disagreement_score":0.0033371812,"about_ca_system_score_codex":0.0007976434,"about_ca_system_score_gemma":0.00053652766,"threshold_uncertainty_score":0.006635487},"labels":[],"label_agreement":null},{"id":"W2154298189","doi":"10.1109/ccece.2008.4564716","title":"Markerless human tracking for industrial environments","year":2008,"lang":"en","type":"article","venue":"Conference proceedings - Canadian Conference on Electrical and Computer Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Pixel; Tracking (education); Background subtraction; Robot","score_opus":0.06542515969268059,"score_gpt":0.24177703659860494,"score_spread":0.17635187690592435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2154298189","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006204914,0.00022746273,0.9916419,0.00002293772,0.000031037427,0.00001459425,0.000022476845,0.0011571822,0.00067743735],"genre_scores_gemma":[0.19308876,0.00046482554,0.80292845,0.00006317969,0.000037647853,0.000064128064,0.00018077572,0.0001671628,0.0030050015],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993799,0.00012759412,0.000018879922,0.00017116204,0.0002508375,0.00005179598],"domain_scores_gemma":[0.999311,0.00014953496,0.000109696,0.00020016973,0.00017419466,0.000055475663],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044061238,0.00057915336,0.00065645005,0.0007994868,0.00045991398,0.00071179314,0.0011825524,0.0008702654,0.0020503874],"category_scores_gemma":[0.0013589782,0.00049178774,0.00039644554,0.0006833653,0.0003468997,0.00078422413,0.0010832626,0.0006310648,0.0013096113],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002710109,0.00007809633,0.0015244648,0.0001736797,0.00004687163,0.00025209467,0.00025955684,0.047714222,0.13357502,0.0050089303,0.0051775407,0.8059184],"study_design_scores_gemma":[0.000045422432,0.000280011,0.0042490354,0.000051001236,0.00003509957,0.001272951,0.00006325881,0.8825017,0.08470773,0.005207991,0.021486616,0.00009911535],"about_ca_topic_score_codex":0.0017797669,"about_ca_topic_score_gemma":0.002010745,"teacher_disagreement_score":0.0020503874,"about_ca_system_score_codex":0.00030469173,"about_ca_system_score_gemma":0.0005589843,"threshold_uncertainty_score":0.006859243},"labels":[],"label_agreement":null},{"id":"W2155067688","doi":"10.1109/crv.2006.66","title":"Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":128,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Action recognition; Artificial intelligence; Computer science; Computer vision; Tracking (education); Pattern recognition (psychology); Action (physics); Principal component analysis; Physics","score_opus":0.09271310548961603,"score_gpt":0.3160339430992911,"score_spread":0.22332083760967508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155067688","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012180612,0.00021101076,0.9830898,0.000048661175,0.00007918988,0.00005481683,0.00010568518,0.0021744017,0.002055884],"genre_scores_gemma":[0.2099363,0.00046465165,0.7814616,0.000132738,0.000084362866,0.000121311066,0.0008145486,0.00018298393,0.006801515],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992982,0.0000446509,0.000029046307,0.00022136423,0.00032173705,0.00008487534],"domain_scores_gemma":[0.999634,0.000060840415,0.000037304675,0.000077760735,0.00014913418,0.000041038154],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054168183,0.0005565815,0.0010136714,0.0017253335,0.00035436288,0.00094483956,0.001086555,0.00069461024,0.0014117822],"category_scores_gemma":[0.0008436048,0.00045611066,0.00056067284,0.0015647396,0.00035575777,0.0011556508,0.0010143622,0.0006020158,0.0016984498],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013745051,0.00017324147,0.002100308,0.000082893464,0.00007445155,0.00009902313,0.000059298316,0.012179177,0.103540264,0.0034973219,0.0041727372,0.8738838],"study_design_scores_gemma":[0.000056929446,0.00028988183,0.011226764,0.000031267762,0.000111107445,0.0010283091,0.00008327882,0.8266875,0.13073201,0.0072979685,0.0223329,0.00012220023],"about_ca_topic_score_codex":0.0038629912,"about_ca_topic_score_gemma":0.004973887,"teacher_disagreement_score":0.0038629912,"about_ca_system_score_codex":0.00032368448,"about_ca_system_score_gemma":0.0008767445,"threshold_uncertainty_score":0.007681012},"labels":[],"label_agreement":null},{"id":"W2155213743","doi":"10.1109/cvpr.2005.233","title":"Moving Cast Shadow Detection from a Gaussian Mixture Shadow Model","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":109,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Shadow (psychology); Gaussian; Shadow mapping; Computer vision; Computer science; Mixture model; Artificial intelligence; Gaussian process; Computer graphics (images); Physics; Psychology","score_opus":0.020166932909184213,"score_gpt":0.2666959264177416,"score_spread":0.2465289935085574,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155213743","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022436554,0.0001278182,0.9761062,0.00004324778,0.000020823802,0.000016842638,0.000032528627,0.000629052,0.000586845],"genre_scores_gemma":[0.5775859,0.00043994188,0.41907558,0.00008487783,0.0000575475,0.000046304216,0.0003539645,0.00019388618,0.002162011],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997011,0.00005705769,0.0000088281995,0.00005744863,0.00013839397,0.00003717658],"domain_scores_gemma":[0.99958783,0.00015444857,0.00003569749,0.00006948045,0.00012576845,0.00002680954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006009815,0.00052100123,0.0006262774,0.0010682798,0.0002346514,0.00064779265,0.00077006494,0.0005693628,0.00076981983],"category_scores_gemma":[0.0018548366,0.00043915986,0.00068552373,0.0006992922,0.00046265763,0.000823932,0.0006620712,0.000858838,0.000511319],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037129244,0.00009426405,0.0040022237,0.00015447078,0.00013860372,0.0002684854,0.00020145808,0.4417437,0.06742309,0.011850244,0.0028310518,0.47092116],"study_design_scores_gemma":[0.000004013581,0.000015690908,0.000708293,0.000004159656,0.000008357406,0.000057291167,0.000010812152,0.99221367,0.0044486606,0.0019656532,0.0005525123,0.000010899967],"about_ca_topic_score_codex":0.005370125,"about_ca_topic_score_gemma":0.0061882376,"teacher_disagreement_score":0.005370125,"about_ca_system_score_codex":0.0005626037,"about_ca_system_score_gemma":0.00059759105,"threshold_uncertainty_score":0.010677755},"labels":[],"label_agreement":null},{"id":"W2155969220","doi":"10.1109/ratfg.1999.799230","title":"Detection and tracking of faces in real environments","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Johns Hopkins University","keywords":"Computer vision; Computer science; Artificial intelligence; Frame rate; Interface (matter); Frame (networking); Face (sociological concept); Tracking (education); Active vision","score_opus":0.029736195630899055,"score_gpt":0.2813240414228992,"score_spread":0.2515878457920001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155969220","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22447032,0.00036168963,0.7642253,0.00014495863,0.00008375708,0.000097272845,0.00023035843,0.0026454362,0.007740791],"genre_scores_gemma":[0.6832117,0.00033076302,0.3095141,0.00014455094,0.000042047628,0.000107482214,0.00029106397,0.00011889688,0.006239347],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996648,0.000047285208,0.000008882992,0.000100611855,0.00012748597,0.000050822644],"domain_scores_gemma":[0.99971503,0.00007989355,0.00004335241,0.00003812292,0.00009285942,0.00003069333],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030854478,0.00030158894,0.000337357,0.00066104915,0.00024890742,0.000531085,0.0004781366,0.00061482604,0.0016901394],"category_scores_gemma":[0.0008710482,0.00023936304,0.00018478914,0.00021186275,0.0002430328,0.00050178927,0.0005203509,0.0002458507,0.00079580426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042020102,0.00013735124,0.0059691602,0.00012040545,0.00003956153,0.0003849599,0.0004353089,0.0067038964,0.47449866,0.0026195664,0.002976103,0.5056949],"study_design_scores_gemma":[0.000055117594,0.00086249225,0.06773231,0.00005888707,0.000085076754,0.004469271,0.0005021116,0.47789556,0.42358524,0.0048502814,0.019789524,0.00011400412],"about_ca_topic_score_codex":0.0015018822,"about_ca_topic_score_gemma":0.0018547623,"teacher_disagreement_score":0.0016901394,"about_ca_system_score_codex":0.00019200494,"about_ca_system_score_gemma":0.00021312578,"threshold_uncertainty_score":0.0056540966},"labels":[],"label_agreement":null},{"id":"W2155994391","doi":"10.1109/tcsvt.2005.857311","title":"Voting-based simultaneous tracking of multiple video objects","year":2005,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Video tracking; Segmentation; Object (grammar); Coding (social sciences); Feature extraction; Object detection; Feature (linguistics); Pattern recognition (psychology); Mathematics","score_opus":0.027739224305368994,"score_gpt":0.2748423860241719,"score_spread":0.2471031617188029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2155994391","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":[0.024420714,0.00016620065,0.97390276,0.000032100343,0.000050110124,0.000045475874,0.000022809141,0.0004885288,0.00087136874],"genre_scores_gemma":[0.4685055,0.00021131976,0.5269608,0.00006827961,0.00007517852,0.00010977244,0.00020914373,0.00011775133,0.00374231],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985221,0.00021334145,0.00007980887,0.00042537323,0.00063317415,0.0001263659],"domain_scores_gemma":[0.998659,0.0004612835,0.00015805756,0.00025370315,0.00040211575,0.000065696324],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013969064,0.0005781262,0.0011674962,0.0010440496,0.00044273117,0.0008318068,0.0017799863,0.0006902602,0.00087584154],"category_scores_gemma":[0.0032045587,0.0004946341,0.0005684009,0.0009189567,0.0004432379,0.0014035037,0.0008621086,0.000545671,0.00044948593],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044970142,0.000098365126,0.002826416,0.00011254506,0.000102102174,0.00013726723,0.00021178146,0.041747384,0.1319429,0.004906987,0.001044959,0.8164196],"study_design_scores_gemma":[0.00005180075,0.00026353347,0.0036820376,0.000018150597,0.00008531952,0.00031296464,0.000045930316,0.9182181,0.068610795,0.0029557024,0.0057035526,0.00005217896],"about_ca_topic_score_codex":0.002679391,"about_ca_topic_score_gemma":0.0032728983,"teacher_disagreement_score":0.002679391,"about_ca_system_score_codex":0.0005160017,"about_ca_system_score_gemma":0.00066234637,"threshold_uncertainty_score":0.007387638},"labels":[],"label_agreement":null},{"id":"W2156178969","doi":"10.1109/icar.2005.1507512","title":"An overview of a probabilistic tracker for multiple cooperative tracking agents","year":2006,"lang":"en","type":"article","venue":"ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005.","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Probabilistic logic; Computer vision; Computer science; Robustness (evolution); Artificial intelligence; Tracking (education); Zoom; Tracking system; Video tracking; Object (grammar); Engineering; Kalman filter","score_opus":0.13022106459838112,"score_gpt":0.38354646068607273,"score_spread":0.2533253960876916,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156178969","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":[0.00011362581,0.000578048,0.9981286,0.000033323417,0.000038559305,0.000017735782,0.00001613713,0.00028966845,0.00078422],"genre_scores_gemma":[0.03328694,0.004493885,0.954257,0.0001460657,0.00037059034,0.00035576097,0.00034927155,0.00027400747,0.006466512],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986848,0.00025746776,0.000106676154,0.00032522896,0.0005613882,0.000064522734],"domain_scores_gemma":[0.9993267,0.00024837666,0.00006984878,0.00011541669,0.00020059408,0.00003915203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012701174,0.0010775514,0.0010806909,0.0010478128,0.0006820884,0.0014055764,0.0023440365,0.0024839502,0.0046718516],"category_scores_gemma":[0.0023537423,0.0010455592,0.0013584249,0.0018796641,0.00058401725,0.0024934974,0.0017649756,0.002284109,0.0045023467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012779812,0.0001097714,0.00077289273,0.000934992,0.00016425762,0.0005292647,0.00027270697,0.27697188,0.0201672,0.13736428,0.013912295,0.5486726],"study_design_scores_gemma":[0.000029806972,0.00016236996,0.00037487806,0.000103202125,0.00006943083,0.0008084546,0.000027530188,0.8423283,0.004337884,0.049761202,0.101918295,0.000078690224],"about_ca_topic_score_codex":0.0025678277,"about_ca_topic_score_gemma":0.0013152901,"teacher_disagreement_score":0.0046718516,"about_ca_system_score_codex":0.00081344874,"about_ca_system_score_gemma":0.0010703141,"threshold_uncertainty_score":0.015628874},"labels":[],"label_agreement":null},{"id":"W2156426548","doi":"10.1109/ccece.2003.1226149","title":"Vision-based detection of activity for traffic control","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Robustness (evolution); Computer vision; Artificial intelligence; Computer science; Segmentation; Intersection (aeronautics); Image segmentation; Image processing; Feature extraction; Context (archaeology); Object detection; Machine vision; Image (mathematics); Engineering; Geography","score_opus":0.015789791467557775,"score_gpt":0.2969831603734885,"score_spread":0.2811933689059307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156426548","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.072360724,0.006319128,0.89345884,0.00060773327,0.00043535,0.00020664487,0.00085880933,0.006624641,0.019128175],"genre_scores_gemma":[0.79116094,0.0025923122,0.19890109,0.0003229406,0.00021157424,0.00012897835,0.0010035184,0.00013228919,0.0055463593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979156,0.000032529002,0.0000069291536,0.000058535512,0.00008656567,0.000023931003],"domain_scores_gemma":[0.99977905,0.000049728966,0.000027301588,0.00002559203,0.00009645555,0.000021991464],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023117206,0.0004013444,0.00052379095,0.0010682344,0.00025282183,0.0006468666,0.0004887806,0.00051868276,0.002742719],"category_scores_gemma":[0.000825687,0.00015678549,0.00025727588,0.0007447288,0.00024999218,0.00044732034,0.00023775427,0.00042608537,0.0013757049],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033258792,0.00026422922,0.0029329613,0.00026141896,0.000058008423,0.000088838286,0.0000552699,0.0145874275,0.16421883,0.0046371575,0.008868402,0.8036948],"study_design_scores_gemma":[0.00011704654,0.00094408053,0.02628042,0.00012959294,0.00020303835,0.00077457976,0.00012683877,0.7197385,0.18547332,0.0142051065,0.051878527,0.00012902333],"about_ca_topic_score_codex":0.0025164832,"about_ca_topic_score_gemma":0.0031158214,"teacher_disagreement_score":0.002742719,"about_ca_system_score_codex":0.00041475595,"about_ca_system_score_gemma":0.00045403384,"threshold_uncertainty_score":0.009175301},"labels":[],"label_agreement":null},{"id":"W2156994206","doi":"10.1109/itsc.2002.1041181","title":"Mobile vision-based vehicle tracking and traffic control","year":2003,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University; University of Toronto","funders":"","keywords":"Active vision; Computer science; Focus (optics); Machine vision; Zoom; Mobile robot; Task (project management); Orientation (vector space); Artificial intelligence; Smart camera; Computer vision; Human–computer interaction; Engineering; Robot; Systems engineering","score_opus":0.0135847004816847,"score_gpt":0.281308686738587,"score_spread":0.2677239862569023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2156994206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03377143,0.009892428,0.9378411,0.00059411,0.00050377054,0.00007164267,0.00009971799,0.0011809161,0.01604495],"genre_scores_gemma":[0.8531942,0.0057037375,0.12385942,0.0003228487,0.00047606858,0.00009010278,0.00019859066,0.00007180154,0.016083183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997943,0.000035069712,0.0000058416135,0.000055253906,0.000079333164,0.00003017063],"domain_scores_gemma":[0.99980897,0.000060260223,0.00003228424,0.000015221662,0.00006863641,0.000014572965],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028513192,0.00042734455,0.00043850765,0.0006877312,0.00025986662,0.0009370816,0.00054111343,0.00087372935,0.0015987114],"category_scores_gemma":[0.000641991,0.00015403755,0.0002657426,0.0006083188,0.0004050896,0.00080886483,0.00032276282,0.0004101461,0.00052110426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003127464,0.00015099232,0.0015408106,0.00032155585,0.00006986359,0.00025364538,0.00014038922,0.19262893,0.05293768,0.057068814,0.006342701,0.6882319],"study_design_scores_gemma":[0.000035985868,0.00026622813,0.0018455838,0.000052620475,0.000038209775,0.00023904261,0.000058996306,0.9277653,0.016571086,0.018216465,0.034870736,0.000039691357],"about_ca_topic_score_codex":0.0029741558,"about_ca_topic_score_gemma":0.0022017278,"teacher_disagreement_score":0.0029741558,"about_ca_system_score_codex":0.00049290725,"about_ca_system_score_gemma":0.000353185,"threshold_uncertainty_score":0.005913675},"labels":[],"label_agreement":null},{"id":"W2157499108","doi":"10.3141/2365-12","title":"Flexible, Mobile Video Camera System and Open Source Video Analysis Software for Road Safety and Behavioral Analysis","year":2013,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal; McGill University","funders":"","keywords":"Computer science; Video processing; Video camera; Open source; Software; Video tracking; Scalability; Data collection; Real-time computing; Computer vision; Database","score_opus":0.08310695199950566,"score_gpt":0.41350856978738576,"score_spread":0.33040161778788013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2157499108","genre_codex":"methods","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015325056,0.00028578934,0.89353496,0.00016748921,0.00019183733,0.001870318,0.004356265,0.07555633,0.008711966],"genre_scores_gemma":[0.16073054,0.0005023375,0.7905024,0.00053430983,0.00022231542,0.005293213,0.012652923,0.0067471247,0.022814661],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99880433,0.000125687,0.00008941123,0.00043433704,0.0004490122,0.00009721579],"domain_scores_gemma":[0.99798894,0.0004838943,0.00017949771,0.0003479309,0.000815614,0.0001840206],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012890324,0.0012349326,0.00091242074,0.002502862,0.00045605673,0.0010333325,0.0018252052,0.0007877208,0.021194702],"category_scores_gemma":[0.0035997494,0.0005008696,0.00077742484,0.0012621832,0.00043528996,0.0013172771,0.001398067,0.00096656044,0.0075580245],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013490906,0.0006853302,0.0076680398,0.0010488032,0.00029075966,0.0006611417,0.00077158166,0.005983217,0.12281771,0.00904786,0.0796226,0.77005386],"study_design_scores_gemma":[0.00076925574,0.0018747591,0.052259065,0.0005613137,0.00048451574,0.003224442,0.0007063276,0.3340737,0.2737549,0.014389872,0.3171943,0.00070744735],"about_ca_topic_score_codex":0.0037893527,"about_ca_topic_score_gemma":0.0044050748,"teacher_disagreement_score":0.021194702,"about_ca_system_score_codex":0.000792848,"about_ca_system_score_gemma":0.0011720413,"threshold_uncertainty_score":0.0709033},"labels":[],"label_agreement":null},{"id":"W2158008709","doi":"10.1109/icpr.2006.224","title":"Adaptive Step Size Window Matching for Detection","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Matching (statistics); Object detection; Window (computing); Set (abstract data type); Algorithm; Computational complexity theory; Point (geometry); Selection (genetic algorithm); Artificial intelligence; Pattern recognition (psychology); Mathematics","score_opus":0.017669748758683556,"score_gpt":0.26532148402178113,"score_spread":0.24765173526309758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158008709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076896595,0.00026856983,0.99043894,0.000052983934,0.00003409793,0.000036361434,0.000018098777,0.00075464137,0.0007066034],"genre_scores_gemma":[0.20654629,0.00037000183,0.79100686,0.000101682206,0.00006168044,0.000089032525,0.000107644126,0.00018245427,0.0015343713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985587,0.00032203997,0.00007244624,0.0002960271,0.0006443822,0.00010636921],"domain_scores_gemma":[0.9976636,0.0012068712,0.00013822655,0.0004897739,0.00041719733,0.000084384425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015197782,0.00049354805,0.0006845059,0.0014025704,0.0004825647,0.00090575713,0.0013881279,0.00087011995,0.0025969183],"category_scores_gemma":[0.007779134,0.00041436037,0.00042608884,0.0013617221,0.00051701785,0.0019873779,0.0009409461,0.00091673335,0.0012267336],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054411596,0.00014214718,0.0015074487,0.00017224728,0.000079978534,0.0001699693,0.00010622468,0.052022833,0.14036229,0.028875105,0.0048382888,0.77117926],"study_design_scores_gemma":[0.000045853092,0.0002034122,0.0015279177,0.00002456278,0.000040895415,0.0005321064,0.000030416291,0.8720705,0.100335754,0.017152121,0.007993876,0.000042583837],"about_ca_topic_score_codex":0.0009297981,"about_ca_topic_score_gemma":0.0011862478,"teacher_disagreement_score":0.0025969183,"about_ca_system_score_codex":0.00050808047,"about_ca_system_score_gemma":0.0008041729,"threshold_uncertainty_score":0.008687556},"labels":[],"label_agreement":null},{"id":"W2158110926","doi":"10.1139/l10-064","title":"Development of a vehicle image-tracking system based on a long-distance detection algorithm","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer vision; Computer science; Vehicle tracking system; Image processing; Artificial intelligence; Process (computing); Tracking (education); Zoom; Acceleration; Track (disk drive); Image (mathematics); Kalman filter; Engineering","score_opus":0.008466942301378465,"score_gpt":0.21335332025800122,"score_spread":0.20488637795662276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158110926","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":[0.010240608,0.00008200286,0.98435646,0.00006289254,0.00004856336,0.00012959816,0.00005449471,0.0034485417,0.0015769177],"genre_scores_gemma":[0.090869285,0.000116871546,0.9034822,0.000079689744,0.00003265592,0.0001716379,0.00023010725,0.000095709256,0.004921921],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995503,0.000036540532,0.00003127514,0.00017330618,0.00016957367,0.000039058596],"domain_scores_gemma":[0.9992536,0.00011731401,0.0000475798,0.00007088659,0.00044991128,0.000060790124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005772229,0.00041936958,0.00070131104,0.00090677047,0.0004833945,0.0008120837,0.0013778048,0.000985542,0.0033443784],"category_scores_gemma":[0.0008805488,0.00039727936,0.00030727545,0.0006331194,0.0002768676,0.0010725887,0.00041618335,0.0008904333,0.002047293],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031833843,0.00030397598,0.003098394,0.00015528017,0.00007876904,0.00030929426,0.00018080555,0.027484106,0.23725584,0.008828759,0.005223656,0.71676284],"study_design_scores_gemma":[0.000088004985,0.00040327618,0.003522834,0.00003349032,0.0000835836,0.0006809233,0.000035915135,0.8286343,0.1430838,0.0009781251,0.02237456,0.00008114847],"about_ca_topic_score_codex":0.004944909,"about_ca_topic_score_gemma":0.0037784027,"teacher_disagreement_score":0.004944909,"about_ca_system_score_codex":0.0006076556,"about_ca_system_score_gemma":0.0012064432,"threshold_uncertainty_score":0.01118809},"labels":[],"label_agreement":null},{"id":"W2158403369","doi":"10.1109/icpr.2008.4760998","title":"Review and Evaluation of Commonly-Implemented Background Subtraction Algorithms","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":309,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Computer vision; Jitter; Motion detection; Noise (video); Probabilistic logic; Process (computing); Frame (networking); Algorithm; Subtraction; Motion estimation; Motion (physics); Pixel; Mathematics; Image (mathematics)","score_opus":0.2051079295448833,"score_gpt":0.41408043215860024,"score_spread":0.20897250261371694,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2158403369","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043856863,0.36645555,0.5685342,0.0006529932,0.0012848374,0.00051927177,0.001206748,0.0058129174,0.011676692],"genre_scores_gemma":[0.1632732,0.2493443,0.5694384,0.0006576356,0.0008921996,0.00041347707,0.0057020015,0.0013414007,0.00893741],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99488163,0.0009342486,0.0006752193,0.00084913144,0.0024746142,0.00018522827],"domain_scores_gemma":[0.98663867,0.0053995317,0.00059415493,0.0006407771,0.0064895176,0.00023730221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004759548,0.0020629908,0.002097557,0.005389404,0.0009179509,0.0022238472,0.003939132,0.0014969007,0.0020803816],"category_scores_gemma":[0.0135736,0.0008579125,0.0010584677,0.0069881007,0.0004908244,0.0023292203,0.0008597686,0.0006960412,0.001763296],"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":[0.000361153,0.0001039724,0.0016576314,0.004590589,0.00032928752,0.00008041723,0.000095407704,0.00809678,0.009525629,0.0013838523,0.007274476,0.9665009],"study_design_scores_gemma":[0.00029609882,0.0021159744,0.022937916,0.006165829,0.0030787147,0.0043916157,0.0007701857,0.23049532,0.26542157,0.0062735407,0.45726946,0.00078389345],"about_ca_topic_score_codex":0.005162701,"about_ca_topic_score_gemma":0.0043253573,"teacher_disagreement_score":0.005389404,"about_ca_system_score_codex":0.0010588544,"about_ca_system_score_gemma":0.0016997892,"threshold_uncertainty_score":0.02517122},"labels":[],"label_agreement":null},{"id":"W2161379091","doi":"10.1109/crv.2012.64","title":"3D Town: The Automatic Urban Awareness Project","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Context (archaeology); Key (lock); Animation; Computer vision; 3D city models; Artificial intelligence; Real-time computing; Computer graphics (images); Visualization; Geography","score_opus":0.05767770730193764,"score_gpt":0.3376661842952506,"score_spread":0.2799884769933129,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161379091","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09756209,0.0011361293,0.65297234,0.0014602428,0.00095525524,0.0013821366,0.015780633,0.13030176,0.09844946],"genre_scores_gemma":[0.298616,0.00084683136,0.60222656,0.00040033858,0.0001581522,0.0014495315,0.038253773,0.007691429,0.050357293],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901485,0.00026702278,0.00002288363,0.00027959867,0.0003092176,0.000106435466],"domain_scores_gemma":[0.99943024,0.000107469605,0.000020489875,0.00019151166,0.00010364388,0.00014659936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001622539,0.0010286699,0.00061936973,0.0008712549,0.00074310636,0.0019530552,0.0012770224,0.0009338079,0.013741829],"category_scores_gemma":[0.0012180123,0.0006702784,0.00070271397,0.0008938454,0.0006778364,0.0019051932,0.002890554,0.001226129,0.005019909],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019469403,0.0010374184,0.008050431,0.00041212243,0.00016661198,0.00091926043,0.0014876086,0.045446713,0.05186763,0.0495281,0.23675786,0.6023793],"study_design_scores_gemma":[0.000668638,0.0006868562,0.006557257,0.00009023411,0.00009135002,0.0006930509,0.0006057382,0.2421142,0.037016615,0.016920324,0.69440126,0.00015448805],"about_ca_topic_score_codex":0.0039766245,"about_ca_topic_score_gemma":0.004768105,"teacher_disagreement_score":0.013741829,"about_ca_system_score_codex":0.0005532588,"about_ca_system_score_gemma":0.001234408,"threshold_uncertainty_score":0.045970976},"labels":[],"label_agreement":null},{"id":"W2161468953","doi":"10.1145/1099396.1099420","title":"Surveillance camera scheduling","year":2005,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer vision; Zoom; Artificial intelligence; Pedestrian; Smart camera; Field of view; Real-time computing; Computer graphics (images)","score_opus":0.022302112507509082,"score_gpt":0.2919215976128424,"score_spread":0.26961948510533335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2161468953","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09215425,0.00049866,0.8918827,0.00020289989,0.00017818497,0.00038249636,0.00033689436,0.0028946297,0.0114691695],"genre_scores_gemma":[0.7956658,0.00028345195,0.19723684,0.00008301284,0.00007526719,0.00020444414,0.0006533202,0.00018581683,0.0056120786],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99939966,0.000110213805,0.000034917066,0.00021494093,0.00014298326,0.00009731493],"domain_scores_gemma":[0.9992467,0.00013200889,0.00010240195,0.00016365018,0.0002236852,0.00013149147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036625107,0.0008297185,0.0006203283,0.00038674285,0.000563522,0.000982525,0.0011469022,0.00043799798,0.004708272],"category_scores_gemma":[0.0013529622,0.00029394595,0.00034257403,0.0003363738,0.00022480442,0.0010112243,0.0008492687,0.0005456715,0.0009277588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019091795,0.00047702182,0.007789871,0.00036283297,0.00013895915,0.0005580334,0.00032486103,0.4530552,0.11453428,0.021427363,0.015077473,0.3843449],"study_design_scores_gemma":[0.00007476965,0.00027719612,0.0017473215,0.000012809059,0.000029866227,0.0002495399,0.00009248285,0.9446337,0.03656125,0.0034049044,0.012877129,0.0000390339],"about_ca_topic_score_codex":0.0036213722,"about_ca_topic_score_gemma":0.0038157934,"teacher_disagreement_score":0.004708272,"about_ca_system_score_codex":0.00063507986,"about_ca_system_score_gemma":0.0009498341,"threshold_uncertainty_score":0.015750706},"labels":[],"label_agreement":null},{"id":"W2162175217","doi":"10.1109/cjece.2008.4721631","title":"Cooperative hybrid multi-camera tracking for people surveillance","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; Tracking (education); Computer science; Zoom; Tracking system; Event (particle physics); Camera auto-calibration; Particle filter; Field of view; Eye tracking; Camera resectioning; Kalman filter; Engineering","score_opus":0.020894873868969852,"score_gpt":0.22878847392246318,"score_spread":0.20789360005349333,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2162175217","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0356435,0.00085625314,0.96147007,0.000053976688,0.00006375847,0.000035828823,0.000022603788,0.0006311168,0.0012229864],"genre_scores_gemma":[0.7713926,0.00059919356,0.22514711,0.00009136066,0.00006880734,0.0000880988,0.00009884323,0.000038703318,0.0024753523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993136,0.00016211132,0.000023886645,0.00019557583,0.00023796325,0.000066713525],"domain_scores_gemma":[0.99938965,0.00023357541,0.00009045485,0.00009407987,0.00014608778,0.000046185105],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006962725,0.0005819452,0.00076756737,0.0007328678,0.0003514413,0.0005836947,0.00086672395,0.0007811937,0.0006869533],"category_scores_gemma":[0.0010686768,0.00033472697,0.00058826216,0.0005479447,0.00024282343,0.00085633906,0.00078243046,0.00044287616,0.0003116903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082657224,0.00024009474,0.0064465515,0.00041815406,0.00031009648,0.0008603664,0.00064126705,0.13848816,0.1557828,0.0049789753,0.0027931076,0.6882138],"study_design_scores_gemma":[0.00004958753,0.00034110277,0.004226557,0.00002475196,0.00009003228,0.0005514983,0.00008668399,0.9599118,0.028037017,0.0021560907,0.0044840747,0.000040863568],"about_ca_topic_score_codex":0.002424317,"about_ca_topic_score_gemma":0.002413386,"teacher_disagreement_score":0.002424317,"about_ca_system_score_codex":0.00032974317,"about_ca_system_score_gemma":0.00030725525,"threshold_uncertainty_score":0.0048204064},"labels":[],"label_agreement":null},{"id":"W2163141266","doi":"10.1109/icassp.2004.1326550","title":"A novel motion estimation method for mesh-based video motion tracking","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University Grants Committee; University of Calgary","keywords":"Motion estimation; Quarter-pixel motion; Computer science; Computer vision; Block-matching algorithm; Artificial intelligence; Peak signal-to-noise ratio; Matching (statistics); Computation; Tracking (education); Reduction (mathematics); Motion (physics); Computational complexity theory; Video tracking; Algorithm; Image (mathematics); Mathematics; Video processing; Statistics","score_opus":0.0527722408113121,"score_gpt":0.3493264428225843,"score_spread":0.2965542020112722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2163141266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0015803748,0.000088185574,0.9977957,0.000016653239,0.000033596447,0.000016310978,0.000016366092,0.0001846825,0.00026814625],"genre_scores_gemma":[0.06699581,0.0002813821,0.9299126,0.000036178364,0.000056454992,0.00008615635,0.00016151914,0.0000657156,0.0024040774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996785,0.000042546457,0.000023250741,0.000074755786,0.00016378962,0.000017210728],"domain_scores_gemma":[0.9996903,0.00008392759,0.000034779572,0.000044614695,0.00012965941,0.00001676715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034420774,0.00041526984,0.00042173226,0.00086251897,0.00028104978,0.00038035182,0.0008474721,0.0005708669,0.0022165321],"category_scores_gemma":[0.0014120007,0.00027010706,0.00045011815,0.0006718609,0.00020752345,0.0008163101,0.00051697204,0.00046884283,0.0008065872],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013049417,0.00003656595,0.00065180263,0.00015575685,0.000052407886,0.000070910435,0.000080665,0.026381157,0.10806271,0.007393323,0.0024431134,0.854541],"study_design_scores_gemma":[0.00003407999,0.00010966232,0.0010479996,0.000021740336,0.000046033245,0.00039977333,0.000026735255,0.9259717,0.052425466,0.0026014652,0.017273037,0.000042426098],"about_ca_topic_score_codex":0.0017494364,"about_ca_topic_score_gemma":0.0017258624,"teacher_disagreement_score":0.0022165321,"about_ca_system_score_codex":0.00030463538,"about_ca_system_score_gemma":0.00037217065,"threshold_uncertainty_score":0.0074149966},"labels":[],"label_agreement":null},{"id":"W2164202775","doi":"10.1109/cvpr.2004.1315181","title":"An unsupervised, online learning framework for moving object detection","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":130,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Object detection; Background subtraction; Computer vision; Online learning; Labeled data; Machine learning; Pattern recognition (psychology); Pixel; Multimedia","score_opus":0.030040879872158928,"score_gpt":0.3306314444655524,"score_spread":0.3005905645933935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164202775","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0012979072,0.00010310917,0.9972331,0.00003354182,0.000012831623,0.000036736106,0.00003022141,0.0009576417,0.00029492108],"genre_scores_gemma":[0.11598318,0.00030017714,0.8784418,0.00015470388,0.00014887506,0.0004251646,0.00047749,0.00034970947,0.003718928],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983889,0.00043687288,0.000064860906,0.0005980671,0.0003765462,0.00013479526],"domain_scores_gemma":[0.99799526,0.0007974878,0.00021471894,0.00039602836,0.00051285466,0.000083642786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024175097,0.0013724099,0.0014337478,0.0017641346,0.00079160207,0.001159165,0.0039108614,0.0015559723,0.0022164613],"category_scores_gemma":[0.004663425,0.0007552174,0.0009599787,0.0015338799,0.0012831044,0.0022876433,0.0013259357,0.002110941,0.0016514403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018769254,0.0004742921,0.0017292127,0.00020695405,0.00015596733,0.00013963351,0.00018284073,0.25250074,0.01378695,0.031714577,0.0061816685,0.6927395],"study_design_scores_gemma":[0.000010374191,0.00006983496,0.00028836433,0.0000073057918,0.000011437476,0.000068799825,0.0000120926925,0.9812975,0.0036510106,0.012080528,0.0024871232,0.000015606422],"about_ca_topic_score_codex":0.0066137263,"about_ca_topic_score_gemma":0.010133401,"teacher_disagreement_score":0.0066137263,"about_ca_system_score_codex":0.0010958533,"about_ca_system_score_gemma":0.0016773553,"threshold_uncertainty_score":0.013150454},"labels":[],"label_agreement":null},{"id":"W2164627420","doi":"10.1109/crv.2011.34","title":"Gesture Analysis Using 3D Camera, Shape Features and Particle Filters","year":2011,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Gesture; Particle filter; Computer science; Gesture recognition; Computer vision; Artificial intelligence; Tracking (education); Set (abstract data type); Throwing; Video game; Kalman filter; Multimedia; Engineering","score_opus":0.05808194197703601,"score_gpt":0.29726433742886643,"score_spread":0.23918239545183043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164627420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0037959958,0.0001028323,0.99543977,0.000020810816,0.000013194068,0.000015373702,0.000015606454,0.00024579072,0.00035050517],"genre_scores_gemma":[0.22500567,0.0005809269,0.77179533,0.0000706508,0.0000468359,0.0000977657,0.0001347346,0.00009994747,0.0021681746],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99952245,0.00008443658,0.000025044095,0.00011788082,0.0002178457,0.00003242307],"domain_scores_gemma":[0.9996635,0.00013845965,0.000051413124,0.000056067845,0.00006834042,0.000022399425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005178037,0.00074804405,0.0009147056,0.0011263437,0.00037825783,0.0010083988,0.0005506353,0.00088722276,0.00093168387],"category_scores_gemma":[0.0013220463,0.00048566086,0.0010488374,0.000818857,0.0006276246,0.00094707566,0.0005795399,0.0006522919,0.00054281164],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023486727,0.00010414045,0.0036456177,0.00020201423,0.00021615931,0.00032443137,0.0002552679,0.26907757,0.09658829,0.011535258,0.0018415308,0.6159748],"study_design_scores_gemma":[0.0000114721715,0.000049954844,0.0023030324,0.000012623578,0.000023886654,0.00015406866,0.00002161992,0.977222,0.015687259,0.0026635283,0.0018210548,0.000029508601],"about_ca_topic_score_codex":0.008697181,"about_ca_topic_score_gemma":0.008134317,"teacher_disagreement_score":0.008697181,"about_ca_system_score_codex":0.0005898235,"about_ca_system_score_gemma":0.0007134696,"threshold_uncertainty_score":0.017293096},"labels":[],"label_agreement":null},{"id":"W2164734564","doi":"10.1109/tpami.2007.1039","title":"Learning and Removing Cast Shadows through a Multidistribution Approach","year":2007,"lang":"en","type":"article","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":138,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Robustness (evolution); Computer vision; Pixel; Shadow (psychology); Foreground detection; Mixture model; Statistical model; Gaussian; Gaussian process; Background subtraction; Pattern recognition (psychology)","score_opus":0.02983047505234876,"score_gpt":0.30929399148604114,"score_spread":0.2794635164336924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164734564","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014126625,0.00011959442,0.9849005,0.0000854843,0.000013943035,0.000018301977,0.00001929146,0.00041850776,0.00029781746],"genre_scores_gemma":[0.5585437,0.00035852147,0.43716553,0.00024477887,0.00010672629,0.00010663402,0.000300223,0.0002193378,0.002954615],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99911875,0.00017403033,0.000041663046,0.000251788,0.00031108042,0.000102604965],"domain_scores_gemma":[0.99865025,0.0005849438,0.00014121916,0.00023084448,0.0003071055,0.00008560076],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013007654,0.0006838669,0.0012334384,0.0014005549,0.0006139808,0.0008128618,0.0018146762,0.0010125837,0.0012097625],"category_scores_gemma":[0.003312369,0.00065094215,0.0012453473,0.0009070111,0.0009428904,0.0018030746,0.0018397009,0.0011304229,0.0005645211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027076973,0.00022848824,0.0043485127,0.00009900454,0.00015605084,0.00016677845,0.00018839954,0.48141265,0.019685838,0.009270977,0.0015444601,0.482628],"study_design_scores_gemma":[0.00000968184,0.000031576925,0.0005212635,0.0000029554596,0.000012050278,0.000040815128,0.000015951153,0.9936052,0.001994963,0.003351128,0.0004050515,0.000009529628],"about_ca_topic_score_codex":0.005623712,"about_ca_topic_score_gemma":0.0065894597,"teacher_disagreement_score":0.005623712,"about_ca_system_score_codex":0.0010039725,"about_ca_system_score_gemma":0.001342546,"threshold_uncertainty_score":0.011181951},"labels":[],"label_agreement":null},{"id":"W2164783262","doi":"10.1109/crv.2006.52","title":"Object detection and tracking using iterative division and correlograms","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Tracking (education); Histogram; Object detection; Video tracking; HSL and HSV; Object (grammar); Division (mathematics); Object-class detection; Pattern recognition (psychology); Mathematics; Image (mathematics); Face detection; Facial recognition system","score_opus":0.024643009428026386,"score_gpt":0.2855171775489426,"score_spread":0.26087416812091624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2164783262","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":[0.0069552613,0.0002226435,0.9910568,0.000031272066,0.000025490916,0.000025455278,0.000016070206,0.00056153577,0.0011054671],"genre_scores_gemma":[0.10465964,0.00041700253,0.8928894,0.000043853983,0.000041924974,0.000095229385,0.00009068065,0.00015096423,0.0016112714],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988341,0.000230349,0.000059949834,0.000222372,0.00056785677,0.000085440166],"domain_scores_gemma":[0.99789727,0.0010411355,0.0001999901,0.00025231598,0.0005492969,0.000059908176],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014523815,0.0006749028,0.00079983776,0.0025195635,0.0005878861,0.0012142955,0.0010130188,0.0006069156,0.0012203582],"category_scores_gemma":[0.004831054,0.0005378925,0.0005657624,0.002531552,0.00087424647,0.0014304467,0.000881896,0.0006349761,0.00072514254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023295145,0.00007750898,0.0023083375,0.00015214081,0.000097877986,0.00016609626,0.00028430208,0.09170615,0.03511687,0.02305833,0.002080172,0.8447193],"study_design_scores_gemma":[0.000035785146,0.00009912231,0.0021477877,0.000041531053,0.000056189638,0.0004028037,0.00005137922,0.9428195,0.035068743,0.01105609,0.008161036,0.000060096296],"about_ca_topic_score_codex":0.005052651,"about_ca_topic_score_gemma":0.003665176,"teacher_disagreement_score":0.005052651,"about_ca_system_score_codex":0.00078820693,"about_ca_system_score_gemma":0.00118179,"threshold_uncertainty_score":0.010046482},"labels":[],"label_agreement":null},{"id":"W2165003608","doi":"10.1109/icme.2008.4607437","title":"Automatic scheduling of CCTV camera views using a human-centric approach","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Operator (biology); Scheduling (production processes); Computer vision; Artificial intelligence; Real-time computing; Engineering","score_opus":0.1460214508098569,"score_gpt":0.3482850675243165,"score_spread":0.20226361671445958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165003608","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.112736195,0.0007381334,0.88170433,0.00016577265,0.00010604715,0.00018145755,0.000110827175,0.00205405,0.002203258],"genre_scores_gemma":[0.8150644,0.000207689,0.18342996,0.00008973157,0.00009299044,0.0000786972,0.00016375007,0.00009904711,0.0007737691],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991104,0.00022375218,0.00003784145,0.0003127442,0.00020208504,0.000113035465],"domain_scores_gemma":[0.9982632,0.00045758302,0.00037354176,0.0001734176,0.00045388573,0.0002783649],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009848019,0.0010004058,0.0011575309,0.0012319924,0.0007293536,0.00082644937,0.00088341197,0.0004618959,0.00092546776],"category_scores_gemma":[0.0019366265,0.0004038683,0.00029516837,0.00094824034,0.00037510355,0.0005757907,0.0004138192,0.00047489387,0.00023846007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011240043,0.00043510014,0.011025622,0.00017485498,0.00016461375,0.00021697064,0.00053156365,0.1752398,0.091889784,0.0026532372,0.0072646476,0.7092798],"study_design_scores_gemma":[0.00006589921,0.00024389771,0.0073406496,0.000013450191,0.000056484914,0.00014349373,0.0001442137,0.9665612,0.019594904,0.0018868706,0.0038942604,0.000054594395],"about_ca_topic_score_codex":0.009427505,"about_ca_topic_score_gemma":0.016318154,"teacher_disagreement_score":0.009427505,"about_ca_system_score_codex":0.0008956694,"about_ca_system_score_gemma":0.0018964623,"threshold_uncertainty_score":0.018745303},"labels":[],"label_agreement":null},{"id":"W2165927991","doi":"10.1109/avss.2008.19","title":"Evaluation of Background Subtraction Algorithms with Post-Processing","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":189,"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":"Background subtraction; Computer science; Segmentation; Popularity; Artificial intelligence; Video processing; Computer vision; Image processing; Algorithm; Image segmentation; Image (mathematics); Pixel","score_opus":0.12069328559003371,"score_gpt":0.3496764280950583,"score_spread":0.22898314250502455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2165927991","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.47620082,0.0065053804,0.4972726,0.00055145874,0.00072961,0.0005797991,0.00078617607,0.008877281,0.008496842],"genre_scores_gemma":[0.5704483,0.0015650223,0.4212993,0.00022947739,0.00016746685,0.00017455063,0.0026037067,0.0006487381,0.0028634302],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9973335,0.0006268314,0.00027109968,0.00039888822,0.0011663327,0.0002034071],"domain_scores_gemma":[0.990923,0.004712412,0.0004492243,0.0006712975,0.002963258,0.00028083278],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00429526,0.0021901221,0.0014875932,0.0019954322,0.0008077818,0.001808405,0.0021662433,0.0016125154,0.0017709672],"category_scores_gemma":[0.013167724,0.00036502775,0.00076298794,0.002238596,0.0005334078,0.0016507274,0.0008616889,0.00091317523,0.0008568117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0046086,0.0011909205,0.007203816,0.0010162361,0.0006566224,0.00019284633,0.00016523078,0.20386957,0.060062684,0.0018748823,0.0035724123,0.71558625],"study_design_scores_gemma":[0.00019839723,0.0013065907,0.007734848,0.00003409788,0.00019713545,0.00032593415,0.000119232114,0.8858991,0.100484334,0.0006254445,0.0030130302,0.000061744584],"about_ca_topic_score_codex":0.0054898732,"about_ca_topic_score_gemma":0.0046819225,"teacher_disagreement_score":0.0054898732,"about_ca_system_score_codex":0.00097051583,"about_ca_system_score_gemma":0.0010150718,"threshold_uncertainty_score":0.022715747},"labels":[],"label_agreement":null},{"id":"W2166175880","doi":"10.1109/coase.2008.4626537","title":"Robot tracking using vision and laser sensors","year":2008,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer vision; Artificial intelligence; Robustness (evolution); Particle filter; Computer science; Robot; Mobile robot; Tracking system; Tracking (education); Kalman filter","score_opus":0.06900485138224219,"score_gpt":0.3243138067018392,"score_spread":0.25530895531959696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166175880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027730018,0.0009864292,0.9670743,0.00008276478,0.00008127194,0.0000389721,0.000037845602,0.0016128126,0.0023557255],"genre_scores_gemma":[0.42091867,0.0014366371,0.57074547,0.00019070531,0.00012192947,0.00014672715,0.00017745302,0.00006295316,0.006199461],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997013,0.00003869512,0.0000130935805,0.000088481145,0.00013229372,0.000026082891],"domain_scores_gemma":[0.99982834,0.00004576927,0.000039339928,0.000021847574,0.00005296715,0.00001177968],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002373751,0.00041313292,0.0004795102,0.0005200657,0.00030315196,0.0005306385,0.0006160958,0.0008226081,0.001088174],"category_scores_gemma":[0.0005417787,0.00034898665,0.00045334906,0.00046813014,0.00027201688,0.00089583494,0.00056757126,0.00037014217,0.0006107521],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034209984,0.00014649195,0.0023013048,0.00038943405,0.00012680325,0.00040985833,0.000190918,0.07095636,0.2206222,0.0065613077,0.0032690687,0.69468415],"study_design_scores_gemma":[0.000080336125,0.0005462575,0.0041655325,0.000052205116,0.000109620014,0.0009637007,0.000057519468,0.87624884,0.098219,0.004432493,0.015036166,0.00008825251],"about_ca_topic_score_codex":0.002372957,"about_ca_topic_score_gemma":0.0019129197,"teacher_disagreement_score":0.002372957,"about_ca_system_score_codex":0.00028486972,"about_ca_system_score_gemma":0.00041631327,"threshold_uncertainty_score":0.0047183633},"labels":[],"label_agreement":null},{"id":"W2166938561","doi":"10.21307/ijssis-2017-316","title":"Cooperative Multi Target Tracking Using Multi Sensor Network","year":2008,"lang":"en","type":"article","venue":"International Journal on Smart Sensing and Intelligent Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"BitTorrent tracker; Computer science; Tracking (education); Key (lock); Tracking system; Cluster analysis; Artificial intelligence; Energy (signal processing); Real-time computing; Wireless sensor network; Eye tracking; Kalman filter; Computer network; Computer security","score_opus":0.12109053174983064,"score_gpt":0.3438469453206144,"score_spread":0.22275641357078374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166938561","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06429615,0.0006524353,0.93200326,0.00015971485,0.00006552035,0.00003510143,0.000023468738,0.00030025493,0.0024641608],"genre_scores_gemma":[0.9469757,0.00021767509,0.05116648,0.000044561897,0.00003023196,0.000049457885,0.000033036165,0.00001121612,0.0014717556],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999421,0.00018273409,0.000029257451,0.0001419808,0.0001775222,0.000047476038],"domain_scores_gemma":[0.999212,0.00039307636,0.00011112994,0.00008300617,0.00016329529,0.000037416594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079302373,0.00041294313,0.00062066864,0.00067172706,0.0004372842,0.0006237021,0.00067595334,0.0007028536,0.00068548776],"category_scores_gemma":[0.0013532109,0.00026108677,0.0003283926,0.00073488825,0.00031802742,0.0011453562,0.0007571277,0.00040500122,0.00015623929],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023804023,0.00007271638,0.0011461137,0.0000910843,0.00006214526,0.00015501409,0.000085701424,0.8620565,0.021411253,0.005065376,0.00067714864,0.1089389],"study_design_scores_gemma":[0.0000045596835,0.000029518094,0.00014626606,0.0000025305305,0.000004979298,0.00002152913,0.000007002609,0.9969098,0.0013786885,0.0012331046,0.00025859664,0.0000034076538],"about_ca_topic_score_codex":0.0013975112,"about_ca_topic_score_gemma":0.0011490893,"teacher_disagreement_score":0.0013975112,"about_ca_system_score_codex":0.00055148295,"about_ca_system_score_gemma":0.00030392173,"threshold_uncertainty_score":0.0041939616},"labels":[],"label_agreement":null},{"id":"W2166943865","doi":"10.1109/tip.2010.2087764","title":"Real-Time Discriminative Background Subtraction","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; Memorial University of Newfoundland","funders":"","keywords":"Computer science; Background subtraction; Discriminative model; Markov random field; Artificial intelligence; Robustness (evolution); Graphics processing unit; Inference; Pixel; Maximum a posteriori estimation; A priori and a posteriori; Pattern recognition (psychology); Image segmentation; Computer vision; Segmentation; Mathematics","score_opus":0.02556292082399655,"score_gpt":0.31747080540923794,"score_spread":0.2919078845852414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2166943865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008267649,0.0001858323,0.9900024,0.00007313239,0.00004637631,0.000017363574,0.00004553313,0.00064037286,0.00072123],"genre_scores_gemma":[0.20923202,0.0004528022,0.78199214,0.00016124478,0.00013202893,0.00004936079,0.00047471368,0.0003181861,0.0071875094],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944824,0.00009249785,0.000020088562,0.00020507225,0.00017562049,0.00005846883],"domain_scores_gemma":[0.99928087,0.00023858738,0.000055291825,0.00019090176,0.00018707267,0.000047327536],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000800983,0.0011336753,0.0012821503,0.00092612096,0.00039108636,0.0014282458,0.0016982484,0.0011827704,0.0017351536],"category_scores_gemma":[0.0017045676,0.0005557651,0.00075572706,0.0011366382,0.00050919194,0.001125431,0.0010819862,0.0012492524,0.001078788],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000419317,0.00018552289,0.0011135611,0.00015650182,0.00014126873,0.00018890113,0.00013533658,0.15033977,0.08908748,0.009786276,0.00594904,0.742497],"study_design_scores_gemma":[0.000015945143,0.000056170382,0.0006465116,0.000007704157,0.00002648048,0.00023487864,0.00001930636,0.9610616,0.03058316,0.0033959888,0.0039338297,0.000018408698],"about_ca_topic_score_codex":0.0030347449,"about_ca_topic_score_gemma":0.0048695724,"teacher_disagreement_score":0.0030347449,"about_ca_system_score_codex":0.00063874235,"about_ca_system_score_gemma":0.0006609814,"threshold_uncertainty_score":0.006034136},"labels":[],"label_agreement":null},{"id":"W2167163524","doi":"10.1109/crv.2006.5","title":"A Novel Clustering-Based Method for Adaptive Background Segmentation","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Computer science; Cluster analysis; HSL and HSV; Computer vision; Histogram; Color space; Discriminative model; Segmentation; RGB color model; Shadow (psychology); Image segmentation; RGB color space; Color histogram; Pattern recognition (psychology); Image (mathematics); Color image; Image processing","score_opus":0.06142535193244675,"score_gpt":0.34633887202027247,"score_spread":0.2849135200878257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167163524","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016758939,0.00020617768,0.9962566,0.000030719988,0.000057855756,0.000027590055,0.00004565498,0.0010955962,0.0006039423],"genre_scores_gemma":[0.029379006,0.00034609565,0.9665698,0.00007625077,0.00007805767,0.0000719315,0.00036564312,0.0004445463,0.002668732],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992398,0.00007118288,0.00003480155,0.00026534355,0.00032406437,0.00006481226],"domain_scores_gemma":[0.999554,0.00007565931,0.000034839493,0.00007391308,0.00022656993,0.000035161967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004485986,0.001049753,0.0009659311,0.0022953749,0.0007380289,0.0009415698,0.0021133993,0.0010117051,0.0026987202],"category_scores_gemma":[0.0009528756,0.00064338534,0.0009057951,0.001979954,0.00039653474,0.0012108485,0.0008457184,0.0010634465,0.002429936],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013107988,0.00007862621,0.0004051683,0.0002461409,0.0001188344,0.00011521231,0.00012749764,0.026012607,0.15726845,0.005312921,0.0070582186,0.8031252],"study_design_scores_gemma":[0.000037218306,0.0000715307,0.0015057473,0.00003334109,0.00008738978,0.00064328534,0.000052556545,0.86094284,0.09782586,0.004321587,0.034374956,0.00010366714],"about_ca_topic_score_codex":0.0045486265,"about_ca_topic_score_gemma":0.005847457,"teacher_disagreement_score":0.0045486265,"about_ca_system_score_codex":0.0006761943,"about_ca_system_score_gemma":0.00081326853,"threshold_uncertainty_score":0.009044349},"labels":[],"label_agreement":null},{"id":"W2167462877","doi":"10.1007/11744085_9","title":"Robust Visual Tracking for Multiple Targets","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Particle filter; Artificial intelligence; Clutter; Tracking (education); Video tracking; Tracking system; Frame (networking); Filter (signal processing); Radar; Video processing","score_opus":0.04229944082357135,"score_gpt":0.29179060562894904,"score_spread":0.2494911648053777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167462877","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0031869758,0.0005885286,0.99413246,0.000036253507,0.00005294732,0.000009627335,0.000030968247,0.0007118724,0.0012503912],"genre_scores_gemma":[0.21284638,0.0017674927,0.76643765,0.00014961619,0.00014207371,0.00008482654,0.0006007369,0.0005937438,0.017377464],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994387,0.000054843633,0.000022825496,0.00017130883,0.0002570686,0.00005510157],"domain_scores_gemma":[0.9994815,0.00018159194,0.0000685871,0.00013284004,0.00011509314,0.000020379532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005899815,0.0008952223,0.0009457529,0.0010412218,0.00027146374,0.00092358975,0.0013425114,0.0011787702,0.0025575394],"category_scores_gemma":[0.0023640606,0.0007368638,0.0007516723,0.0010857478,0.00044893206,0.0011687374,0.0013785148,0.0009861176,0.0015871532],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021191259,0.00004489624,0.00019745325,0.00014694498,0.0000901125,0.00010825469,0.00006199115,0.14998433,0.078710355,0.011079298,0.005880051,0.7534843],"study_design_scores_gemma":[0.000015773052,0.0000610859,0.0005031821,0.000021610154,0.000031856336,0.0001989901,0.000011217146,0.9546823,0.027221942,0.01250998,0.004718217,0.000023901059],"about_ca_topic_score_codex":0.0028053003,"about_ca_topic_score_gemma":0.002511791,"teacher_disagreement_score":0.0028053003,"about_ca_system_score_codex":0.0004971559,"about_ca_system_score_gemma":0.00038675228,"threshold_uncertainty_score":0.0085558295},"labels":[],"label_agreement":null},{"id":"W2167568179","doi":"10.5194/isprsarchives-xxxviii-5-w12-301-2011","title":"INTEGRATION OF TERRESTRIAL LASER SCANNING POINTS AND 2D FLOOR PLANS BASED ON MAXIMUM SEQUENTIAL SIMILARITY","year":2012,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Centres of Excellence","keywords":"Floor plan; Similarity (geometry); Matching (statistics); Transformation (genetics); Line (geometry); Computer science; Plan (archaeology); Invariant (physics); Line segment; Horizontal and vertical; Series (stratigraphy); Artificial intelligence; Algorithm; Pattern recognition (psychology); Mathematics; Computer vision; Image (mathematics); Geometry; Engineering drawing; Engineering; Geology; Statistics","score_opus":0.032066827015871234,"score_gpt":0.28664281555421756,"score_spread":0.25457598853834634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167568179","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03012837,0.00009871854,0.9671173,0.000033989694,0.000024197534,0.00008457078,0.00014637917,0.0010705807,0.0012958049],"genre_scores_gemma":[0.2855117,0.00009066797,0.7122886,0.00003237068,0.000025301984,0.00011183003,0.0007831453,0.00012811426,0.0010282472],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99836737,0.00023114077,0.00008899686,0.00043472394,0.0007827673,0.00009502477],"domain_scores_gemma":[0.9992693,0.00016129992,0.00014562425,0.00016444176,0.0002227691,0.0000366803],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087012944,0.00066364574,0.0007962374,0.003859094,0.00043534394,0.00096002716,0.001270505,0.0006998199,0.0017985728],"category_scores_gemma":[0.0026234365,0.0005965653,0.0012469176,0.0031305992,0.0006195729,0.0014520218,0.0012217409,0.0005340213,0.0009224416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034222798,0.00020617398,0.007885474,0.0002662643,0.00018124528,0.00028832327,0.0004278582,0.14289258,0.048226662,0.0069862395,0.0018325247,0.79046446],"study_design_scores_gemma":[0.0000218983,0.000116722855,0.0046605705,0.000022667238,0.000043254233,0.00024080701,0.00012454492,0.97032434,0.016875196,0.004207428,0.0033261012,0.000036393263],"about_ca_topic_score_codex":0.0045328923,"about_ca_topic_score_gemma":0.0066384426,"teacher_disagreement_score":0.0045328923,"about_ca_system_score_codex":0.0005797159,"about_ca_system_score_gemma":0.0011382918,"threshold_uncertainty_score":0.009013057},"labels":[],"label_agreement":null},{"id":"W2167595698","doi":"10.1016/j.cviu.2015.03.010","title":"Collaborative part-based tracking using salient local predictors","year":2015,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Salient; Tracking (education); Artificial intelligence; Computer science; Computer vision; Pattern recognition (psychology); Machine learning; Psychology","score_opus":0.09344043918016402,"score_gpt":0.33525207756432124,"score_spread":0.2418116383841572,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2167595698","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00854515,0.0001720664,0.9902906,0.000034576253,0.00003582885,0.000018289273,0.00002568788,0.00047490056,0.00040287702],"genre_scores_gemma":[0.43332314,0.00049349037,0.55920434,0.00018648943,0.0001488482,0.00011858071,0.00049525953,0.00035182285,0.0056780246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988444,0.00021944132,0.00005035261,0.00043729402,0.00033314308,0.00011537925],"domain_scores_gemma":[0.9973412,0.0011004397,0.00023603425,0.0006682985,0.0004926327,0.00016138345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001716064,0.0012509283,0.0026198719,0.0015561507,0.0009943476,0.0015472951,0.0024814254,0.0019617605,0.0018473064],"category_scores_gemma":[0.0042115706,0.0015125638,0.0013922203,0.0023048664,0.0008099241,0.0023349894,0.0023759392,0.00162548,0.001556116],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085688365,0.00033146387,0.0030814644,0.00016410279,0.00033891082,0.00029568517,0.00026813822,0.31443378,0.057980392,0.0074088355,0.004531369,0.610309],"study_design_scores_gemma":[0.000010706535,0.000059669004,0.0005667352,0.000005814024,0.00003576733,0.00008175519,0.000011259498,0.99081486,0.005204746,0.0025481852,0.00064571074,0.0000148900335],"about_ca_topic_score_codex":0.0034459895,"about_ca_topic_score_gemma":0.004871498,"teacher_disagreement_score":0.0034459895,"about_ca_system_score_codex":0.0004405126,"about_ca_system_score_gemma":0.0010679755,"threshold_uncertainty_score":0.009075522},"labels":[],"label_agreement":null},{"id":"W2169671170","doi":"10.1109/cvpr.2007.383134","title":"Detecting Pedestrians by Learning Shapelet Features","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":512,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); AdaBoost; Pedestrian detection; Pattern recognition (psychology); Pedestrian; Set (abstract data type); Feature (linguistics); Machine learning; Computer vision; Engineering","score_opus":0.013716016303632098,"score_gpt":0.28799026633375496,"score_spread":0.27427425003012285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2169671170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07287341,0.00042660296,0.92281204,0.00017152689,0.00011293573,0.00010561097,0.00025729378,0.0019101585,0.0013303543],"genre_scores_gemma":[0.32965863,0.00058339746,0.6633695,0.00026300497,0.00016264497,0.000087488464,0.0017850641,0.00023810376,0.003852168],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934226,0.00010800828,0.00002802562,0.00022694899,0.00021020744,0.00008448039],"domain_scores_gemma":[0.9988059,0.00045099866,0.00014548512,0.00016467695,0.00033453488,0.00009835213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010926494,0.0010891608,0.0015831242,0.0032432585,0.000456345,0.0011241115,0.0011368841,0.0014601161,0.0012677351],"category_scores_gemma":[0.0025404778,0.00073470554,0.001129579,0.0012955996,0.0005119257,0.0016874124,0.00086657616,0.0009962284,0.002181887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00079125026,0.0003908441,0.016539874,0.0001511688,0.00020750497,0.00043542782,0.00013765611,0.05028548,0.063621916,0.0027384923,0.0072044195,0.8574959],"study_design_scores_gemma":[0.000035813486,0.00025844303,0.008648778,0.00003226497,0.00008782815,0.0008400069,0.000087631124,0.943784,0.03658992,0.005682316,0.003909341,0.00004370057],"about_ca_topic_score_codex":0.001447149,"about_ca_topic_score_gemma":0.0019128332,"teacher_disagreement_score":0.0032432585,"about_ca_system_score_codex":0.0004740474,"about_ca_system_score_gemma":0.00045680703,"threshold_uncertainty_score":0.0057784915},"labels":[],"label_agreement":null},{"id":"W2170567421","doi":"10.1109/crv.2007.20","title":"Constructing Face Image Logs that are Both Complete and Concise","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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":"Face (sociological concept); Computer science; Construct (python library); Artificial intelligence; Image (mathematics); Computer vision; Process (computing); Quality (philosophy); Face detection; Selection (genetic algorithm); Image quality; Sequence (biology); Object-class detection; Facial recognition system; Pattern recognition (psychology)","score_opus":0.052270183357792856,"score_gpt":0.3066771988927986,"score_spread":0.2544070155350057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170567421","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07505783,0.00020121285,0.9088095,0.00019382182,0.00008677197,0.0006409288,0.0024274886,0.00947915,0.0031032392],"genre_scores_gemma":[0.26596415,0.00033315798,0.7190633,0.00015529142,0.000076706536,0.00082519813,0.009043662,0.001004187,0.0035343745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989235,0.00020057913,0.00008501636,0.00019022859,0.00053987047,0.000060747097],"domain_scores_gemma":[0.9907178,0.0029457237,0.0012883341,0.0024630874,0.002259252,0.00032564744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001393929,0.0008180727,0.0007258343,0.002420663,0.00048353113,0.001504556,0.0008735995,0.0005429744,0.0030744225],"category_scores_gemma":[0.012631923,0.00058606896,0.0005487334,0.0010270904,0.00064382795,0.003643128,0.0014093213,0.0010051625,0.0018062665],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011904474,0.00089550245,0.01811548,0.0007686212,0.00010518537,0.0007369779,0.0011777929,0.028786771,0.10369858,0.01551397,0.02055409,0.8084566],"study_design_scores_gemma":[0.00012096629,0.0013753675,0.030641817,0.00022163251,0.00013846095,0.003003151,0.0017265572,0.5877037,0.2373005,0.046542246,0.090856574,0.00036902394],"about_ca_topic_score_codex":0.0010701254,"about_ca_topic_score_gemma":0.0017300847,"teacher_disagreement_score":0.0030744225,"about_ca_system_score_codex":0.00035978243,"about_ca_system_score_gemma":0.00070821244,"threshold_uncertainty_score":0.01028502},"labels":[],"label_agreement":null},{"id":"W2170936221","doi":"10.1109/icassp.2006.1660315","title":"Monocular Human Motion Tracking with the DE-MC Particle Filter","year":2006,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Particle filter; Tracking (education); Computer vision; Computer science; Artificial intelligence; State space; Auxiliary particle filter; Motion (physics); Video tracking; Key (lock); Markov chain; Filter (signal processing); Eye tracking; Trajectory; Kalman filter; Mathematics; Object (grammar); Extended Kalman filter; Machine learning; Physics; Ensemble Kalman filter","score_opus":0.025162545343503154,"score_gpt":0.2721083979406938,"score_spread":0.24694585259719065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2170936221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008547568,0.00013451392,0.9903267,0.00006460614,0.00002385558,0.000015985119,0.000019252111,0.00016366276,0.000703925],"genre_scores_gemma":[0.3836175,0.00029579978,0.6126976,0.00010867362,0.00004739086,0.000080345846,0.00014505157,0.000036668273,0.002970946],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964726,0.00007094632,0.000012779043,0.00010143942,0.00014418866,0.000023361814],"domain_scores_gemma":[0.99950075,0.00021280462,0.000054354965,0.000097545926,0.00011475297,0.00001978461],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00068905216,0.0003057836,0.00045617588,0.00046936516,0.00023488978,0.0004062945,0.00055639294,0.0005551764,0.0005840373],"category_scores_gemma":[0.0023570983,0.00032323273,0.0003243845,0.00057749834,0.00031873223,0.00060777203,0.00052087614,0.00057453424,0.00017701616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023354618,0.00008370827,0.0024506694,0.000084023486,0.000096917895,0.00010887476,0.00009927303,0.5636543,0.02538392,0.022609344,0.002363777,0.3828317],"study_design_scores_gemma":[0.0000061310357,0.0000146690545,0.00038705795,0.000002101374,0.0000037574468,0.000030824387,0.0000022996428,0.9948814,0.0026637749,0.001204508,0.0007982184,0.0000052629807],"about_ca_topic_score_codex":0.006625826,"about_ca_topic_score_gemma":0.0051719025,"teacher_disagreement_score":0.006625826,"about_ca_system_score_codex":0.00049137935,"about_ca_system_score_gemma":0.00060316094,"threshold_uncertainty_score":0.013174534},"labels":[],"label_agreement":null},{"id":"W2171326479","doi":"10.1109/crv.2007.13","title":"Automatic Detection and Clustering of Actor Faces based on Spectral Clustering Techniques","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"","keywords":"Cluster analysis; Computer science; Artificial intelligence; Search engine indexing; Pattern recognition (psychology); Classifier (UML); Face detection; Cascade; Face (sociological concept); Facial recognition system; Computer vision; Feature (linguistics)","score_opus":0.021547381004246993,"score_gpt":0.2971376782576801,"score_spread":0.27559029725343315,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171326479","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.059537906,0.00022622081,0.9359811,0.000070657064,0.00004838424,0.000121860896,0.00018075258,0.001776677,0.0020564361],"genre_scores_gemma":[0.23849922,0.00026146177,0.75842613,0.00004409984,0.000043615837,0.0000993522,0.00046418235,0.0001622724,0.0019996837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993218,0.00008992595,0.00003135799,0.00018222592,0.00029593252,0.000078776415],"domain_scores_gemma":[0.99928087,0.00014930358,0.000075934964,0.0001295775,0.00032318174,0.000041168943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006079525,0.00057056715,0.0007598339,0.002998664,0.000751213,0.0007127373,0.00083186023,0.0005920801,0.0019021842],"category_scores_gemma":[0.0015178315,0.0003249804,0.00049408065,0.0012112516,0.00041239834,0.00081219984,0.00056676386,0.0004476859,0.0017054327],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036225774,0.00021489584,0.0040435805,0.000120296536,0.000075820026,0.00017202769,0.00025181702,0.0305656,0.23168601,0.004904461,0.0046840534,0.72291917],"study_design_scores_gemma":[0.000022567618,0.000111589135,0.0074784663,0.000018837634,0.00003612233,0.00074290205,0.00017052852,0.8627153,0.11935348,0.0051175104,0.0041732327,0.000059467795],"about_ca_topic_score_codex":0.0040204613,"about_ca_topic_score_gemma":0.0051458445,"teacher_disagreement_score":0.0040204613,"about_ca_system_score_codex":0.00045717668,"about_ca_system_score_gemma":0.0004516074,"threshold_uncertainty_score":0.007994115},"labels":[],"label_agreement":null},{"id":"W2171804456","doi":"10.1109/ccece.2013.6567814","title":"Automated audience polling on iPhone","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Polling; Computer science; AdaBoost; Computer vision; Voting; Artificial intelligence; Object (grammar); Object detection; Field (mathematics); Class (philosophy); Computer graphics (images); Pattern recognition (psychology); Support vector machine; Mathematics","score_opus":0.02145697217330289,"score_gpt":0.2853895046933972,"score_spread":0.2639325325200943,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2171804456","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27516022,0.000756643,0.67917645,0.00017935941,0.0003139361,0.00024196267,0.0005698704,0.025011435,0.018590124],"genre_scores_gemma":[0.8392337,0.00019969724,0.15026654,0.000110335364,0.00010120492,0.000095582414,0.00048325007,0.000512796,0.008996945],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995902,0.000050749983,0.000009740762,0.00010663498,0.00017509086,0.00006753323],"domain_scores_gemma":[0.9996617,0.000076266384,0.00003400392,0.00004654687,0.00012848117,0.00005307188],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036965974,0.00045667664,0.00086920214,0.0012396541,0.0003483297,0.0006309935,0.0006998112,0.000563479,0.004059906],"category_scores_gemma":[0.0007896432,0.00022336398,0.0003339556,0.00034998936,0.00017986225,0.00047432343,0.0006591113,0.00043863265,0.001501747],"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":[0.00053319003,0.00011676316,0.007806328,0.0001582716,0.00007378614,0.00038711904,0.0003598281,0.0051152566,0.26470536,0.0010881558,0.011206137,0.7084498],"study_design_scores_gemma":[0.000121392244,0.0004092041,0.07525577,0.00008551737,0.00011368641,0.001786472,0.00037620362,0.7053379,0.18730198,0.003056968,0.026023507,0.00013141263],"about_ca_topic_score_codex":0.0018911292,"about_ca_topic_score_gemma":0.0028804443,"teacher_disagreement_score":0.004059906,"about_ca_system_score_codex":0.00029584314,"about_ca_system_score_gemma":0.00019861512,"threshold_uncertainty_score":0.013581753},"labels":[],"label_agreement":null},{"id":"W2172099411","doi":"","title":"Particle Filter Based Tracking for Crossing of Targets with Similar Pattern","year":2007,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Tracking (education); Particle filter; Artificial intelligence; Computer vision; Computer science; Histogram; Probabilistic logic; Similarity (geometry); Object (grammar); Likelihood function; Video tracking; Pattern recognition (psychology); Filter (signal processing); Image (mathematics); Algorithm; Estimation theory","score_opus":0.04369819101899602,"score_gpt":0.31730404480818397,"score_spread":0.27360585378918795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2172099411","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0066224863,0.00023259876,0.99183017,0.00005351241,0.00005234588,0.000024278685,0.000014667435,0.00045334105,0.0007166357],"genre_scores_gemma":[0.30069864,0.00081497116,0.69292647,0.00012948876,0.00008247567,0.00012131107,0.00018094145,0.000106737956,0.004939078],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994772,0.00009747872,0.000021721753,0.00012458565,0.00023819703,0.000040948802],"domain_scores_gemma":[0.9990996,0.0003984619,0.0001290814,0.00010404636,0.0002340023,0.00003471708],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011372425,0.0006494883,0.0010717211,0.0010152507,0.000630518,0.0008640156,0.00086874847,0.0013208538,0.0013028503],"category_scores_gemma":[0.002958578,0.00042804988,0.00061730744,0.001066161,0.00059209537,0.0011169214,0.0006173798,0.00087910646,0.0005904425],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027152163,0.00016622416,0.003808047,0.00023831251,0.0002093693,0.00052145397,0.0003058587,0.5071834,0.032894876,0.024206374,0.005387699,0.42480692],"study_design_scores_gemma":[0.000011902065,0.000036522575,0.0006929587,0.0000071773943,0.000021097829,0.00011132942,0.000007496579,0.9912418,0.0038278652,0.0023614033,0.001666853,0.00001361042],"about_ca_topic_score_codex":0.0060551595,"about_ca_topic_score_gemma":0.0041124714,"teacher_disagreement_score":0.0060551595,"about_ca_system_score_codex":0.00087399455,"about_ca_system_score_gemma":0.0008469651,"threshold_uncertainty_score":0.012039781},"labels":[],"label_agreement":null},{"id":"W2177776506","doi":"10.1016/j.procs.2015.10.030","title":"Video Foreground Detection in Non-static Background Using Multi-dimensional Color Space","year":2015,"lang":"en","type":"article","venue":"Procedia Computer Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Foreground detection; Computer graphics (images); Space (punctuation); Color space; Background subtraction; Pixel; Image (mathematics)","score_opus":0.0785705464570195,"score_gpt":0.3329366249137926,"score_spread":0.2543660784567731,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2177776506","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35577598,0.0006702449,0.6390395,0.00009987953,0.000088478315,0.000101405676,0.00012479347,0.0023171052,0.0017825856],"genre_scores_gemma":[0.6332162,0.00043407743,0.3647106,0.00007949061,0.000026341677,0.000035327772,0.00028642773,0.00007645229,0.001135084],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99959177,0.00006916438,0.000019235475,0.00012349477,0.00013101958,0.00006527433],"domain_scores_gemma":[0.9994173,0.00017508479,0.00006780592,0.000076905206,0.00020158575,0.000061375315],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00062159833,0.0007799192,0.000624113,0.001183685,0.00031559693,0.00088884146,0.0007171134,0.00061729364,0.0006700637],"category_scores_gemma":[0.0013666924,0.00017300915,0.00037150533,0.00089563825,0.00036469765,0.0008125331,0.00044580386,0.00042401973,0.00028654904],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013732058,0.00052364543,0.010627464,0.00034709286,0.00018088127,0.00044204373,0.0002705421,0.07506799,0.33845758,0.003124283,0.0014235278,0.5681617],"study_design_scores_gemma":[0.00003896248,0.00039821415,0.01085613,0.00001769844,0.000061461535,0.000530393,0.00009522073,0.820092,0.16561353,0.00074732833,0.0015080288,0.000041082545],"about_ca_topic_score_codex":0.0029179773,"about_ca_topic_score_gemma":0.003010631,"teacher_disagreement_score":0.0029179773,"about_ca_system_score_codex":0.00038529115,"about_ca_system_score_gemma":0.00040458926,"threshold_uncertainty_score":0.0058020353},"labels":[],"label_agreement":null},{"id":"W2189345037","doi":"","title":"Glint: An MDS Framework for Costly Distance Functions","year":2012,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Scalability; Computer science; Function (biology); Algorithm; Scaling; Multidimensional scaling; Distance matrix; Mathematics; Machine learning","score_opus":0.06221842355510967,"score_gpt":0.3625123627079709,"score_spread":0.30029393915286123,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2189345037","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":[0.0005347638,0.00008124025,0.9981133,0.000051369687,0.000020436606,0.000026679105,0.00008970026,0.00075869495,0.00032388247],"genre_scores_gemma":[0.02159328,0.0002397412,0.97535896,0.000065974236,0.00005437933,0.0003194137,0.00043747263,0.0006537853,0.0012770433],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99788433,0.00071927387,0.00014682012,0.00038136123,0.0007733649,0.000094844196],"domain_scores_gemma":[0.99674135,0.0011486801,0.00032371623,0.0008339382,0.0007686757,0.00018375304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027937803,0.002523519,0.0021785519,0.0028345317,0.0015822215,0.0026096462,0.004719565,0.0018726331,0.006356727],"category_scores_gemma":[0.011249895,0.0011553509,0.0018693028,0.0027887723,0.0017023254,0.0031481723,0.00526594,0.0035826443,0.0032875782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019123044,0.00008109157,0.00086028676,0.0003974712,0.0001622776,0.0001837047,0.0003683651,0.52552766,0.0066200835,0.201575,0.020105198,0.24392763],"study_design_scores_gemma":[0.000039049224,0.000059626775,0.00011044514,0.000034875313,0.000014622946,0.000121986785,0.000046666064,0.91532564,0.0025817829,0.0679359,0.01368742,0.000041937754],"about_ca_topic_score_codex":0.004307557,"about_ca_topic_score_gemma":0.0073383097,"teacher_disagreement_score":0.006356727,"about_ca_system_score_codex":0.0016139038,"about_ca_system_score_gemma":0.002883064,"threshold_uncertainty_score":0.021265328},"labels":[],"label_agreement":null},{"id":"W2203916885","doi":"","title":"Presenting an Efficient Method for Vehicle Detection using Edge Detection and Morphological Operations","year":2015,"lang":"en","type":"article","venue":"Journal of academic and applied studies","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Background subtraction; Computer vision; Sobel operator; Frame (networking); Artificial intelligence; Object detection; Centroid; Noise (video); Computational complexity theory; Edge detection; Filter (signal processing); Task (project management); Enhanced Data Rates for GSM Evolution; Pattern recognition (psychology); Image (mathematics); Pixel; Image processing; Algorithm; Engineering","score_opus":0.15561199648026636,"score_gpt":0.4151090324210571,"score_spread":0.25949703594079077,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2203916885","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":[0.002598715,0.00016632615,0.99606353,0.00004139001,0.000075616095,0.000038043127,0.000024933284,0.0005645382,0.00042694522],"genre_scores_gemma":[0.025781397,0.0004164705,0.97130907,0.00004248509,0.00007618599,0.000058020967,0.00016958483,0.00007430909,0.0020723648],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961746,0.000037439666,0.0000332466,0.000092562404,0.00019179448,0.000027415826],"domain_scores_gemma":[0.9996877,0.00006479692,0.00003169524,0.00005929565,0.00014011495,0.00001641825],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038564962,0.0008940447,0.00068170985,0.001753938,0.00034718474,0.0007949174,0.0011499439,0.001168256,0.0027813308],"category_scores_gemma":[0.0007537237,0.00062216556,0.00086747046,0.0013541514,0.0003360265,0.0012359986,0.0005954062,0.0008006572,0.0026420308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010046513,0.00012133175,0.000936886,0.00040235138,0.00009498051,0.00038949717,0.0000945754,0.011272731,0.259754,0.0066225277,0.004788613,0.7154221],"study_design_scores_gemma":[0.000055218585,0.0003850846,0.0029754622,0.000050822222,0.00014974651,0.004008541,0.00008579429,0.66474223,0.27804148,0.00803235,0.041332535,0.00014081733],"about_ca_topic_score_codex":0.00055539765,"about_ca_topic_score_gemma":0.0007927423,"teacher_disagreement_score":0.0027813308,"about_ca_system_score_codex":0.00020079136,"about_ca_system_score_gemma":0.00047403798,"threshold_uncertainty_score":0.009304464},"labels":[],"label_agreement":null},{"id":"W2215825884","doi":"","title":"Birds/Bats Motion Tracking with Infrared Radiation Camera for Wind Farm Applications","year":2012,"lang":"en","type":"article","venue":"OhioLink ETD Center (Ohio Library and Information Network)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Remote sensing; Tracking (education); Infrared; Computer vision; Geography; Motion (physics); Match moving; Environmental science; Artificial intelligence; Computer science; Astronomy; Physics","score_opus":0.01415234001979019,"score_gpt":0.23344666735425462,"score_spread":0.21929432733446444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2215825884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19492726,0.0007994628,0.7971146,0.00009474855,0.000058700687,0.000105534964,0.00011557756,0.0011393032,0.005644817],"genre_scores_gemma":[0.6382844,0.00056730804,0.35537335,0.00007394009,0.000034064167,0.000110201836,0.00027279626,0.000049949416,0.0052340706],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998536,0.000027861315,0.0000047218086,0.000045104072,0.000057705438,0.000010971093],"domain_scores_gemma":[0.9999075,0.000017916343,0.000018016475,0.0000103867205,0.000038529535,0.000007672111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022752662,0.00021351059,0.00016404461,0.00038643216,0.00018225501,0.0002987629,0.00024970228,0.00024507326,0.000863942],"category_scores_gemma":[0.00028756904,0.00016449369,0.00015794147,0.00025892016,0.000093371375,0.0003323634,0.00013748003,0.00014455894,0.00041684264],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002454943,0.00014193791,0.0150509225,0.00018138507,0.00005587968,0.0001452989,0.00019631922,0.03654308,0.29391858,0.0015152409,0.0037925777,0.64821327],"study_design_scores_gemma":[0.00003970381,0.00044069343,0.040492386,0.000050855906,0.000089779314,0.00037906857,0.0001450276,0.85989803,0.08439348,0.00079009455,0.013243486,0.000037364283],"about_ca_topic_score_codex":0.0023334546,"about_ca_topic_score_gemma":0.00421569,"teacher_disagreement_score":0.0023334546,"about_ca_system_score_codex":0.00018540882,"about_ca_system_score_gemma":0.0002422192,"threshold_uncertainty_score":0.0046397448},"labels":[],"label_agreement":null},{"id":"W2235559271","doi":"10.1007/978-3-319-24261-3_11","title":"Unsupervised Motion Segmentation Using Metric Embedding of Features","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Embedding; Computer vision; Motion (physics); Metric (unit); Image segmentation; A priori and a posteriori; Prior probability; Pattern recognition (psychology)","score_opus":0.05885663374578947,"score_gpt":0.3361807769074182,"score_spread":0.27732414316162873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2235559271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009349702,0.00023724066,0.9886147,0.00003903189,0.000027957632,0.000035948055,0.00011578605,0.0008455386,0.00073418533],"genre_scores_gemma":[0.1845522,0.00050718745,0.80797714,0.00006174561,0.00007092177,0.000118142816,0.0016799186,0.00060789345,0.00442485],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99961567,0.00005831017,0.000021793821,0.00015989675,0.00009113206,0.000053160413],"domain_scores_gemma":[0.99956864,0.000117545664,0.00005745329,0.00009803911,0.00012799008,0.000030391093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033988437,0.0010110062,0.001303564,0.0014878524,0.00031379977,0.0009052318,0.0012494165,0.00081395934,0.0016525688],"category_scores_gemma":[0.0011291993,0.0005909072,0.00089524663,0.0019281594,0.00057476346,0.0011432759,0.0011057588,0.00086861436,0.0013033585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021876133,0.00008858976,0.0008442876,0.00016674511,0.000085737964,0.0000657939,0.000135818,0.09243432,0.077428095,0.011268818,0.0052471785,0.8120157],"study_design_scores_gemma":[0.000009797725,0.00007184612,0.0009954543,0.000022119708,0.000017946104,0.00009563422,0.00003311065,0.97102565,0.014873056,0.009577884,0.0032596467,0.00001788794],"about_ca_topic_score_codex":0.0035906327,"about_ca_topic_score_gemma":0.005442317,"teacher_disagreement_score":0.0035906327,"about_ca_system_score_codex":0.0005571441,"about_ca_system_score_gemma":0.00067964173,"threshold_uncertainty_score":0.007139504},"labels":[],"label_agreement":null},{"id":"W2244252896","doi":"10.1109/iccvw.2015.80","title":"Scalable Kernel Correlation Filter with Sparse Feature Integration","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":89,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Kernel (algebra); BitTorrent tracker; Eye tracking; Video tracking; Scalability; Benchmark (surveying); Correlation; Filter (signal processing); Pattern recognition (psychology); Computer vision; Mathematics; Object (grammar)","score_opus":0.04202676590144399,"score_gpt":0.2770659564973058,"score_spread":0.2350391905958618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2244252896","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":[0.003991606,0.00013437208,0.9942668,0.000042552143,0.000033906184,0.000022416583,0.00005156392,0.0011038877,0.00035289873],"genre_scores_gemma":[0.15635993,0.00034264065,0.83833456,0.00014794059,0.00008724442,0.00013541615,0.00084282074,0.00029842657,0.0034510447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992035,0.00013187395,0.00003399939,0.00019563654,0.0003562178,0.000078772275],"domain_scores_gemma":[0.9986089,0.00036429745,0.00013623899,0.0003758054,0.00044553322,0.0000692376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011577951,0.00089319114,0.001298217,0.001025167,0.0004024787,0.00079057226,0.001299521,0.0009913496,0.002383889],"category_scores_gemma":[0.0043742177,0.00048680545,0.0008737793,0.0022203287,0.0003918107,0.0017165003,0.0012562046,0.001266287,0.0019102714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026245843,0.00018117638,0.0017890851,0.00012344179,0.00017208523,0.00014550006,0.00008757651,0.1429405,0.03485626,0.014276833,0.01485488,0.7903102],"study_design_scores_gemma":[0.000012953375,0.000033476095,0.00035645926,0.0000051623383,0.000012942878,0.000078007026,0.000007169084,0.98968434,0.0055548134,0.0020025275,0.0022396767,0.000012554614],"about_ca_topic_score_codex":0.0072515234,"about_ca_topic_score_gemma":0.010199975,"teacher_disagreement_score":0.0072515234,"about_ca_system_score_codex":0.00067427533,"about_ca_system_score_gemma":0.0017655956,"threshold_uncertainty_score":0.014418662},"labels":[],"label_agreement":null},{"id":"W2247229935","doi":"10.1109/iccvw.2015.86","title":"The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":113,"is_retracted":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":"BitTorrent tracker; Computer vision; Computer science; Artificial intelligence; Tracking (education); Benchmark (surveying); Video tracking; Infrared; Object (grammar); Term (time); Thermal infrared; Visualization; Eye tracking; Physics; Optics; Geography","score_opus":0.07255331517948924,"score_gpt":0.3342199512109393,"score_spread":0.26166663603145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2247229935","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":[0.30588824,0.03210091,0.29500547,0.0038488936,0.019044133,0.007213169,0.17721218,0.073642045,0.08604501],"genre_scores_gemma":[0.30503157,0.0021050056,0.12625284,0.0018127144,0.0013261122,0.0016969695,0.52497303,0.003222285,0.03357949],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9926997,0.0014887376,0.0005211006,0.0021846036,0.0022169924,0.0008889828],"domain_scores_gemma":[0.9942074,0.0011665776,0.00032327042,0.0014773104,0.0020911405,0.00073423795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00779671,0.0042599435,0.0028328705,0.0016872511,0.0013983614,0.0032320188,0.003051255,0.0040116953,0.0066899695],"category_scores_gemma":[0.016709184,0.00045235056,0.001970768,0.001492241,0.00089764845,0.003097171,0.003832378,0.0024544834,0.0077195163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030269232,0.0015538033,0.008340711,0.0028841689,0.0010422728,0.0007356988,0.00034113764,0.04050994,0.02178354,0.0034202812,0.60972536,0.30663615],"study_design_scores_gemma":[0.0018401872,0.0053169033,0.049483452,0.0014642702,0.0009149616,0.0052051223,0.0012213037,0.49715874,0.06387177,0.019031668,0.35386106,0.00063063693],"about_ca_topic_score_codex":0.01731823,"about_ca_topic_score_gemma":0.022499204,"teacher_disagreement_score":0.01731823,"about_ca_system_score_codex":0.001661273,"about_ca_system_score_gemma":0.002443927,"threshold_uncertainty_score":0.04123342},"labels":[],"label_agreement":null},{"id":"W2273867668","doi":"10.1109/iranianmvip.2015.7397529","title":"Adaptive Gaussian kernel learning for sparse Bayesian classification: An approach for silhouette based vehicle classification","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Kernel (algebra); Radial basis function kernel; Machine learning; Support vector machine; Gaussian function; Kernel method; Gaussian process; Polynomial kernel; Classifier (UML); Gaussian; Mathematics","score_opus":0.17353529962614733,"score_gpt":0.33987431565076714,"score_spread":0.1663390160246198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2273867668","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003401389,0.00021168271,0.99566215,0.00008703702,0.000020097612,0.00002295491,0.000026050442,0.00031880144,0.00024991392],"genre_scores_gemma":[0.3163558,0.00088554213,0.67737687,0.0002660554,0.00027329152,0.00020865627,0.0005430763,0.00026056104,0.003830246],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998458,0.00041077044,0.000087769404,0.00035595,0.0005315085,0.00015592575],"domain_scores_gemma":[0.9984862,0.00047364805,0.00019996027,0.00023098185,0.00053590414,0.00007332818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018930861,0.0007138573,0.001462816,0.0020712672,0.00048079356,0.0009908048,0.0026040545,0.0015562981,0.001422059],"category_scores_gemma":[0.0046928073,0.00047390466,0.00104113,0.0020364793,0.00082499493,0.001609312,0.0014545629,0.001645401,0.0012323011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018111098,0.00021657592,0.0023512188,0.00015254915,0.00013382487,0.0000724465,0.00017010201,0.110984914,0.011809746,0.0151985595,0.0049066413,0.85382235],"study_design_scores_gemma":[0.0000057165707,0.000028263676,0.00041229595,0.000010342373,0.000013087132,0.00005630877,0.000016918151,0.9903936,0.002343254,0.005160039,0.0015474115,0.000012633549],"about_ca_topic_score_codex":0.003853366,"about_ca_topic_score_gemma":0.003417466,"teacher_disagreement_score":0.003853366,"about_ca_system_score_codex":0.000913626,"about_ca_system_score_gemma":0.0010426905,"threshold_uncertainty_score":0.010011733},"labels":[],"label_agreement":null},{"id":"W2275245736","doi":"","title":"Real-time object detection and background maintenance for uncontrolled environments","year":2008,"lang":"en","type":"article","venue":"International Conference on Signal Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Institut National d'Optique","funders":"","keywords":"Background subtraction; Initialization; Computer science; Object detection; Codebook; Frame (networking); Real-time computing; Artificial intelligence; Computer vision; Object (grammar); Pattern recognition (psychology); Pixel","score_opus":0.05663572363898937,"score_gpt":0.309233729626583,"score_spread":0.25259800598759363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2275245736","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":[0.059263814,0.00017981221,0.9385182,0.00003737702,0.000028019922,0.00003110759,0.000048465266,0.0011246016,0.0007686953],"genre_scores_gemma":[0.6459795,0.00026664877,0.35067946,0.000035700337,0.000043384352,0.00006280531,0.00030878646,0.00014898836,0.0024747057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996126,0.00005966693,0.000013217358,0.000115640236,0.00015381342,0.00004502449],"domain_scores_gemma":[0.9996025,0.00012469091,0.00006515252,0.00007333514,0.0000983223,0.00003601435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003374929,0.0005039681,0.00059450854,0.00053181016,0.0003077586,0.00058940763,0.001024079,0.00051469187,0.0007328319],"category_scores_gemma":[0.001134746,0.00022435862,0.00023765679,0.0005405925,0.00029479014,0.0007276984,0.00059106894,0.00041537202,0.00040516842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065440877,0.00018477185,0.002733195,0.00020333454,0.000041715077,0.00059835595,0.00030177634,0.075710945,0.39760402,0.0064285793,0.0029418035,0.5125971],"study_design_scores_gemma":[0.000032574317,0.00024063078,0.0044889157,0.000009536466,0.000027468386,0.00051753107,0.000073622025,0.89907146,0.08903585,0.0023695722,0.0040991963,0.00003366595],"about_ca_topic_score_codex":0.0021254425,"about_ca_topic_score_gemma":0.0016307739,"teacher_disagreement_score":0.0021254425,"about_ca_system_score_codex":0.0003128937,"about_ca_system_score_gemma":0.00039067352,"threshold_uncertainty_score":0.004226148},"labels":[],"label_agreement":null},{"id":"W2276650849","doi":"10.1007/978-3-642-21593-3_43","title":"From Optical Flow to Tracking Objects on Movie Videos","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Tracking (education); Optical flow; Minimum bounding box; Motion (physics); Match moving; Bounding overwatch; Motion vector; Segmentation; Track (disk drive); Video tracking; Exploit; Motion estimation; Object (grammar); Tracking system; Image (mathematics)","score_opus":0.03960306121684678,"score_gpt":0.2844106236417969,"score_spread":0.24480756242495014,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2276650849","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":[0.011039956,0.002721294,0.9822035,0.0002778564,0.00019695135,0.00005521201,0.00023853398,0.00085706107,0.0024096193],"genre_scores_gemma":[0.22439075,0.008714014,0.7522069,0.00030952235,0.0006561324,0.00014535924,0.0015814684,0.00044981542,0.011545948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997528,0.000042363125,0.000010357747,0.00009283,0.00007101129,0.00003053461],"domain_scores_gemma":[0.9995503,0.00021955621,0.00003711412,0.00006563993,0.00009939873,0.00002800511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004968712,0.0010173605,0.0009479242,0.001622675,0.0004249407,0.0011971308,0.0011563451,0.0012482194,0.001971051],"category_scores_gemma":[0.0024518135,0.0007901908,0.0005512386,0.0017998128,0.0005684115,0.0023899318,0.0011704124,0.0012266875,0.00064531434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017685984,0.000076815144,0.00056491664,0.0002408583,0.00004586045,0.00009905302,0.00012509944,0.057354167,0.019480659,0.015107037,0.010805371,0.8959233],"study_design_scores_gemma":[0.000016793338,0.00006147805,0.0010375925,0.000048775644,0.000025448127,0.00014916126,0.000054426244,0.92865145,0.0116613675,0.051742148,0.0065239156,0.000027431826],"about_ca_topic_score_codex":0.00832408,"about_ca_topic_score_gemma":0.005052375,"teacher_disagreement_score":0.00832408,"about_ca_system_score_codex":0.0005592755,"about_ca_system_score_gemma":0.00041495962,"threshold_uncertainty_score":0.016551256},"labels":[],"label_agreement":null},{"id":"W2291161277","doi":"","title":"Large-Scale Multi-Sensor Monitoring of Pedestrian Dynamics in Public Spaces: Preliminary Results","year":2016,"lang":"en","type":"article","venue":"Transportation Research Board 95th Annual MeetingTransportation Research Board","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pedestrian; Extrapolation; Attendance; Scale (ratio); Computer science; Process (computing); Real-time computing; Data science; Transport engineering; Geography; Engineering; Cartography","score_opus":0.10581005596422922,"score_gpt":0.40151725746167466,"score_spread":0.29570720149744545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2291161277","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":[0.98309755,0.00014230535,0.014611359,0.000054709,0.000025320536,0.00012143128,0.00041370132,0.00046602337,0.0010676215],"genre_scores_gemma":[0.9891712,0.00010749532,0.009429173,0.00002205468,0.00001948555,0.000056470268,0.0006288564,0.000016403126,0.000548752],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99951017,0.0001353658,0.000018646138,0.00010301065,0.00013355813,0.00009935351],"domain_scores_gemma":[0.9990289,0.000309437,0.00010152306,0.00012971666,0.00025983655,0.00017065104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00086806907,0.0007065894,0.0006312973,0.0006656617,0.00039584227,0.00040493422,0.0005063306,0.0004732966,0.00085556187],"category_scores_gemma":[0.00086339103,0.00021992484,0.00046856963,0.00070453825,0.00033722582,0.00063973415,0.00061103475,0.00030521766,0.00027918644],"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":[0.0039878697,0.009784963,0.32324323,0.0009702383,0.0008498211,0.0021176971,0.0021971408,0.14673738,0.20913015,0.0006712608,0.0052390224,0.2950712],"study_design_scores_gemma":[0.00017656165,0.007072178,0.5386029,0.000044157674,0.0002832387,0.00052980264,0.003473096,0.4000326,0.04605989,0.0005550396,0.0030587276,0.00011178824],"about_ca_topic_score_codex":0.012950652,"about_ca_topic_score_gemma":0.016554993,"teacher_disagreement_score":0.012950652,"about_ca_system_score_codex":0.00035471303,"about_ca_system_score_gemma":0.000294843,"threshold_uncertainty_score":0.025750577},"labels":[],"label_agreement":null},{"id":"W2293582111","doi":"10.1109/icip.2015.7351412","title":"Traffic analysis without motion features","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Traffic analysis; Artificial intelligence; Perspective (graphical); Motion (physics); Process (computing); Frame (networking); Computer vision; Motion analysis; Machine learning; Data mining","score_opus":0.043844143649790814,"score_gpt":0.31735140199916173,"score_spread":0.2735072583493709,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293582111","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":[0.33502474,0.0008155041,0.6513484,0.00027593595,0.00018261906,0.00012004901,0.0019464936,0.0035242178,0.0067620142],"genre_scores_gemma":[0.8671854,0.0004658931,0.12699059,0.00008253633,0.00014661669,0.00006189201,0.0023029842,0.0001679496,0.0025960573],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99966586,0.00004247011,0.000017089318,0.00009863359,0.00009954256,0.00007655819],"domain_scores_gemma":[0.9994758,0.00012164469,0.000096426935,0.00008249138,0.00018954904,0.0000341953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028476518,0.00068620674,0.0006290643,0.0031126207,0.000277382,0.00084657705,0.0005741059,0.0004791999,0.0015368558],"category_scores_gemma":[0.0015078189,0.00022458185,0.00053385796,0.0014767704,0.00023593956,0.001262627,0.00043524618,0.00041961911,0.00085269223],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005343754,0.00029446883,0.029983006,0.00032225376,0.00021803303,0.00047768533,0.00014045624,0.19014058,0.11431209,0.005808769,0.006298009,0.65147024],"study_design_scores_gemma":[0.000007995667,0.000095535346,0.0111506265,0.000019784107,0.000038900806,0.00032166555,0.00007940078,0.9647879,0.017858902,0.0018957988,0.0037199287,0.000023537985],"about_ca_topic_score_codex":0.0039724847,"about_ca_topic_score_gemma":0.0035728798,"teacher_disagreement_score":0.0039724847,"about_ca_system_score_codex":0.00036843316,"about_ca_system_score_gemma":0.00037201017,"threshold_uncertainty_score":0.007898688},"labels":[],"label_agreement":null},{"id":"W2293693209","doi":"10.1007/978-3-319-27857-5_25","title":"UT-MARO: Unscented Transformation and Matrix Rank Optimization for Moving Objects Detection in Aerial Imagery","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Aerial image; Robustness (evolution); Artificial intelligence; Computer vision; Computation; Object detection; Aerial imagery; Transformation (genetics); Transformation matrix; Pattern recognition (psychology); Image (mathematics); Algorithm","score_opus":0.022586632583867203,"score_gpt":0.2855284726706321,"score_spread":0.26294184008676486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293693209","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":[0.0010418872,0.0003049362,0.99591005,0.000066842455,0.00008966356,0.000024560437,0.00014207694,0.001444211,0.0009757856],"genre_scores_gemma":[0.021592114,0.00042248567,0.9697971,0.00009515655,0.000121080535,0.00012822873,0.0008762688,0.00076438236,0.006203238],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937856,0.00013654072,0.000029385983,0.000118739794,0.00028746476,0.000049277587],"domain_scores_gemma":[0.9995111,0.0001786282,0.00003588257,0.00010544302,0.00014279954,0.000026256657],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006680695,0.0014443933,0.0013509503,0.000675386,0.00034384045,0.0010287537,0.0014238104,0.0011215081,0.0077113025],"category_scores_gemma":[0.002370687,0.0006089958,0.0010330294,0.0011059875,0.0006086288,0.0013035062,0.001537975,0.0018485722,0.004462879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019809985,0.000089811816,0.00019231265,0.00031400242,0.000081712766,0.00007880168,0.000082193816,0.09179164,0.019207489,0.021227151,0.041363493,0.82537335],"study_design_scores_gemma":[0.000016805496,0.00006075352,0.00021412892,0.00002221285,0.00001023427,0.000086099826,0.000016176351,0.96780753,0.006665019,0.011388136,0.013692168,0.000020858804],"about_ca_topic_score_codex":0.002823135,"about_ca_topic_score_gemma":0.004457789,"teacher_disagreement_score":0.0077113025,"about_ca_system_score_codex":0.0003359687,"about_ca_system_score_gemma":0.00082411023,"threshold_uncertainty_score":0.02579683},"labels":[],"label_agreement":null},{"id":"W2293830259","doi":"10.1109/crv.2013.47","title":"Eigenbackground Bootstrapping","year":2013,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Exploit; Background subtraction; Bootstrapping (finance); Initialization; Artificial intelligence; Set (abstract data type); Frame (networking); Machine learning; Data set; Pixel; Data mining; Training set; Pattern recognition (psychology); Mathematics; Econometrics","score_opus":0.0205162315347739,"score_gpt":0.25176935703525394,"score_spread":0.23125312550048005,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2293830259","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":[0.010176723,0.00008849439,0.9849844,0.00003808681,0.00004488284,0.000051468982,0.0000937424,0.0038685615,0.0006536405],"genre_scores_gemma":[0.25561228,0.00011881787,0.73708546,0.00021416282,0.0000707419,0.00021717056,0.0014826371,0.0010261269,0.0041725766],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99905974,0.00022575559,0.00004230446,0.00026981684,0.00028422556,0.00011811258],"domain_scores_gemma":[0.99822026,0.0006631331,0.00013597676,0.00044690806,0.00045811178,0.0000754756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001226466,0.0014675432,0.0012124509,0.0017704125,0.00077361043,0.0011352694,0.0021124743,0.001463802,0.0059174947],"category_scores_gemma":[0.0045652776,0.0007481524,0.0011853019,0.0010094148,0.0008413173,0.0020182992,0.0017266687,0.0016819407,0.0033171582],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035226467,0.00025921955,0.0019591642,0.00013788568,0.00012332112,0.00025553242,0.0001749732,0.11017858,0.043294538,0.0068316725,0.009790871,0.826642],"study_design_scores_gemma":[0.00001835583,0.000053638712,0.0007207034,0.000014347573,0.00001524862,0.00012117587,0.000031583677,0.97532994,0.015426212,0.005373807,0.002871227,0.000023722152],"about_ca_topic_score_codex":0.0025917336,"about_ca_topic_score_gemma":0.0049515627,"teacher_disagreement_score":0.0059174947,"about_ca_system_score_codex":0.00041975093,"about_ca_system_score_gemma":0.0007413081,"threshold_uncertainty_score":0.019795954},"labels":[],"label_agreement":null},{"id":"W2294399075","doi":"10.1109/tip.2012.2199326","title":"Behavior Subtraction","year":2012,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Computer vision; Spurious relationship; Anomaly detection; Object detection; Pattern recognition (psychology); Pixel; Machine learning","score_opus":0.03575649701534747,"score_gpt":0.3246583672616879,"score_spread":0.2889018702463404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294399075","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030729001,0.0015108149,0.7141,0.00083092635,0.0017995329,0.00038229642,0.010067859,0.047248874,0.19333068],"genre_scores_gemma":[0.3135983,0.0019258931,0.45485994,0.0021029518,0.0003658101,0.00046736738,0.04093242,0.009408328,0.17633897],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929595,0.00003459067,0.00002506439,0.00026836531,0.0002671203,0.0001089963],"domain_scores_gemma":[0.9995396,0.000028519002,0.00002465446,0.00010753211,0.00026029188,0.000039460436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030915224,0.0017253507,0.00082851836,0.0016059338,0.0007211895,0.0019484913,0.0018304783,0.0010953784,0.047964003],"category_scores_gemma":[0.0011555078,0.00055228046,0.00095185154,0.0011240639,0.0003012708,0.0014312248,0.0016981442,0.0010994714,0.04030685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046083145,0.0002119482,0.004476679,0.00044236888,0.00013678898,0.00039829657,0.0002390673,0.010735647,0.080577806,0.01679917,0.11472958,0.77079177],"study_design_scores_gemma":[0.00006478888,0.00023073984,0.01231708,0.00015530651,0.0001939514,0.0013754423,0.0006268034,0.23190245,0.12833281,0.022512952,0.60211504,0.00017261092],"about_ca_topic_score_codex":0.0052294396,"about_ca_topic_score_gemma":0.007647567,"teacher_disagreement_score":0.047964003,"about_ca_system_score_codex":0.0008164063,"about_ca_system_score_gemma":0.0010807732,"threshold_uncertainty_score":0.16045558},"labels":[],"label_agreement":null},{"id":"W2294411327","doi":"10.1109/icip.2015.7351560","title":"Multiple object tracking based on sparse generative appearance modeling","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Computer science; Active appearance model; Computer vision; Object (grammar); Video tracking; Focus (optics); Generative model; Tracking (education); Similarity (geometry); Feature (linguistics); Pattern recognition (psychology); Generative grammar; Image (mathematics)","score_opus":0.12678690582185972,"score_gpt":0.3195410560460898,"score_spread":0.19275415022423006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294411327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055079344,0.00010959383,0.99343383,0.00003935776,0.000020008489,0.000012670072,0.00001979359,0.000480771,0.00037611334],"genre_scores_gemma":[0.45321968,0.0005209809,0.5417379,0.00018914693,0.00010065301,0.000084701125,0.00047176355,0.00029590505,0.0033791808],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923706,0.00012430866,0.000026827185,0.00023373317,0.00031126922,0.00006689494],"domain_scores_gemma":[0.998836,0.00048602716,0.0001667231,0.00022996053,0.00021819533,0.00006321967],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010207074,0.0007280112,0.0013276973,0.0014487194,0.00037656588,0.00096454803,0.0017886193,0.00094349467,0.00091591483],"category_scores_gemma":[0.0027476065,0.000640682,0.0014387333,0.0016604459,0.00056401605,0.001567652,0.001188387,0.0014986006,0.00071688415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015352282,0.00011494831,0.0024369787,0.00011007395,0.00015962905,0.00025658234,0.0001878237,0.5220633,0.027535563,0.013418438,0.0023769774,0.43118614],"study_design_scores_gemma":[0.0000030141514,0.000011675437,0.00016118935,0.0000028688967,0.0000085433985,0.00005592917,0.0000035505798,0.9962063,0.001626963,0.0015677762,0.00034670017,0.0000054291004],"about_ca_topic_score_codex":0.0049322834,"about_ca_topic_score_gemma":0.0047957473,"teacher_disagreement_score":0.0049322834,"about_ca_system_score_codex":0.0007071792,"about_ca_system_score_gemma":0.00058210193,"threshold_uncertainty_score":0.009807169},"labels":[],"label_agreement":null},{"id":"W2294551309","doi":"10.1109/tim.2016.2514780","title":"Extending the Detection Range of Vision-Based Vehicular Instrumentation","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Track (disk drive); Range (aeronautics); Pedestrian detection; Tracking (education); Lens (geology); Pedestrian; Real-time computing; Engineering","score_opus":0.038822227394304125,"score_gpt":0.28652216713874745,"score_spread":0.24769993974444332,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2294551309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06287129,0.0023880897,0.9274336,0.00013545678,0.00018003421,0.00006843032,0.00005177978,0.002399027,0.0044723786],"genre_scores_gemma":[0.72605014,0.00090154255,0.2695473,0.0003936809,0.00018189538,0.0001172082,0.00020158498,0.00011692863,0.0024896674],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99893373,0.00014134523,0.00004435608,0.00034981812,0.0004048929,0.0001259123],"domain_scores_gemma":[0.99884087,0.00043333488,0.00012353378,0.00016936962,0.00036641172,0.00006654177],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00077466393,0.0006743929,0.0007689939,0.0011300299,0.0003662563,0.0006377696,0.0013943473,0.0011554781,0.0011287035],"category_scores_gemma":[0.0020949706,0.00047860784,0.00043146982,0.00048329044,0.00046881827,0.0015126877,0.0015289859,0.0007756308,0.0010940462],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004936368,0.00018063013,0.005053079,0.0005158788,0.00007152442,0.0004143823,0.0003327233,0.014192871,0.46043536,0.004962584,0.0016848168,0.5116624],"study_design_scores_gemma":[0.000108058775,0.0018858415,0.011430163,0.00016387795,0.00023196425,0.0038485497,0.0001890756,0.42618576,0.5007319,0.0072461218,0.047720846,0.000257891],"about_ca_topic_score_codex":0.00077511393,"about_ca_topic_score_gemma":0.000630235,"teacher_disagreement_score":0.0013943473,"about_ca_system_score_codex":0.00042995502,"about_ca_system_score_gemma":0.00040687592,"threshold_uncertainty_score":0.0040968657},"labels":[],"label_agreement":null},{"id":"W2295374856","doi":"10.1109/icip.2015.7350978","title":"Object tracking with adaptive motion modeling of particle filter and support vector machines","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Particle filter; Support vector machine; Video tracking; BitTorrent tracker; Kernel (algebra); Noise (video); Tracking (education); Filter (signal processing); Motion vector; Motion (physics); Pattern recognition (psychology); Eye tracking; Object (grammar); Mathematics; Image (mathematics)","score_opus":0.08817435171437756,"score_gpt":0.29818810485789754,"score_spread":0.21001375314352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295374856","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":[0.002563311,0.00013114668,0.9968375,0.000027314174,0.000024315263,0.000007979456,0.000009175587,0.00017835811,0.00022083557],"genre_scores_gemma":[0.39585003,0.00074121467,0.5994009,0.0001301759,0.00014313616,0.00015008352,0.00021733769,0.000107346335,0.003259751],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994815,0.00010170572,0.000031006835,0.00014752342,0.00019643236,0.00004186784],"domain_scores_gemma":[0.99946135,0.00021705391,0.000083955754,0.00008089639,0.00013165135,0.000024998779],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008722005,0.0007382161,0.0010824611,0.00089256844,0.00030683927,0.0007358414,0.0015674251,0.0012098415,0.00071291364],"category_scores_gemma":[0.0022585897,0.0005463616,0.000950841,0.001001762,0.0004581179,0.0015802702,0.0007370394,0.0012704827,0.0003808861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000094469324,0.00006775865,0.0012370491,0.00007817613,0.0001024495,0.00009536102,0.000080984115,0.7451876,0.0076003284,0.018301072,0.0013098462,0.22584492],"study_design_scores_gemma":[0.0000022973088,0.000007649499,0.00007460859,0.000001680949,0.0000032049472,0.000009798443,0.0000011906989,0.9978662,0.00046344378,0.0011934597,0.00037332857,0.0000031136792],"about_ca_topic_score_codex":0.0055184574,"about_ca_topic_score_gemma":0.0033831755,"teacher_disagreement_score":0.0055184574,"about_ca_system_score_codex":0.0005738496,"about_ca_system_score_gemma":0.00068680116,"threshold_uncertainty_score":0.010972679},"labels":[],"label_agreement":null},{"id":"W2295905810","doi":"10.1007/978-3-319-20801-5_47","title":"Vehicle Detection Using Approximation of Feature Pyramids in the DFT Domain","year":2015,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Domain (mathematical analysis); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Algorithm; Mathematics","score_opus":0.042452697598906845,"score_gpt":0.29633818103801535,"score_spread":0.2538854834391085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2295905810","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":[0.014674579,0.000112137415,0.9838185,0.000041068513,0.000023255461,0.000011532464,0.0000647923,0.00028835973,0.0009659445],"genre_scores_gemma":[0.34685448,0.00060182484,0.6471474,0.000064746826,0.000052991174,0.000028915483,0.00052133464,0.00010846413,0.004619719],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9998586,0.000016126141,0.000005780446,0.000027976746,0.000066903056,0.00002470046],"domain_scores_gemma":[0.99977165,0.00008082101,0.000019693896,0.00004074689,0.00007215699,0.000015050106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020356434,0.00032870396,0.00048262058,0.0006665028,0.00012705284,0.00065475306,0.00050236954,0.0004191767,0.001776531],"category_scores_gemma":[0.0009853143,0.00024983994,0.0005024912,0.00087370357,0.0002489677,0.0006657556,0.00042260718,0.00049290276,0.000865988],"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":[0.00026725206,0.00008206292,0.0013828534,0.0001578328,0.00006139123,0.00016530784,0.00007838299,0.13531214,0.11340222,0.018109957,0.0032732638,0.72770745],"study_design_scores_gemma":[0.000005304818,0.000038791433,0.00078739226,0.000006131272,0.000010953173,0.00017038632,0.00001326185,0.9851458,0.009092572,0.0033613013,0.0013600377,0.000008071481],"about_ca_topic_score_codex":0.003346251,"about_ca_topic_score_gemma":0.002915122,"teacher_disagreement_score":0.003346251,"about_ca_system_score_codex":0.00030887945,"about_ca_system_score_gemma":0.00038368296,"threshold_uncertainty_score":0.0066535473},"labels":[],"label_agreement":null},{"id":"W2296354816","doi":"10.1109/icip.2015.7351270","title":"Moving object detection from moving platforms using Lagrange multiplier","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Lagrange multiplier; Computer science; Multiplier (economics); Computer vision; Object detection; Artificial intelligence; Object (grammar); Mathematics; Mathematical optimization; Pattern recognition (psychology)","score_opus":0.07702283558980615,"score_gpt":0.3042574303787726,"score_spread":0.22723459478896646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296354816","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0071351356,0.00010979506,0.99206066,0.00006358009,0.000022995406,0.00002896785,0.000018993016,0.00014798006,0.00041191315],"genre_scores_gemma":[0.11987216,0.00036536163,0.8763238,0.00006971366,0.000064193235,0.00015061842,0.00022245443,0.00007439454,0.0028573268],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952185,0.0001287265,0.000022700677,0.00011592287,0.00016750846,0.000043226304],"domain_scores_gemma":[0.99947315,0.00021290858,0.00009225366,0.00004803378,0.00014538835,0.000028313267],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008640215,0.0010023954,0.0010081087,0.0011926754,0.00040361003,0.0008739458,0.00097028527,0.00094811304,0.0015309623],"category_scores_gemma":[0.0023381861,0.00056226196,0.0007294689,0.0010307948,0.0005151427,0.001096483,0.0011040027,0.0012682346,0.00065253815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027434347,0.00010277749,0.0016695908,0.00026422355,0.00014251386,0.00034665762,0.00017113442,0.40775907,0.047192287,0.018999314,0.0050367853,0.5180413],"study_design_scores_gemma":[0.000006225271,0.000024216692,0.00021317793,0.0000066785497,0.0000049512914,0.000053621785,0.000007826378,0.99378633,0.003380686,0.0016481229,0.00085973286,0.000008522361],"about_ca_topic_score_codex":0.0021116743,"about_ca_topic_score_gemma":0.0019705803,"teacher_disagreement_score":0.0021116743,"about_ca_system_score_codex":0.0005197461,"about_ca_system_score_gemma":0.00094584114,"threshold_uncertainty_score":0.0051216483},"labels":[],"label_agreement":null},{"id":"W2296388617","doi":"10.1109/crv.2016.36","title":"Modular Decomposition and Analysis of Registration Based Trackers","year":2016,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"BitTorrent tracker; Computer science; Modular design; Tracking (education); Decomposition; Interface (matter); Component (thermodynamics); Artificial intelligence; Operating system; Eye tracking","score_opus":0.027796540621131478,"score_gpt":0.32769837487496084,"score_spread":0.2999018342538294,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296388617","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00600106,0.00008300196,0.992523,0.000043272907,0.000022778897,0.000029758765,0.000032896718,0.00029462753,0.0009695222],"genre_scores_gemma":[0.36871943,0.0005788179,0.6205915,0.00013842176,0.00024361731,0.00028433846,0.00058336515,0.00097467186,0.007885873],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974668,0.00065164245,0.00015323842,0.0006108091,0.0008962999,0.00022120183],"domain_scores_gemma":[0.9948089,0.0015064696,0.0009031343,0.0011327442,0.001225319,0.00042339956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004017402,0.0012980452,0.0011616095,0.0032431413,0.00058748754,0.001502993,0.0016139245,0.0012642021,0.004612724],"category_scores_gemma":[0.011124284,0.0009487614,0.0024311661,0.0013567926,0.0017081258,0.0021245445,0.0029798155,0.001529099,0.0021454033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033800607,0.0001191304,0.005131906,0.00041123747,0.00028374733,0.00040154182,0.00062830193,0.35024595,0.05336258,0.3041784,0.0043300977,0.28056908],"study_design_scores_gemma":[0.000013115529,0.000080124824,0.0011874352,0.000020523155,0.0000416494,0.0001439113,0.00002308297,0.9418504,0.0040422627,0.04944877,0.0031244634,0.00002413804],"about_ca_topic_score_codex":0.0012387554,"about_ca_topic_score_gemma":0.00087912154,"teacher_disagreement_score":0.004612724,"about_ca_system_score_codex":0.0011515141,"about_ca_system_score_gemma":0.0007758537,"threshold_uncertainty_score":0.021246254},"labels":[],"label_agreement":null},{"id":"W2296924361","doi":"10.1049/iet-cvi.2015.0210","title":"Online learning of mixture experts for real‐time tracking","year":2016,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Specialized Research Fund for the Doctoral Program of Higher Education of China; Basic Research Programs of Sichuan Province; National Natural Science Foundation of China","keywords":"Computer science; Hyperplane; Artificial intelligence; Tracking (education); Image warping; Gradient descent; Video tracking; Computer vision; Set (abstract data type); Dynamic time warping; Object (grammar); Pattern recognition (psychology); Mathematics; Artificial neural network","score_opus":0.022068068834633588,"score_gpt":0.32040743662902327,"score_spread":0.2983393677943897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2296924361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005800484,0.00026976544,0.9930101,0.00005687999,0.000025065021,0.000015536622,0.000020054367,0.000407824,0.0003942565],"genre_scores_gemma":[0.56090873,0.0005824825,0.4311021,0.0003036248,0.0001446101,0.00016510565,0.00040418594,0.00022168145,0.0061674924],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985775,0.00047040725,0.000061329236,0.00041879405,0.00027731352,0.00019471183],"domain_scores_gemma":[0.99738187,0.0015731007,0.0002264822,0.00024262287,0.00046194968,0.000113998234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029325348,0.0012724919,0.00197598,0.0011199302,0.0005600255,0.001018579,0.0020508948,0.0021934998,0.0024161935],"category_scores_gemma":[0.0065791113,0.0010276317,0.0017443972,0.0011050717,0.00085119327,0.0020788615,0.0016950268,0.0025768015,0.0014236729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038948024,0.00013477186,0.0012692276,0.00012415943,0.00019349175,0.00009615851,0.00017845877,0.71388316,0.00524551,0.009704582,0.0028389562,0.26594207],"study_design_scores_gemma":[0.0000043248715,0.00001602418,0.00008083535,0.0000026520593,0.0000069150874,0.000013774897,0.0000037929842,0.99778193,0.00048361495,0.0014026404,0.00019790059,0.000005579267],"about_ca_topic_score_codex":0.005356913,"about_ca_topic_score_gemma":0.0044718795,"teacher_disagreement_score":0.005356913,"about_ca_system_score_codex":0.0007980053,"about_ca_system_score_gemma":0.0008579608,"threshold_uncertainty_score":0.01550889},"labels":[],"label_agreement":null},{"id":"W2312242712","doi":"10.3166/ts.27.297-324","title":"Réidentification des personnes à travers un réseau de caméras","year":2010,"lang":"fr","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Identification (biology); Biology","score_opus":0.03910677478693544,"score_gpt":0.2878595975210142,"score_spread":0.24875282273407875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2312242712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.111567535,0.000980488,0.8727199,0.0002790674,0.00021593182,0.00020772258,0.00088047056,0.006989159,0.006159714],"genre_scores_gemma":[0.3242333,0.0012508404,0.6554148,0.00019422222,0.00009488402,0.0002030898,0.0018938592,0.00062039745,0.016094612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99834573,0.00021037525,0.000068319234,0.0008107529,0.00043965928,0.00012518668],"domain_scores_gemma":[0.99772006,0.00073631364,0.00023002169,0.0007913676,0.0004594265,0.00006280454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010688489,0.0010638164,0.0013830853,0.0026653886,0.00079826236,0.001851707,0.0015458504,0.001489882,0.0045912107],"category_scores_gemma":[0.0053537833,0.0006548189,0.001104607,0.0017952987,0.00056353083,0.0021026125,0.001285257,0.0008433263,0.003134499],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003737016,0.000121845966,0.007422973,0.0003159486,0.00020424479,0.00034903333,0.0006401059,0.017953293,0.080560975,0.0023326054,0.0036445016,0.88608086],"study_design_scores_gemma":[0.000076248514,0.00074656884,0.07588347,0.0001956569,0.00048387464,0.0037622487,0.0015538454,0.43278039,0.41002107,0.009566389,0.064596765,0.00033346858],"about_ca_topic_score_codex":0.009100216,"about_ca_topic_score_gemma":0.014675379,"teacher_disagreement_score":0.009100216,"about_ca_system_score_codex":0.0007615569,"about_ca_system_score_gemma":0.0006780722,"threshold_uncertainty_score":0.01809454},"labels":[],"label_agreement":null},{"id":"W2322052938","doi":"10.7210/jrsj.25.707","title":"Person Following System for the Autonomous Mobile Robot by Independent Tracking of Left and Right Feet","year":2007,"lang":"en","type":"article","venue":"Journal of the Robotics Society of Japan","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"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":"Canadian Institute for Advanced Research","keywords":"Computer vision; Artificial intelligence; Tracking (education); Mobile robot; Track (disk drive); Robot; Computer science; Position (finance); Feature (linguistics); Left and right; Cart; Engineering; Psychology","score_opus":0.020112686939502317,"score_gpt":0.2741679582376644,"score_spread":0.2540552712981621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2322052938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.093299344,0.0002713567,0.900476,0.00010772146,0.000108662665,0.000075981705,0.000087113374,0.0035131911,0.0020606949],"genre_scores_gemma":[0.59473425,0.00021341453,0.3952003,0.00016206453,0.00007983911,0.00014810663,0.00030347772,0.000057345544,0.0091012595],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998951,0.000010084212,0.0000047685576,0.000036793972,0.00004001527,0.000013232017],"domain_scores_gemma":[0.99982786,0.000021065209,0.00002283665,0.00003194733,0.00007311095,0.000023135255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017926951,0.00028188864,0.00042664018,0.0002660892,0.0004117309,0.00021569015,0.00074264756,0.00046861963,0.0014619513],"category_scores_gemma":[0.00025448858,0.00021153575,0.0002747466,0.00018793966,0.00016920459,0.00035545844,0.00034884762,0.00031545883,0.0005871911],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038289814,0.00017447895,0.0051698256,0.0001640401,0.00009291692,0.00033126806,0.000289718,0.008349025,0.3089935,0.0020591305,0.006842388,0.6671508],"study_design_scores_gemma":[0.0002143679,0.0014009819,0.018821554,0.000046041576,0.00023497008,0.0024688458,0.00017858166,0.80889386,0.14246272,0.0016222856,0.023533618,0.00012216574],"about_ca_topic_score_codex":0.002080219,"about_ca_topic_score_gemma":0.0030823746,"teacher_disagreement_score":0.002080219,"about_ca_system_score_codex":0.00019318885,"about_ca_system_score_gemma":0.00047254804,"threshold_uncertainty_score":0.00489074},"labels":[],"label_agreement":null},{"id":"W2339957118","doi":"10.1109/tits.2016.2545245","title":"Tracking All Road Users at Multimodal Urban Traffic Intersections","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada; Polytechnique Montréal","keywords":"Computer science; Background subtraction; Intersection (aeronautics); Computer vision; Artificial intelligence; Collision; Transport engineering; Engineering; Computer security","score_opus":0.04409927078331705,"score_gpt":0.2915235503387356,"score_spread":0.24742427955541857,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2339957118","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":[0.86689276,0.00030639133,0.12812781,0.0000647128,0.000023963066,0.00010282172,0.00045178656,0.001460442,0.0025694158],"genre_scores_gemma":[0.9665668,0.00011282045,0.031799886,0.000014930148,0.0000069160683,0.000022172782,0.0003744315,0.000031694537,0.0010703735],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997098,0.000038793587,0.000009336352,0.00010544396,0.000067323235,0.00006931353],"domain_scores_gemma":[0.99972314,0.0000476429,0.00004658899,0.00003619697,0.00010775586,0.00003861582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033239793,0.00047145115,0.00048421635,0.001176534,0.00047961692,0.00051453244,0.0006651633,0.0005035256,0.00070261554],"category_scores_gemma":[0.0008784479,0.00019665272,0.00024011047,0.0006551582,0.00017037868,0.00044499268,0.00066013786,0.0003805096,0.00039968488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010881991,0.0004092044,0.20667107,0.00020556967,0.0002606466,0.0014789186,0.0015734452,0.08017092,0.0893105,0.002245916,0.004519299,0.61206627],"study_design_scores_gemma":[0.000025309122,0.00038550736,0.113157354,0.000033744516,0.00015757782,0.0009905903,0.001034707,0.8241333,0.053938348,0.00088247383,0.0051924735,0.00006858462],"about_ca_topic_score_codex":0.00898672,"about_ca_topic_score_gemma":0.019123724,"teacher_disagreement_score":0.00898672,"about_ca_system_score_codex":0.0003406891,"about_ca_system_score_gemma":0.00046888445,"threshold_uncertainty_score":0.017868817},"labels":[],"label_agreement":null},{"id":"W2343184955","doi":"10.1109/tits.2016.2545640","title":"Real-Time Vehicle Make and Model Recognition Based on a Bag of SURF Features","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":67,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Support vector machine; Computer science; Artificial intelligence; Pattern recognition (psychology); Histogram; Modular design; Histogram of oriented gradients; Set (abstract data type); Face (sociological concept); Multiclass classification; Machine learning; Image (mathematics)","score_opus":0.0337366364682894,"score_gpt":0.27259871306128264,"score_spread":0.23886207659299324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2343184955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08391143,0.00038953155,0.9070526,0.00009623981,0.00012777906,0.000086674205,0.00029043565,0.0063149086,0.001730393],"genre_scores_gemma":[0.6580803,0.00032628182,0.33665416,0.00008792096,0.00007169416,0.000083058396,0.001212609,0.00020336341,0.0032806108],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951005,0.000044846107,0.000016173339,0.00013626183,0.00022271016,0.00006993412],"domain_scores_gemma":[0.99946684,0.000082427425,0.000105667656,0.0001693919,0.0001470221,0.000028677066],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000443564,0.00085503457,0.0009454744,0.0013167259,0.00027589343,0.00065492315,0.0010900851,0.0006125304,0.0013532701],"category_scores_gemma":[0.0011790931,0.00030303377,0.00052927714,0.0007954941,0.00028061896,0.0014382723,0.00082297076,0.00068819406,0.0015871171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036761566,0.00019831464,0.005275948,0.00011367428,0.00009895493,0.00015074686,0.000070539645,0.055399593,0.06025789,0.0014574628,0.0037862333,0.87282306],"study_design_scores_gemma":[0.00001568401,0.00021955307,0.005564764,0.000014565429,0.0000379689,0.0003857634,0.0000736045,0.94018596,0.048368923,0.0012696504,0.0038220214,0.000041642026],"about_ca_topic_score_codex":0.0017694059,"about_ca_topic_score_gemma":0.0024594257,"teacher_disagreement_score":0.0017694059,"about_ca_system_score_codex":0.00024758317,"about_ca_system_score_gemma":0.00033517784,"threshold_uncertainty_score":0.004527092},"labels":[],"label_agreement":null},{"id":"W2344301817","doi":"10.1109/tfuzz.2015.2513091","title":"Online Feature Selection Based on Fuzzy Clustering and Its Applications","year":2015,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Cluster analysis; Computer science; Robustness (evolution); Feature selection; Artificial intelligence; Data mining; Fuzzy logic; Fuzzy clustering; Fuzzy set; Pattern recognition (psychology); Machine learning; Feature (linguistics)","score_opus":0.04246757106237923,"score_gpt":0.29401075328355913,"score_spread":0.2515431822211799,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2344301817","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":[0.013231361,0.00043437287,0.98499274,0.00011774368,0.000036660746,0.0000320086,0.000031693424,0.00031861878,0.00080474716],"genre_scores_gemma":[0.5502911,0.0004907769,0.4466974,0.000116592644,0.00011547246,0.0001505611,0.00015574925,0.00008264967,0.0018997048],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994467,0.000120137345,0.000040920542,0.00014678239,0.00020200852,0.000043422486],"domain_scores_gemma":[0.9990783,0.00043868308,0.00007115644,0.0000690754,0.0003077832,0.00003506024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00092466874,0.000618104,0.001207015,0.001287647,0.00062308065,0.0006685612,0.0009302806,0.0008875424,0.0013383306],"category_scores_gemma":[0.0027959398,0.00029904186,0.00081799785,0.0012499087,0.00044049288,0.0006630945,0.0005764032,0.0005136337,0.00028626175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002103666,0.00011116296,0.0011316086,0.00013729803,0.000085233456,0.00023955424,0.000120372,0.53214234,0.011600584,0.010622943,0.0028782745,0.44072035],"study_design_scores_gemma":[0.0000039928063,0.000014283799,0.00015957153,0.0000029435748,0.0000059846425,0.000032177035,0.0000041239273,0.9969965,0.00095150253,0.0015122675,0.0003092525,0.0000074160985],"about_ca_topic_score_codex":0.0056738453,"about_ca_topic_score_gemma":0.002861402,"teacher_disagreement_score":0.0056738453,"about_ca_system_score_codex":0.0006294897,"about_ca_system_score_gemma":0.00063449,"threshold_uncertainty_score":0.011281669},"labels":[],"label_agreement":null},{"id":"W2355957488","doi":"","title":"Optical Flow Analysis for Infrared Pedestrian Monitoring in Low View","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Optical flow; Computer vision; Compensation (psychology); Artificial intelligence; Infrared; Tracking (education); Pedestrian; Object detection; Pedestrian detection; Flow (mathematics); Contrast (vision); Object (grammar); Remote sensing; Optics; Pattern recognition (psychology); Image (mathematics); Physics; Geology","score_opus":0.03702932667007689,"score_gpt":0.30638534560312647,"score_spread":0.26935601893304956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2355957488","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13122368,0.00077106745,0.864497,0.00011769148,0.00007519285,0.00008579223,0.000103768114,0.0007311325,0.002394627],"genre_scores_gemma":[0.7029574,0.00081620057,0.29308102,0.000068216694,0.000082813436,0.00007849589,0.00020639361,0.00007017522,0.0026393423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997825,0.000047352012,0.000007874875,0.000035436722,0.000103800114,0.00002304054],"domain_scores_gemma":[0.99966156,0.00009560155,0.000051653646,0.000019214467,0.00014650708,0.00002551375],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051539857,0.00039568573,0.00032262164,0.0020523523,0.0002992923,0.0003658243,0.00026776298,0.0002843105,0.0013830878],"category_scores_gemma":[0.0010286523,0.00021203773,0.00032484686,0.00078638084,0.0002154774,0.0007202338,0.00021958814,0.00027669923,0.00031889032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007162786,0.00026295328,0.012413189,0.0002715891,0.00005721604,0.00024285099,0.0002577192,0.033777583,0.21806367,0.004117636,0.0031112302,0.726708],"study_design_scores_gemma":[0.00005244409,0.00032597673,0.02442706,0.000049442795,0.000088637644,0.000535698,0.00010286256,0.8732656,0.09309854,0.0025934563,0.005385505,0.00007472],"about_ca_topic_score_codex":0.003032978,"about_ca_topic_score_gemma":0.0016450093,"teacher_disagreement_score":0.003032978,"about_ca_system_score_codex":0.00042174893,"about_ca_system_score_gemma":0.00041571938,"threshold_uncertainty_score":0.0060306787},"labels":[],"label_agreement":null},{"id":"W2362993038","doi":"","title":"Using USB Camera On Embedded Linux Platform","year":2006,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"USB; Computer science; Computer hardware; Embedded system; Operating system; Host controller interface; Computer graphics (images); USB hub; Liquid-crystal display; Software","score_opus":0.041396497836397456,"score_gpt":0.31492038022612645,"score_spread":0.273523882389729,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2362993038","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074912935,0.0011078131,0.6931801,0.0007229206,0.00062333985,0.0007880907,0.0028181192,0.16809677,0.057749923],"genre_scores_gemma":[0.28261673,0.0010273755,0.5975221,0.0004257774,0.00018619881,0.000501478,0.0064956406,0.009589631,0.10163515],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993104,0.0000481388,0.00003633503,0.0002023554,0.000322465,0.0000803644],"domain_scores_gemma":[0.9989145,0.00007489789,0.000066288245,0.00023477722,0.00064042275,0.00006906665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043166772,0.0009191087,0.0006585361,0.0013499799,0.00055530074,0.00087924296,0.00083629513,0.0003900675,0.03554138],"category_scores_gemma":[0.0014387298,0.00045882715,0.00032547925,0.00084584043,0.00012861358,0.0013983232,0.00076532474,0.00048564744,0.014106271],"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":[0.0006560987,0.00017193877,0.0045679165,0.00052958145,0.000081457736,0.00036558983,0.0003530585,0.0015943077,0.3476393,0.004277569,0.08883149,0.55093163],"study_design_scores_gemma":[0.0002620887,0.00049000885,0.020112194,0.00025262096,0.00020742652,0.0022429365,0.00022560751,0.054784607,0.56702185,0.003064107,0.35109445,0.00024214727],"about_ca_topic_score_codex":0.0020892504,"about_ca_topic_score_gemma":0.0024458345,"teacher_disagreement_score":0.03554138,"about_ca_system_score_codex":0.0004033952,"about_ca_system_score_gemma":0.00050314347,"threshold_uncertainty_score":0.118897796},"labels":[],"label_agreement":null},{"id":"W2372842639","doi":"","title":"Analysis on 3D Human Body based on Multi-camera","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Voxel; Computer science; Skeleton (computer programming); Computer vision; Artificial intelligence; Human skeleton; Tracking (education); Point (geometry); Human body; Field (mathematics); Mathematics","score_opus":0.023754536339388045,"score_gpt":0.33071411834910786,"score_spread":0.3069595820097198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2372842639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011006081,0.00023621961,0.9878319,0.000038540653,0.000027112495,0.000013020398,0.000023457287,0.00017884352,0.0006448073],"genre_scores_gemma":[0.38966757,0.0011137723,0.6055196,0.0000779336,0.00007178114,0.000061391926,0.00018978046,0.00011495432,0.0031832575],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996687,0.000048516038,0.000014492744,0.00009294363,0.00014863869,0.000026698623],"domain_scores_gemma":[0.99980754,0.00004074416,0.000023240353,0.000026900725,0.00008752443,0.000014171091],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002540653,0.0004888363,0.0005348644,0.001366574,0.0003002998,0.000415015,0.00043774067,0.0004490014,0.0017007572],"category_scores_gemma":[0.0006152367,0.00033286947,0.000865216,0.0010736607,0.00043975384,0.001053061,0.00048190207,0.00033729378,0.0004561365],"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":[0.00020808546,0.000049154627,0.0054385713,0.0003801664,0.00022744459,0.0005083968,0.00055748835,0.2292699,0.1751671,0.02348635,0.0027551826,0.56195223],"study_design_scores_gemma":[0.000006772952,0.000054353965,0.0031612443,0.000017673095,0.000051679715,0.00030441018,0.000055144774,0.97375745,0.015805777,0.003956497,0.0027929095,0.000036067697],"about_ca_topic_score_codex":0.0025431763,"about_ca_topic_score_gemma":0.0024547763,"teacher_disagreement_score":0.0025431763,"about_ca_system_score_codex":0.0002457101,"about_ca_system_score_gemma":0.0002758361,"threshold_uncertainty_score":0.005689621},"labels":[],"label_agreement":null},{"id":"W2404792681","doi":"10.21611/qirt.2004.011","title":"Combining video and thermal imagery for robust pedestrian tracking","year":2004,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Pedestrian; Computer vision; Artificial intelligence; Tracking (education); Pedestrian detection; Geography","score_opus":0.053064922208923004,"score_gpt":0.29475145655978896,"score_spread":0.24168653435086596,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2404792681","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10273421,0.0038973568,0.88314533,0.0002313312,0.00035870407,0.000093735915,0.00045086173,0.0018988386,0.0071896045],"genre_scores_gemma":[0.65289456,0.0021784964,0.34078506,0.00019442035,0.00027985344,0.00006513197,0.0007247084,0.00009653775,0.0027812156],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996705,0.00007080096,0.000010351226,0.000094226656,0.00010563054,0.00004846909],"domain_scores_gemma":[0.9996592,0.00009441124,0.00004916692,0.000053453317,0.000117775584,0.00002603507],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000534871,0.00062979193,0.0005075785,0.00162378,0.00018856303,0.00070776645,0.00045996025,0.0006025818,0.001206038],"category_scores_gemma":[0.0012611757,0.00036701476,0.00042650945,0.0009547681,0.00019181446,0.0009142464,0.00050646765,0.00047213558,0.00084555155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008896968,0.00018035115,0.0048005297,0.00035687978,0.0001400908,0.00017688208,0.000098406985,0.035345983,0.17749439,0.0019783867,0.004923178,0.77361524],"study_design_scores_gemma":[0.00007371482,0.00055145205,0.021784386,0.00010917476,0.00031301132,0.0011833512,0.00015045829,0.8045024,0.1537464,0.0057852287,0.011673662,0.00012679189],"about_ca_topic_score_codex":0.0013887263,"about_ca_topic_score_gemma":0.0028108857,"teacher_disagreement_score":0.00162378,"about_ca_system_score_codex":0.000189339,"about_ca_system_score_gemma":0.00019310082,"threshold_uncertainty_score":0.004034579},"labels":[],"label_agreement":null},{"id":"W2406965490","doi":"","title":"Context-aware real-time video analytics","year":2015,"lang":"en","type":"article","venue":"Computer Science and Software Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Upload; Computer science; Scalability; Context (archaeology); Analytics; Internet of Things; World Wide Web; The Internet; Multimedia; Mobile device; Data science; Database","score_opus":0.02497697905976251,"score_gpt":0.2531745222659734,"score_spread":0.2281975432062109,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2406965490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13243118,0.004898525,0.84404206,0.0012462009,0.0003338513,0.00020648671,0.00060943153,0.005209514,0.01102277],"genre_scores_gemma":[0.8832823,0.0015653714,0.11162651,0.00021889106,0.00024699554,0.000074640164,0.000599222,0.00008844572,0.0022975875],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995981,0.000059827657,0.000020469097,0.00011729168,0.00015911512,0.000045208115],"domain_scores_gemma":[0.9994547,0.00016604798,0.00008249657,0.00012259418,0.00012797282,0.00004632778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037823006,0.0005598011,0.0007090056,0.00082358386,0.00041755236,0.0013100791,0.000848299,0.00049404585,0.0009027979],"category_scores_gemma":[0.001325901,0.00023111851,0.00029417133,0.00087731396,0.00033984665,0.0021426457,0.0009902611,0.00076806895,0.00042948616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006525233,0.0004835401,0.008780393,0.00041568736,0.00013219824,0.00051952247,0.00045315936,0.036572233,0.18810724,0.014288729,0.012390885,0.73720384],"study_design_scores_gemma":[0.00003564541,0.00044185805,0.0072115497,0.00008775091,0.000091910864,0.0007102589,0.00063294935,0.85801023,0.09191289,0.01907543,0.021707738,0.000081856124],"about_ca_topic_score_codex":0.001364306,"about_ca_topic_score_gemma":0.0029719155,"teacher_disagreement_score":0.001364306,"about_ca_system_score_codex":0.0003463038,"about_ca_system_score_gemma":0.00041719424,"threshold_uncertainty_score":0.0030201674},"labels":[],"label_agreement":null},{"id":"W2409440702","doi":"10.2316/journal.206.2009.3.206-3268","title":"STEREO-BASED RECONSTRUCTION UNCERTAINTY AND EGO-MOTION ESTIMATION","year":2009,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Estimation; Artificial intelligence; Id, ego and super-ego; Computer vision; Motion (physics); Psychology; Economics; Psychoanalysis","score_opus":0.015282105542893509,"score_gpt":0.29338579453815333,"score_spread":0.27810368899525983,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2409440702","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0028313177,0.00011111565,0.99654657,0.00002212659,0.000010122219,0.000007242513,0.000022620632,0.0001130707,0.00033586868],"genre_scores_gemma":[0.36025625,0.00088406634,0.6353804,0.00009325165,0.00012405784,0.00011786989,0.00037726923,0.0001465758,0.0026202567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985744,0.00020326316,0.00007130392,0.00027082855,0.0007913121,0.00008896855],"domain_scores_gemma":[0.99872965,0.000358111,0.00019370165,0.00019553053,0.00048904575,0.000033858203],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007628658,0.00069574115,0.001176678,0.001751692,0.0005098571,0.00077458785,0.0011337795,0.00077565335,0.0011458462],"category_scores_gemma":[0.0033420683,0.00072105514,0.00096980325,0.0012880432,0.0006973396,0.0019204016,0.0012545006,0.0008457663,0.00038881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017363741,0.0000534949,0.0018209439,0.00030999485,0.0001266755,0.00022276993,0.0002405125,0.43491527,0.032709938,0.042348694,0.0023277616,0.48475042],"study_design_scores_gemma":[0.000006242131,0.000034534758,0.00066092896,0.000012611662,0.000019317098,0.00013594914,0.00001708566,0.98303014,0.009224221,0.0051970477,0.001628326,0.000033660588],"about_ca_topic_score_codex":0.004014127,"about_ca_topic_score_gemma":0.003275326,"teacher_disagreement_score":0.004014127,"about_ca_system_score_codex":0.0006838317,"about_ca_system_score_gemma":0.0011447628,"threshold_uncertainty_score":0.007981539},"labels":[],"label_agreement":null},{"id":"W2463396900","doi":"10.3390/s16071102","title":"Sensors for Entertainment","year":2016,"lang":"en","type":"editorial","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Entertainment; Field (mathematics); State (computer science); Computer science; Engineering; Art; Visual arts","score_opus":0.01489137773368058,"score_gpt":0.3137583352210051,"score_spread":0.2988669574873245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2463396900","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":[0.000032438857,0.026798166,0.00031238014,0.026223682,0.94050443,0.000018502642,0.00004668959,0.00006674493,0.0059970096],"genre_scores_gemma":[0.0007808578,0.033062063,0.00032938624,0.027563265,0.8931372,0.00003323244,0.00006851042,0.00008252026,0.04494302],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99609226,0.0005086257,0.00041505994,0.0004896776,0.002323564,0.00017079677],"domain_scores_gemma":[0.99279046,0.0025426908,0.0004846844,0.0003020999,0.0028652332,0.0010148376],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035461818,0.002752674,0.0018560066,0.0023018988,0.0018968876,0.0056354543,0.0022240472,0.01040994,0.015354863],"category_scores_gemma":[0.010873165,0.0007694447,0.0013625791,0.0010137757,0.0022812886,0.004491036,0.0020365887,0.016585056,0.016030472],"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":[0.000017819137,0.000006930132,0.000012295314,0.00022347955,0.000007096972,0.00004734382,0.000012375981,0.000018557706,0.00015334395,0.0016719077,0.98255426,0.015274519],"study_design_scores_gemma":[0.0000066804478,0.000011716056,0.000048868325,0.000121450874,0.0000053572944,0.00009389391,0.0000088376055,0.000024935634,0.0000689549,0.0007958174,0.9988097,0.000003863007],"about_ca_topic_score_codex":0.0007549994,"about_ca_topic_score_gemma":0.0026104578,"teacher_disagreement_score":0.015354863,"about_ca_system_score_codex":0.0017485514,"about_ca_system_score_gemma":0.0021075194,"threshold_uncertainty_score":0.051367164},"labels":[],"label_agreement":null},{"id":"W2470867422","doi":"10.1049/el.2016.2109","title":"Person re‐identification by graph‐based metric fusion","year":2016,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Identification (biology); Metric (unit); Graph; Fusion; Artificial intelligence; Mathematics; Theoretical computer science; Engineering; Biology","score_opus":0.015673339879111216,"score_gpt":0.25299581203680077,"score_spread":0.23732247215768956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2470867422","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012426201,0.00024258191,0.98535883,0.0001300909,0.000054066622,0.000040736464,0.00008136053,0.0005888288,0.0010772955],"genre_scores_gemma":[0.4553071,0.00073274,0.538372,0.00022860683,0.00012878218,0.00010828013,0.00095485046,0.000253821,0.0039138356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984257,0.00039744537,0.00009292151,0.00048549887,0.00048076792,0.00011772161],"domain_scores_gemma":[0.99887615,0.00022122516,0.00017032336,0.00034353053,0.0003233002,0.000065481334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011208337,0.0012921294,0.0014426187,0.00268677,0.00042305558,0.0011523212,0.0015905444,0.0014589598,0.00120988],"category_scores_gemma":[0.0039239866,0.00041379806,0.0012289099,0.002408184,0.00066642376,0.0029066121,0.0021507097,0.0012205424,0.0010827003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017749691,0.00012101814,0.0019150967,0.00013085855,0.00021010725,0.00023288892,0.00019955488,0.20886993,0.01745325,0.014882926,0.005894144,0.7499127],"study_design_scores_gemma":[0.0000060130474,0.000073112715,0.0009945591,0.000013562129,0.00003220568,0.0002322536,0.00006906176,0.97165424,0.0066895,0.016932258,0.0032660726,0.000037173755],"about_ca_topic_score_codex":0.0033792355,"about_ca_topic_score_gemma":0.0035237113,"teacher_disagreement_score":0.0033792355,"about_ca_system_score_codex":0.000721196,"about_ca_system_score_gemma":0.0005411939,"threshold_uncertainty_score":0.0067191124},"labels":[],"label_agreement":null},{"id":"W2483543396","doi":"10.4018/978-1-4666-1830-5.ch005","title":"Occlusion Handling in Object Detection","year":2012,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer vision; Artificial intelligence; Object (grammar); Tracking (education); Occlusion; Feature (linguistics); Texture (cosmology); Computer science; Video tracking; Mathematics; Pattern recognition (psychology); Image (mathematics)","score_opus":0.02578590880951475,"score_gpt":0.27674430558075086,"score_spread":0.2509583967712361,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2483543396","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":[0.008190102,0.0047484376,0.9785462,0.00009387351,0.00019785768,0.000091694914,0.000099363744,0.0016573279,0.006375134],"genre_scores_gemma":[0.18184496,0.011397641,0.7753265,0.00034414054,0.00050518423,0.0002666803,0.0014412428,0.0013688796,0.02750485],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977049,0.00031441826,0.00009833145,0.000667129,0.0009430124,0.00027212422],"domain_scores_gemma":[0.99813074,0.0009378863,0.0001949089,0.00037149718,0.00031237796,0.00005265751],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016329591,0.0013325245,0.0020963636,0.002078812,0.0012159386,0.0027002797,0.0020537064,0.001879605,0.0048913807],"category_scores_gemma":[0.0051734755,0.0011816657,0.0012391306,0.0027993652,0.0010406158,0.0030619581,0.0021815922,0.0011380607,0.0038169606],"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":[0.00024361389,0.00007324416,0.0017391125,0.0007385703,0.00010178365,0.000588635,0.00061679975,0.01903084,0.037975032,0.017688088,0.009990003,0.9112143],"study_design_scores_gemma":[0.00006550487,0.00039621268,0.009670233,0.00047279336,0.00033611766,0.0048286454,0.00057793263,0.559348,0.11372039,0.06304239,0.24734724,0.00019463318],"about_ca_topic_score_codex":0.003492963,"about_ca_topic_score_gemma":0.0024969287,"teacher_disagreement_score":0.0048913807,"about_ca_system_score_codex":0.00088124035,"about_ca_system_score_gemma":0.0008244048,"threshold_uncertainty_score":0.016363263},"labels":[],"label_agreement":null},{"id":"W2485576322","doi":"10.1109/cvprw.2016.109","title":"Embedded Motion Detection via Neural Response Mixture Background Modeling","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Deep neural networks; Leverage (statistics); Artificial neural network; Artificial intelligence; Mixture model; Gaussian; Motion (physics); Computer vision; Pattern recognition (psychology)","score_opus":0.03736003334446491,"score_gpt":0.2937142198888596,"score_spread":0.2563541865443947,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2485576322","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027285192,0.000181411,0.9704734,0.00009791375,0.000022343182,0.000015443584,0.000057698362,0.0011366337,0.00072987034],"genre_scores_gemma":[0.62424546,0.00029870338,0.37051156,0.00016233948,0.0000432832,0.00004970258,0.0004229801,0.00015188147,0.0041140732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999716,0.000060513645,0.000011004485,0.000094164374,0.00008716193,0.000031267435],"domain_scores_gemma":[0.9996952,0.00012196602,0.000054624245,0.000041024425,0.000066568195,0.000020656644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056814036,0.0006684647,0.0005867198,0.00067471276,0.00017617627,0.00042292092,0.0010306087,0.00062912685,0.0007534361],"category_scores_gemma":[0.001517953,0.0004931387,0.000576786,0.000508359,0.00033011872,0.0008381523,0.0006836079,0.0008697113,0.00034583974],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025923876,0.000099989,0.0017652147,0.000060239847,0.00011340194,0.00010984776,0.00006728545,0.6658806,0.041651502,0.011153878,0.0017244988,0.27711433],"study_design_scores_gemma":[0.0000017063328,0.000006893383,0.00013835091,0.0000011905536,0.000002540056,0.000009527722,0.0000011735632,0.9969626,0.0019254285,0.0007636362,0.0001838366,0.0000030295228],"about_ca_topic_score_codex":0.0052301143,"about_ca_topic_score_gemma":0.00694395,"teacher_disagreement_score":0.0052301143,"about_ca_system_score_codex":0.00066554087,"about_ca_system_score_gemma":0.00049174624,"threshold_uncertainty_score":0.010399401},"labels":[],"label_agreement":null},{"id":"W2505908631","doi":"10.5539/apr.v8n4p20","title":"Geographical Location Estimation based on An Improved Particle Swarm Optimization","year":2016,"lang":"en","type":"article","venue":"Applied Physics Research","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Particle swarm optimization; Shadow (psychology); Computer science; Convergence (economics); Geographic coordinate system; Object (grammar); Basis (linear algebra); Artificial intelligence; Algorithm; Sequence (biology); Series (stratigraphy); Computer vision; Mathematical optimization; Mathematics; Geography; Geodesy; Geology","score_opus":0.06664341537202347,"score_gpt":0.3805927365966939,"score_spread":0.31394932122467045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2505908631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013646002,0.00020644323,0.98446554,0.00008884663,0.00006409496,0.000027706168,0.000020951682,0.00017657755,0.0013037651],"genre_scores_gemma":[0.5513106,0.00050834194,0.4449659,0.000089709625,0.00014235641,0.00014032124,0.00018459134,0.0000668056,0.0025914067],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956995,0.00011003555,0.000029191546,0.00010357052,0.00015636609,0.000030887597],"domain_scores_gemma":[0.9995204,0.000180629,0.00006814205,0.00004550374,0.00016733451,0.000018023005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005973761,0.0006596668,0.0009148372,0.00096599694,0.00033433532,0.00069483137,0.00084724,0.00078960264,0.00058207393],"category_scores_gemma":[0.0022271508,0.00040773262,0.0007140097,0.0007754283,0.0003904639,0.00093314267,0.00055146014,0.00059028715,0.000230291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000065351545,0.00003535087,0.0018831353,0.0000727313,0.00007422382,0.0001107629,0.00010642895,0.89872706,0.004837823,0.0057971273,0.0010494578,0.08724066],"study_design_scores_gemma":[0.000004874861,0.000010792005,0.0002539258,0.0000023677494,0.000006638789,0.000015687338,0.0000048467414,0.9986426,0.00033845942,0.00039647197,0.0003185912,0.00000464548],"about_ca_topic_score_codex":0.008422748,"about_ca_topic_score_gemma":0.0036869105,"teacher_disagreement_score":0.008422748,"about_ca_system_score_codex":0.000447431,"about_ca_system_score_gemma":0.0006021371,"threshold_uncertainty_score":0.016747475},"labels":[],"label_agreement":null},{"id":"W2507792233","doi":"10.1109/iscas.2016.7527408","title":"Weighted residual minimization in PCA subspace for visual tracking","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Subspace topology; Residual; Sparse approximation; Computer science; Neural coding; Pattern recognition (psychology); Bayesian inference; Eye tracking; Coding (social sciences); Principal component analysis; Computer vision; Bayesian probability; Algorithm; Mathematics","score_opus":0.03366920982359783,"score_gpt":0.3248937686994016,"score_spread":0.2912245588758038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2507792233","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":[0.0017264042,0.00012563517,0.99761665,0.000028124872,0.000009079368,0.000008223,0.00001710863,0.00018741145,0.00028140555],"genre_scores_gemma":[0.17617728,0.0008549261,0.8180891,0.00009800211,0.00008994843,0.00019121899,0.0004673955,0.00025880514,0.0037732394],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995939,0.00010713679,0.000016959037,0.00009609094,0.00015274623,0.000033207096],"domain_scores_gemma":[0.9996362,0.00014715994,0.000050449842,0.000049596787,0.00009506527,0.000021600612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056582683,0.000790105,0.00095783995,0.00083994685,0.00028354255,0.0005045117,0.0007735913,0.00067622395,0.0013378118],"category_scores_gemma":[0.001739184,0.0003787065,0.0007418423,0.0014122354,0.0004879969,0.0007292281,0.0008283384,0.0009221051,0.0007979631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008413522,0.000061957784,0.00034788562,0.00013368417,0.00005899938,0.000054257485,0.00009271926,0.61518747,0.018269964,0.026945192,0.0038308168,0.33493295],"study_design_scores_gemma":[0.0000026006423,0.000010885166,0.00006764025,0.0000029387134,0.0000032393866,0.000014808612,0.0000031137547,0.9950918,0.0010486558,0.003208293,0.0005407986,0.0000052401724],"about_ca_topic_score_codex":0.0046748416,"about_ca_topic_score_gemma":0.0032519242,"teacher_disagreement_score":0.0046748416,"about_ca_system_score_codex":0.000407034,"about_ca_system_score_gemma":0.00086379587,"threshold_uncertainty_score":0.009295285},"labels":[],"label_agreement":null},{"id":"W2516388125","doi":"10.1109/icip.2016.7532653","title":"Saliency prior context model for visual tracking","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Context (archaeology); Artificial intelligence; Tracking (education); Eye tracking; Bayesian probability; Computation; Exploit; Context model; Computer vision; Pattern recognition (psychology); Machine learning; Algorithm","score_opus":0.059271392263988566,"score_gpt":0.34507773137990233,"score_spread":0.28580633911591374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516388125","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":[0.004289696,0.00011559117,0.9949352,0.000033175875,0.000014682122,0.000009330772,0.000019372199,0.00017104165,0.00041193079],"genre_scores_gemma":[0.57649744,0.00050129986,0.41989052,0.00013276387,0.0001494378,0.00009387777,0.00023652556,0.00014801463,0.0023501876],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997627,0.00003513254,0.000009878204,0.000074187294,0.00009515486,0.000022979893],"domain_scores_gemma":[0.99966574,0.00014080854,0.000042680345,0.00004030552,0.000087003325,0.000023452609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034331848,0.00055129506,0.00062842545,0.00076798693,0.00030743223,0.00050090346,0.0012364043,0.00057593174,0.0013899297],"category_scores_gemma":[0.002188365,0.0002999456,0.00056034426,0.00058054057,0.000360833,0.001593982,0.0006789704,0.0008380633,0.0003966546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021391155,0.000071585586,0.0012749699,0.00013438707,0.00007056514,0.00021559629,0.00017514912,0.5662196,0.033680383,0.058174845,0.0025607967,0.33720812],"study_design_scores_gemma":[0.000003636992,0.000013383249,0.00020768758,0.0000032806824,0.0000058254973,0.0000400422,0.0000036898225,0.99147964,0.001214921,0.0064225816,0.00059945165,0.0000058578316],"about_ca_topic_score_codex":0.005890996,"about_ca_topic_score_gemma":0.005556877,"teacher_disagreement_score":0.005890996,"about_ca_system_score_codex":0.00075753976,"about_ca_system_score_gemma":0.00054910773,"threshold_uncertainty_score":0.011713386},"labels":[],"label_agreement":null},{"id":"W2516937719","doi":"10.1109/iscas.2016.7539118","title":"A system-level design for foreground and background identification in 3D scenes","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Field-programmable gate array; Pipeline (software); Identification (biology); Digital signal processing; Segmentation; Artificial intelligence; Computer vision; Feature extraction; Object detection; Computer hardware; System on a chip; Video processing; Feature (linguistics); Image processing; Chip; Embedded system; Image (mathematics)","score_opus":0.13062126237903096,"score_gpt":0.32880475382072216,"score_spread":0.1981834914416912,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2516937719","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":[0.013655849,0.00023644588,0.97592384,0.0000785449,0.00007669162,0.00014371303,0.00011190343,0.0046105394,0.0051624933],"genre_scores_gemma":[0.40226737,0.0003040446,0.58862287,0.00034879716,0.00006655552,0.00025082938,0.0004263469,0.0003165671,0.0073965746],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997162,0.000027761163,0.00001492985,0.00006712403,0.00012494734,0.00004908427],"domain_scores_gemma":[0.9997763,0.000034281413,0.000027043363,0.000036122226,0.000107408945,0.000018861421],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022448828,0.00047722753,0.0003239015,0.0005374198,0.0003278292,0.00092988845,0.0012569604,0.0005124376,0.004725325],"category_scores_gemma":[0.00046482254,0.0002997458,0.0003953763,0.00027230594,0.00021514727,0.0006255112,0.0003974358,0.00040079653,0.0019444579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047964504,0.00022814434,0.0042113853,0.0009668378,0.00022936275,0.00091480114,0.00045503263,0.06769613,0.4079062,0.01970119,0.014475275,0.48273602],"study_design_scores_gemma":[0.00008690513,0.000743904,0.004031936,0.00011471773,0.00018943488,0.0013126195,0.00009716517,0.6714889,0.25155556,0.0031563933,0.06713794,0.000084557396],"about_ca_topic_score_codex":0.0017898824,"about_ca_topic_score_gemma":0.0024732042,"teacher_disagreement_score":0.004725325,"about_ca_system_score_codex":0.00055258535,"about_ca_system_score_gemma":0.00072808407,"threshold_uncertainty_score":0.015807807},"labels":[],"label_agreement":null},{"id":"W2519895415","doi":"10.1109/hpcsim.2016.7568386","title":"A unified threshold updating strategy for multivariate Gaussian mixture based moving object detection","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Background subtraction; Pixel; Artificial intelligence; Mixture model; Computer science; Computer vision; Distortion (music); Object detection; Similarity (geometry); Gaussian; Set (abstract data type); Object (grammar); Pattern recognition (psychology); Foreground detection; Gaussian process; Image (mathematics)","score_opus":0.040304148557302265,"score_gpt":0.30856544801283264,"score_spread":0.2682612994555304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2519895415","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":[0.002992144,0.00019595506,0.9959306,0.000024720408,0.000029529036,0.0000184819,0.000014586724,0.00045078734,0.00034316126],"genre_scores_gemma":[0.18269481,0.00061431335,0.81352115,0.000111175184,0.00009248657,0.00011249492,0.00022249836,0.00020533292,0.0024257563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991301,0.00009700018,0.000062581814,0.0002622634,0.00036902344,0.000079215315],"domain_scores_gemma":[0.99952364,0.00010371023,0.000047049005,0.000058362933,0.00023160627,0.000035566583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010727748,0.00087830605,0.0014410259,0.0015507155,0.00045135344,0.0010673947,0.0018417063,0.00090436783,0.0014410843],"category_scores_gemma":[0.002421704,0.0005232615,0.00095347856,0.0013775614,0.0005805552,0.0012679114,0.0012882082,0.0010598273,0.0009848131],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023909348,0.00010398259,0.0013961788,0.00015417849,0.000128431,0.00014680099,0.00022177916,0.0741521,0.06134795,0.010718171,0.002646931,0.84874445],"study_design_scores_gemma":[0.000015509397,0.000084583065,0.0010470436,0.000014145296,0.000068456706,0.0001984561,0.00003440877,0.9767249,0.014488148,0.0038261388,0.0034505879,0.000047621466],"about_ca_topic_score_codex":0.0058871605,"about_ca_topic_score_gemma":0.0057537644,"teacher_disagreement_score":0.0058871605,"about_ca_system_score_codex":0.0007901163,"about_ca_system_score_gemma":0.00094800466,"threshold_uncertainty_score":0.011705756},"labels":[],"label_agreement":null},{"id":"W2525668722","doi":"10.1016/j.patrec.2016.09.014","title":"Interactive deep learning method for segmenting moving objects","year":2016,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":336,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Ground truth; Artificial intelligence; Segmentation; Convolutional neural network; Computer vision; Margin (machine learning); Pixel; Market segmentation; Code (set theory); Deep learning; Pattern recognition (psychology); Machine learning; Set (abstract data type)","score_opus":0.02705768264026516,"score_gpt":0.3007710899321639,"score_spread":0.27371340729189875,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2525668722","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":[0.015241469,0.00018995567,0.9801114,0.00010291558,0.00006773013,0.0000477403,0.0001570843,0.0022845182,0.0017972229],"genre_scores_gemma":[0.23845862,0.0002718331,0.7443194,0.00028766028,0.00009425772,0.00017810664,0.000990408,0.0004925363,0.014907248],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996214,0.00004550122,0.000016363452,0.00012948674,0.000107759486,0.00007957592],"domain_scores_gemma":[0.9996301,0.00010557149,0.00002286231,0.000078202036,0.00011933646,0.000043955977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007248883,0.0011160945,0.0007923658,0.0010393605,0.00047410646,0.0008078596,0.0018546369,0.0015706634,0.006059133],"category_scores_gemma":[0.0009419655,0.00060332945,0.0008244774,0.0007899842,0.00040419662,0.001011804,0.0016220887,0.0015140445,0.0016514198],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047427422,0.00027939153,0.0011658557,0.000116053045,0.00013822905,0.00013652213,0.00011898477,0.09073165,0.05345137,0.0071973745,0.008711478,0.8374788],"study_design_scores_gemma":[0.000014180508,0.00003379284,0.0003591715,0.000007890316,0.000019861616,0.000039548617,0.000011315222,0.98460704,0.0111627355,0.0018208438,0.0019158901,0.0000077618715],"about_ca_topic_score_codex":0.009405319,"about_ca_topic_score_gemma":0.016920155,"teacher_disagreement_score":0.009405319,"about_ca_system_score_codex":0.00086946186,"about_ca_system_score_gemma":0.0010096566,"threshold_uncertainty_score":0.02026987},"labels":[],"label_agreement":null},{"id":"W2532901499","doi":"","title":"Online configuration of PTZ camera networks","year":2012,"lang":"en","type":"article","venue":"International Conference on Distributed Smart Cameras","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Zoom; Computer science; Computer vision; Artificial intelligence; Scalability; Tilt (camera); Tracking (education); Smart camera; Handover; Tracking system; Eye tracking; Computer graphics (images); Kalman filter; Mathematics; Engineering; Computer network","score_opus":0.05490763734917596,"score_gpt":0.3290971248827313,"score_spread":0.2741894875335553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2532901499","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00906675,0.000114100774,0.98644173,0.000053647957,0.000039642044,0.00006351289,0.000055188422,0.0011725645,0.0029928295],"genre_scores_gemma":[0.5675835,0.00028637997,0.42349446,0.00009429545,0.000049584738,0.00033928285,0.00043784827,0.00031915106,0.0073955352],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990414,0.00022066456,0.00003715385,0.00026467175,0.00032226785,0.000113868744],"domain_scores_gemma":[0.9990194,0.00019546978,0.00012643717,0.00039549862,0.0001879388,0.000075292075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056541234,0.00093587924,0.0010691166,0.0008729014,0.00090685766,0.0014214186,0.0019740881,0.00070967077,0.005952096],"category_scores_gemma":[0.0028410978,0.0005454188,0.00036998114,0.0008661745,0.0005961427,0.0019955595,0.00284128,0.000951155,0.0013670048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051408925,0.00009435843,0.0013885322,0.00013517523,0.000047983114,0.00053649984,0.00036556114,0.44157955,0.024746656,0.037592694,0.0076388214,0.48536006],"study_design_scores_gemma":[0.00004179506,0.00008937461,0.0003897103,0.00002013672,0.000013180593,0.00025244884,0.000091186914,0.96917033,0.01103645,0.011424256,0.00744498,0.000026166832],"about_ca_topic_score_codex":0.0023367982,"about_ca_topic_score_gemma":0.002582907,"teacher_disagreement_score":0.005952096,"about_ca_system_score_codex":0.0009806303,"about_ca_system_score_gemma":0.000714702,"threshold_uncertainty_score":0.019911706},"labels":[],"label_agreement":null},{"id":"W2536460470","doi":"10.1109/tencon.1993.320194","title":"A computer vision system for measurement of pedestrian volume","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Volume (thermodynamics); Pedestrian; Computer science; Computer vision; Pedestrian detection; Artificial intelligence; Computer graphics (images); Engineering","score_opus":0.06817798371699026,"score_gpt":0.2831195578778162,"score_spread":0.21494157416082593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2536460470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009226667,0.0006944743,0.979075,0.000101767386,0.00024317225,0.00024716803,0.00036773106,0.0051822034,0.004861792],"genre_scores_gemma":[0.14484502,0.0007439404,0.8421799,0.0002739295,0.00021073004,0.00060246565,0.0011807415,0.00024467643,0.009718491],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990177,0.00016704567,0.000051972776,0.00023509726,0.00046696918,0.00006121502],"domain_scores_gemma":[0.999178,0.0001451836,0.00006412289,0.00013437765,0.00042604172,0.000052243122],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083930115,0.0005568481,0.0007638304,0.0020244834,0.00053138734,0.0009708669,0.0009269548,0.0010754275,0.0059194528],"category_scores_gemma":[0.0018331724,0.00035512462,0.0005376055,0.0014817254,0.00029439016,0.0009421943,0.00068089267,0.0007702422,0.0031672297],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003905217,0.00013097408,0.002060495,0.00033405575,0.00008011296,0.00019203058,0.00016403956,0.006374368,0.15832874,0.014393987,0.014812664,0.8027381],"study_design_scores_gemma":[0.00024709845,0.0014229845,0.017219154,0.00016344007,0.00025121158,0.004021038,0.00013379149,0.4168579,0.32035702,0.011571255,0.22745526,0.00029997408],"about_ca_topic_score_codex":0.0021955967,"about_ca_topic_score_gemma":0.0020268913,"teacher_disagreement_score":0.0059194528,"about_ca_system_score_codex":0.0006534639,"about_ca_system_score_gemma":0.0010057981,"threshold_uncertainty_score":0.01980257},"labels":[],"label_agreement":null},{"id":"W2542062388","doi":"10.1007/s00138-017-0884-9","title":"SPiKeS: Superpixel-Keypoints structure for robust visual tracking","year":2017,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Nvidia","keywords":"Discriminative model; Artificial intelligence; BitTorrent tracker; Computer science; Computer vision; Pattern recognition (psychology); Matching (statistics); Feature (linguistics); Tracking (education); Video tracking; Process (computing); Object (grammar); Eye tracking; Mathematics","score_opus":0.029290076427125448,"score_gpt":0.36416752064534524,"score_spread":0.3348774442182198,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2542062388","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003656084,0.00015892848,0.99123985,0.000067029956,0.00007042522,0.000041897223,0.0003884852,0.004105153,0.00027208534],"genre_scores_gemma":[0.123309016,0.00041879126,0.86757916,0.00018872657,0.0001260578,0.00019081324,0.0030413282,0.0014680285,0.0036781072],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990754,0.00013104409,0.000037173762,0.00023827369,0.00043758852,0.00008057047],"domain_scores_gemma":[0.9988288,0.00028235163,0.00012259187,0.0003771798,0.00028491695,0.000104110055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011794234,0.0014097126,0.0015251718,0.001719013,0.00047255322,0.0011400637,0.0024314749,0.0019302653,0.0057346574],"category_scores_gemma":[0.003871373,0.00090761535,0.0010115895,0.0024360141,0.00065182365,0.001982165,0.0023393042,0.0024065168,0.0038517758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010284709,0.00017799242,0.00072389643,0.00022744677,0.00014206312,0.00013627292,0.000089486806,0.07773263,0.043665014,0.015563248,0.021487648,0.83902586],"study_design_scores_gemma":[0.00004891909,0.000095678326,0.0004523135,0.000016007516,0.000021565833,0.00009730662,0.000015347312,0.96082133,0.017837647,0.015505026,0.005067604,0.000021309543],"about_ca_topic_score_codex":0.0037416806,"about_ca_topic_score_gemma":0.005719276,"teacher_disagreement_score":0.0057346574,"about_ca_system_score_codex":0.00082325743,"about_ca_system_score_gemma":0.0011560897,"threshold_uncertainty_score":0.019184351},"labels":[],"label_agreement":null},{"id":"W2543089309","doi":"10.1109/fit.2015.13","title":"Automatic Vehicle Detection and Driver Identification Framework for Secure Vehicle Parking","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University","funders":"","keywords":"Computer science; Facial recognition system; Identification (biology); Artificial intelligence; Face (sociological concept); Face detection; Computer vision; Eigenface; Haar-like features; Object-class detection; Boosting (machine learning); Key (lock); Feature (linguistics); Feature extraction; Pattern recognition (psychology); Computer security","score_opus":0.042311659764420174,"score_gpt":0.31534758806649843,"score_spread":0.27303592830207823,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2543089309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013684888,0.00012656905,0.9836569,0.00004821203,0.000025243102,0.0000755323,0.000056148932,0.0014929409,0.00083367934],"genre_scores_gemma":[0.43404323,0.00023163181,0.5591551,0.00007087464,0.000038742244,0.00022599263,0.0003940716,0.0000680697,0.005772262],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971455,0.000035087047,0.000010367835,0.000076056574,0.00010909802,0.00005484679],"domain_scores_gemma":[0.9999031,0.00001187846,0.000011118257,0.000011569507,0.000051483647,0.0000108951335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004078977,0.0004260344,0.00053429726,0.0008757977,0.00038455118,0.00046051282,0.0009981889,0.0005004794,0.0019308673],"category_scores_gemma":[0.00033495616,0.00022645922,0.0006029477,0.00030071093,0.00021968217,0.00052823866,0.00058817194,0.00044655605,0.0006926681],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026852466,0.00037430009,0.0039629964,0.0001508164,0.00013233359,0.0005844308,0.00024902204,0.24899827,0.09226822,0.025461504,0.0074480614,0.6201015],"study_design_scores_gemma":[0.0000086761775,0.000058006714,0.0006378564,0.0000039927227,0.000011683538,0.00017235274,0.000037614598,0.98504746,0.009287268,0.001871736,0.002845223,0.00001821077],"about_ca_topic_score_codex":0.0068160356,"about_ca_topic_score_gemma":0.005740751,"teacher_disagreement_score":0.0068160356,"about_ca_system_score_codex":0.0004344379,"about_ca_system_score_gemma":0.0010161041,"threshold_uncertainty_score":0.013552725},"labels":[],"label_agreement":null},{"id":"W2546765065","doi":"10.1109/ccece.2016.7726647","title":"Visual tracking via bilateral 2DPCA and robust coding","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Residual; Computer science; Artificial intelligence; Coding (social sciences); Neural coding; Sparse approximation; Pattern recognition (psychology); Regularization (linguistics); Computer vision; Algorithm; Mathematics","score_opus":0.035728469293574344,"score_gpt":0.29106605228839955,"score_spread":0.25533758299482523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546765065","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":[0.0018329765,0.00007527188,0.9973437,0.00004484653,0.000018924837,0.00001645175,0.000023034632,0.00023140528,0.0004133257],"genre_scores_gemma":[0.18522748,0.00046402874,0.81025326,0.00014788398,0.00009316649,0.00016459335,0.00036934327,0.00021500191,0.003065287],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993488,0.000114040406,0.000029493453,0.0001884907,0.00025588667,0.000063215826],"domain_scores_gemma":[0.9993331,0.0002099144,0.00010597826,0.00013810287,0.00017271437,0.00004012588],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010018006,0.0008398524,0.0008939072,0.0013372406,0.00042578453,0.001020547,0.0012382895,0.00102323,0.0013898814],"category_scores_gemma":[0.0034476537,0.0005462877,0.0011818227,0.0016936203,0.0007580922,0.0011103632,0.0014745123,0.0012725706,0.00065088726],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000116321295,0.00006242158,0.0007332735,0.000108956774,0.000095496405,0.00008929403,0.00013659087,0.41192386,0.030179594,0.029866155,0.0031987382,0.52348936],"study_design_scores_gemma":[0.0000048856587,0.000013869439,0.00017198808,0.000004711316,0.000007907377,0.000039256945,0.000003876336,0.99282163,0.0025397267,0.0033263173,0.001055238,0.000010612073],"about_ca_topic_score_codex":0.010076759,"about_ca_topic_score_gemma":0.0081465375,"teacher_disagreement_score":0.010076759,"about_ca_system_score_codex":0.000628291,"about_ca_system_score_gemma":0.0015817599,"threshold_uncertainty_score":0.02003622},"labels":[],"label_agreement":null},{"id":"W2546871223","doi":"10.1109/ccece.2016.7726655","title":"Cascaded particle filter for real-time tracking using RGB-D sensor","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Particle filter; Computer vision; Artificial intelligence; Tracking (education); Minimum bounding box; Computer science; RGB color model; Filter (signal processing); Tracking system; Image (mathematics)","score_opus":0.08088989510096557,"score_gpt":0.3328959206079782,"score_spread":0.2520060255070126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2546871223","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036191163,0.00017560896,0.99493307,0.000027462791,0.000055637855,0.000022422772,0.000032551656,0.0006201553,0.0005140577],"genre_scores_gemma":[0.2957802,0.00070823607,0.6985775,0.000108273955,0.000084609324,0.00014762039,0.0002821064,0.00010229541,0.0042090775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993868,0.00007322386,0.000032670814,0.00018428203,0.00027321748,0.000049869042],"domain_scores_gemma":[0.9996113,0.00012286671,0.00004520311,0.000050759674,0.0001449087,0.000025067686],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072080543,0.0009715714,0.00095176906,0.00075063255,0.0004367112,0.0006250309,0.0012480762,0.0009911484,0.001789786],"category_scores_gemma":[0.0012413338,0.00059398124,0.00097130996,0.00080958346,0.00027929188,0.0008858909,0.0006359336,0.00091729814,0.00084539567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005338656,0.00021870325,0.004093085,0.00032345834,0.00023701918,0.00037709548,0.00025643676,0.34310627,0.069234826,0.008063683,0.006353469,0.56720203],"study_design_scores_gemma":[0.000009307239,0.000038846014,0.0006936105,0.000006758992,0.00001839457,0.000042330452,0.0000059193876,0.99134946,0.005664827,0.0006372745,0.0015194037,0.000013901657],"about_ca_topic_score_codex":0.012278322,"about_ca_topic_score_gemma":0.010179526,"teacher_disagreement_score":0.012278322,"about_ca_system_score_codex":0.0007972779,"about_ca_system_score_gemma":0.0009100669,"threshold_uncertainty_score":0.024413705},"labels":[],"label_agreement":null},{"id":"W2547139274","doi":"10.1109/ccece.2016.7726834","title":"Toward study of features associated with natural sleep posture using a depth sensor","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Torso; Polygon mesh; Artificial intelligence; Computer vision; Plane (geometry); Computer science; Horizontal plane; Normal; STRIPS; Feature (linguistics); Transformation (genetics); Pattern recognition (psychology); Surface (topology); Geology; Geometry; Mathematics; Geodesy; Computer graphics (images)","score_opus":0.04063904678365207,"score_gpt":0.299806367582345,"score_spread":0.259167320798693,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2547139274","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":[0.5444804,0.0005802724,0.45261717,0.000087547225,0.000029855722,0.000052976255,0.00043182058,0.00030998135,0.0014100007],"genre_scores_gemma":[0.9169536,0.00036793537,0.08185884,0.000019834217,0.000030025843,0.000028560999,0.0002878384,0.000024620038,0.00042869046],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99988973,0.000019706951,0.0000051734137,0.00003237133,0.000036319227,0.000016750162],"domain_scores_gemma":[0.99976295,0.000080648606,0.000061984276,0.000027112104,0.00005101236,0.000016324851],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00012425924,0.00029427515,0.00024095557,0.0006859717,0.00009462778,0.0003499008,0.00021477262,0.00028043147,0.0003407339],"category_scores_gemma":[0.00077281246,0.00016760975,0.00028016852,0.00057423476,0.00018737138,0.0004880832,0.00023031361,0.0001955723,0.000106132225],"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":[0.0004795072,0.00016294827,0.09279929,0.0004238826,0.00013392854,0.0006743019,0.000586982,0.057304956,0.38746077,0.0029727474,0.0015785402,0.45542222],"study_design_scores_gemma":[0.000014348264,0.00035068532,0.21109505,0.00004045692,0.00008089643,0.0011809439,0.0005356122,0.7366998,0.044470083,0.0032257575,0.0022492963,0.00005704915],"about_ca_topic_score_codex":0.0012463304,"about_ca_topic_score_gemma":0.0019193161,"teacher_disagreement_score":0.0012463304,"about_ca_system_score_codex":0.0001086573,"about_ca_system_score_gemma":0.00015575437,"threshold_uncertainty_score":0.0024781823},"labels":[],"label_agreement":null},{"id":"W2550544881","doi":"10.1109/tcsvt.2016.2632439","title":"Traffic Analytics With Low-Frame-Rate Videos","year":2016,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Google (Canada); Université de Sherbrooke","funders":"Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Artificial intelligence; Computer vision; Frame (networking); Convolutional neural network; Process (computing); Perspective (graphical); Road traffic; Frame rate; Motion (physics); Transport engineering; Engineering; Computer network","score_opus":0.022007435243692246,"score_gpt":0.2584224321010866,"score_spread":0.23641499685739437,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2550544881","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25922728,0.0019515128,0.7185144,0.00066269125,0.00041614583,0.0002845022,0.0027340578,0.0064106565,0.009798758],"genre_scores_gemma":[0.7493322,0.0012503073,0.24245821,0.00018817197,0.00040118513,0.000101977006,0.003428411,0.00023992694,0.0025996228],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995833,0.000060536146,0.00001724609,0.0001059294,0.00016184931,0.0000710838],"domain_scores_gemma":[0.9989291,0.0004458748,0.00015000749,0.00011601976,0.00029957137,0.0000593185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044738472,0.0010284294,0.0005926597,0.0025176564,0.00025805095,0.0011476579,0.0008880427,0.00073589396,0.0019975377],"category_scores_gemma":[0.0031756202,0.00023452367,0.00032471743,0.0014438404,0.00023530806,0.0017164055,0.0005121983,0.00075579155,0.0010277883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083036826,0.0006084326,0.018691989,0.0007865683,0.00016869376,0.0010143212,0.00024262763,0.11814858,0.131823,0.0046958067,0.011108731,0.7118809],"study_design_scores_gemma":[0.000020216965,0.0001919232,0.010641789,0.00006228262,0.000052228905,0.00045762758,0.00016309139,0.9373157,0.041597173,0.0031315235,0.006336511,0.000029995681],"about_ca_topic_score_codex":0.004404897,"about_ca_topic_score_gemma":0.0048527448,"teacher_disagreement_score":0.004404897,"about_ca_system_score_codex":0.00040123734,"about_ca_system_score_gemma":0.00032943962,"threshold_uncertainty_score":0.008758545},"labels":[],"label_agreement":null},{"id":"W2555395799","doi":"10.1145/2910674.2910703","title":"A New Approach of Facial Expression Recognition for Ambient Assisted Living","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Facial expression recognition; Computer science; Facial expression; Assisted living; Facial recognition system; Expression (computer science); Artificial intelligence; Pattern recognition (psychology); Computer vision; Medicine","score_opus":0.08548876628517864,"score_gpt":0.311544373994102,"score_spread":0.22605560770892336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2555395799","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012207363,0.0006863073,0.98348814,0.00012776679,0.00022497369,0.000092020375,0.00010226657,0.0006607739,0.0024103133],"genre_scores_gemma":[0.24256392,0.0015460525,0.7429249,0.00022778659,0.00019849982,0.0002531976,0.0005694279,0.00014075518,0.011575423],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990995,0.00012221938,0.000048810813,0.00027503062,0.00039305066,0.00006143145],"domain_scores_gemma":[0.999762,0.000029461193,0.000013155383,0.000037779024,0.00014653771,0.000011041829],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005553553,0.00049755815,0.00068505865,0.0006385116,0.00030983944,0.00051036564,0.0007572078,0.0004922663,0.0014521934],"category_scores_gemma":[0.00086919346,0.00019232246,0.00070728944,0.000623091,0.00027471202,0.0008733914,0.00047425402,0.00057161174,0.001107618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012881761,0.000082096645,0.000880629,0.00013469068,0.00008490324,0.00010754886,0.00011876201,0.007213489,0.13916045,0.004863219,0.003471773,0.8437536],"study_design_scores_gemma":[0.000034125154,0.000422676,0.009428797,0.000054740158,0.00021049568,0.0012993686,0.00019860914,0.78641284,0.15607683,0.005379214,0.040348455,0.00013387157],"about_ca_topic_score_codex":0.0023370509,"about_ca_topic_score_gemma":0.0019737864,"teacher_disagreement_score":0.0023370509,"about_ca_system_score_codex":0.00031362305,"about_ca_system_score_gemma":0.00038791465,"threshold_uncertainty_score":0.004858136},"labels":[],"label_agreement":null},{"id":"W2557448395","doi":"10.1109/iemcon.2016.7746284","title":"Signal processing techniques for natural sleep posture estimation using depth data","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Gabor filter; Computer science; Computer vision; Orientation (vector space); Gabor transform; Pattern recognition (psychology); Feature (linguistics); Feature extraction; SIGNAL (programming language); Signal processing; Filter (signal processing); Sleep (system call); Time–frequency analysis; Digital signal processing; Mathematics","score_opus":0.073395896938933,"score_gpt":0.37086840428223206,"score_spread":0.29747250734329905,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2557448395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.033394862,0.0012630806,0.96322155,0.00009452632,0.0000720527,0.00006191802,0.00022945605,0.00041428683,0.0012483295],"genre_scores_gemma":[0.34995866,0.0033210728,0.6440979,0.00011747609,0.00014456655,0.00018819122,0.0005380107,0.00004576201,0.0015882824],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998636,0.00002200585,0.000012189743,0.000026345819,0.000061277475,0.000014631849],"domain_scores_gemma":[0.9997968,0.000068530135,0.000034632176,0.000023478826,0.00006839603,0.000008079918],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023336467,0.00047347412,0.00030898163,0.0009685562,0.00013073314,0.00033614092,0.0003062314,0.00032712866,0.0010744091],"category_scores_gemma":[0.00083444006,0.0001550993,0.00034611102,0.0010202539,0.0001189055,0.00045026536,0.00022023646,0.00029317738,0.000424601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018720084,0.00006643745,0.0033748206,0.00034583246,0.00005309185,0.00014379286,0.00013848682,0.014923656,0.17318372,0.0023349759,0.0015461998,0.80370176],"study_design_scores_gemma":[0.00005434227,0.0006765182,0.042366,0.00014241863,0.00016066984,0.0013347777,0.00031687773,0.8277378,0.1061771,0.0055382485,0.015405135,0.00009001491],"about_ca_topic_score_codex":0.0010301227,"about_ca_topic_score_gemma":0.0015402436,"teacher_disagreement_score":0.0010744091,"about_ca_system_score_codex":0.00013235876,"about_ca_system_score_gemma":0.00020804768,"threshold_uncertainty_score":0.0035942197},"labels":[],"label_agreement":null},{"id":"W2562023448","doi":"10.1016/j.asoc.2016.12.035","title":"Face detection and recognition in an unconstrained environment for mobile visual assistive system","year":2016,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Computer vision; Mobile device; Facial recognition system; Face detection; Wearable computer; Motion blur; Pattern recognition (psychology); Embedded system","score_opus":0.02085899376072163,"score_gpt":0.271887826267177,"score_spread":0.25102883250645536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2562023448","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":[0.772236,0.00018197083,0.22428127,0.0001350143,0.00007344731,0.00004917185,0.00014369127,0.0005067758,0.002392743],"genre_scores_gemma":[0.9602492,0.00007652437,0.036812697,0.000048604146,0.000025047992,0.000030445528,0.000072851246,0.000019240068,0.002665436],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999828,0.00002542055,0.0000054373772,0.000044572862,0.000055124183,0.000041475178],"domain_scores_gemma":[0.99987304,0.000040868093,0.000011610508,0.000012366125,0.000042447322,0.00001967515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015176651,0.00032108187,0.00043475293,0.00023471854,0.00034400274,0.00029264318,0.00033569286,0.00044597793,0.0014259999],"category_scores_gemma":[0.0003397987,0.00022959909,0.00023136854,0.00013489524,0.00016986496,0.0003073654,0.00040018416,0.00022442002,0.0005081231],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020918176,0.00044476223,0.011775001,0.00012990793,0.000077316174,0.0013807905,0.00026969402,0.037472997,0.6498741,0.0008155744,0.0022760285,0.29339197],"study_design_scores_gemma":[0.000038589485,0.0006409037,0.02425736,0.000011587858,0.000046535955,0.0008754311,0.00020688496,0.86873245,0.1034403,0.00070201926,0.0010119716,0.00003593925],"about_ca_topic_score_codex":0.0026050147,"about_ca_topic_score_gemma":0.004163849,"teacher_disagreement_score":0.0026050147,"about_ca_system_score_codex":0.00014451236,"about_ca_system_score_gemma":0.00033663074,"threshold_uncertainty_score":0.0051797032},"labels":[],"label_agreement":null},{"id":"W2564296266","doi":"10.1109/crv.2016.56","title":"Tiny People Finder: Long-Range Outdoor HRI by Periodicity Detection","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Computer science; Pixel; Robustness (evolution); False positive paradox; Robot; Mobile robot","score_opus":0.012468362811043836,"score_gpt":0.24970024299134552,"score_spread":0.2372318801803017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2564296266","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17420126,0.0010751638,0.8111284,0.000102034435,0.00019209961,0.00014015708,0.0005959735,0.006323752,0.0062411316],"genre_scores_gemma":[0.5512167,0.0003636911,0.44319075,0.00012098678,0.00012193383,0.00010127679,0.0008206695,0.0002908203,0.003773192],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99981934,0.000018104687,0.0000069857856,0.000060668823,0.0000679758,0.000027003756],"domain_scores_gemma":[0.9997681,0.000041961037,0.00005105701,0.000059327795,0.00004695995,0.000032659224],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021132205,0.00037884043,0.00050928193,0.0009929892,0.0001873403,0.00034469052,0.00059377466,0.00033559784,0.0012417891],"category_scores_gemma":[0.0004597527,0.00021433915,0.000262521,0.0005920812,0.00021770295,0.00044006773,0.00043709,0.00032400773,0.0009760332],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038928192,0.00013689134,0.010713394,0.00020197126,0.00010125135,0.0004527619,0.00019333401,0.0071873595,0.21516821,0.0011877712,0.0061715385,0.7580962],"study_design_scores_gemma":[0.00008421845,0.0006781479,0.07294275,0.000063936284,0.00014373954,0.0039587244,0.00021743988,0.71537894,0.1851392,0.0027864054,0.01847424,0.0001323331],"about_ca_topic_score_codex":0.00084413396,"about_ca_topic_score_gemma":0.0017720668,"teacher_disagreement_score":0.0012417891,"about_ca_system_score_codex":0.00012158141,"about_ca_system_score_gemma":0.00017502547,"threshold_uncertainty_score":0.004154265},"labels":[],"label_agreement":null},{"id":"W2567347102","doi":"10.1109/iros.2016.7759649","title":"UAV, come to me: End-to-end, multi-scale situated HRI with an uninstrumented human and a distant UAV","year":2016,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Situated; Gesture; SIGNAL (programming language); End-to-end principle; Computer vision; Human–computer interaction; Artificial intelligence","score_opus":0.030136348254070112,"score_gpt":0.30622335247233623,"score_spread":0.2760870042182661,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2567347102","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32418445,0.0002499181,0.6485714,0.0002839927,0.0001434412,0.00029018088,0.0003039591,0.0122708455,0.013701738],"genre_scores_gemma":[0.84615344,0.00006311821,0.14518653,0.00017431437,0.000021627307,0.00006654733,0.0002551964,0.00015610499,0.007923168],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998049,0.000029551973,0.0000063515918,0.000056822824,0.000066863875,0.000035521603],"domain_scores_gemma":[0.9997787,0.00006155581,0.00002074896,0.000046478148,0.000031036918,0.00006160514],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023810656,0.00057708763,0.00038605276,0.00016006723,0.00027506636,0.00041289927,0.00069966255,0.00064470107,0.004033014],"category_scores_gemma":[0.00057876244,0.00021169495,0.00019621369,0.00008016487,0.00027439996,0.00050608977,0.00097220944,0.00051416253,0.0010908034],"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":[0.0020632364,0.0006748135,0.008478285,0.000318297,0.0001382873,0.005031922,0.0030536747,0.03532795,0.6282374,0.0028491067,0.013084036,0.30074295],"study_design_scores_gemma":[0.00015153518,0.0021603154,0.02289873,0.000052665153,0.00007524284,0.0034744437,0.001040736,0.7875027,0.15747552,0.0024054192,0.022630218,0.00013249756],"about_ca_topic_score_codex":0.0017157112,"about_ca_topic_score_gemma":0.003191491,"teacher_disagreement_score":0.004033014,"about_ca_system_score_codex":0.00016772293,"about_ca_system_score_gemma":0.00016174463,"threshold_uncertainty_score":0.01349175},"labels":[],"label_agreement":null},{"id":"W2574246024","doi":"10.5539/mas.v11n3p98","title":"Real Time Tracking RGB Color Based Kinect","year":2017,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; RGB color model; Background subtraction; Video tracking; Video processing; Color histogram; Process (computing); Image processing; Computer graphics (images); Color image; Pixel; Image (mathematics)","score_opus":0.03625221332626575,"score_gpt":0.31099304044662335,"score_spread":0.2747408271203576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2574246024","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.077461906,0.0011706102,0.89999986,0.0001966922,0.0004729499,0.0002835316,0.0016311264,0.0056861583,0.013097134],"genre_scores_gemma":[0.46325848,0.0012957908,0.51014465,0.0003121946,0.00008222209,0.0003584104,0.0017200862,0.00026387884,0.022564365],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994265,0.000033459542,0.000025543146,0.00013707075,0.0003411653,0.000036331043],"domain_scores_gemma":[0.9997404,0.000025898205,0.00004530079,0.00002588601,0.0001286437,0.000033909702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000310274,0.0005974885,0.0005426239,0.00094318105,0.00023228143,0.00062624254,0.00066811376,0.00059192127,0.0028809852],"category_scores_gemma":[0.0005239607,0.00026022762,0.0003181633,0.0006162455,0.00019519057,0.000614003,0.00046427082,0.00041657925,0.0014097441],"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":[0.00074812846,0.00021134649,0.0068781194,0.000738955,0.000051531068,0.00037242792,0.0002528355,0.011931405,0.5885023,0.0038333645,0.0076859575,0.37879363],"study_design_scores_gemma":[0.000085991785,0.000637463,0.049148604,0.00025309777,0.00009282645,0.0020094037,0.00021867073,0.38203835,0.519246,0.0022128709,0.043863103,0.000193634],"about_ca_topic_score_codex":0.0020478873,"about_ca_topic_score_gemma":0.0034357063,"teacher_disagreement_score":0.0028809852,"about_ca_system_score_codex":0.00032539986,"about_ca_system_score_gemma":0.00053009327,"threshold_uncertainty_score":0.009637892},"labels":[],"label_agreement":null},{"id":"W2580687518","doi":"","title":"Modout: Learning to Fuse Face and Gesture Modalities with Stochastic Regularization","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Regularization (linguistics); Computer science; Artificial intelligence; Modalities; Machine learning; Fuse (electrical); Modal; Gesture; A priori and a posteriori; Deep learning; Equivalence (formal languages); Dropout (neural networks); Pattern recognition (psychology); Mathematics; Engineering","score_opus":0.018879233564544615,"score_gpt":0.2508375835395322,"score_spread":0.2319583499749876,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2580687518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00922709,0.00061106734,0.98338413,0.00025582462,0.00014564357,0.00007981812,0.0003215643,0.0052721957,0.0007027576],"genre_scores_gemma":[0.18557544,0.00072961475,0.79618794,0.0007772081,0.0002737197,0.00036464876,0.0028980637,0.0010858612,0.012107589],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99920243,0.00019109424,0.000032685537,0.00030369574,0.00016078816,0.0001093191],"domain_scores_gemma":[0.999203,0.00034979798,0.000046910813,0.00015156706,0.00017766468,0.000070989365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028397106,0.0025539873,0.0018161944,0.00092043466,0.0006161421,0.0010501797,0.0026146576,0.0031570585,0.0047437814],"category_scores_gemma":[0.0037528046,0.0010347773,0.0018449358,0.0008701626,0.0011722227,0.0018835956,0.0029794672,0.0027520654,0.002430937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005889772,0.0002614802,0.001408401,0.00021539666,0.00031026974,0.0001349845,0.000097575125,0.14382675,0.019308489,0.005522878,0.020627188,0.80769765],"study_design_scores_gemma":[0.0000340331,0.00010147392,0.0002866846,0.000022815535,0.000031355597,0.00006201675,0.00001680521,0.9847562,0.006415864,0.006005991,0.002250384,0.000016472184],"about_ca_topic_score_codex":0.009037849,"about_ca_topic_score_gemma":0.013007672,"teacher_disagreement_score":0.009037849,"about_ca_system_score_codex":0.00071911793,"about_ca_system_score_gemma":0.0018715088,"threshold_uncertainty_score":0.017970502},"labels":[],"label_agreement":null},{"id":"W2585469550","doi":"10.1017/s0269888916000321","title":"Inter-humanoid robot interaction with emphasis on detection: a comparison study","year":2017,"lang":"en","type":"article","venue":"The Knowledge Engineering Review","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Humanoid robot; Artificial intelligence; Robot; Local binary patterns; Computer science; Cascading classifiers; Histogram; Computer vision; Robotics; False positive paradox; Feature (linguistics); Set (abstract data type); Data set; Classifier (UML); Human–computer interaction; Image (mathematics)","score_opus":0.06251306093212919,"score_gpt":0.37341950552611597,"score_spread":0.31090644459398675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2585469550","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":[0.9153098,0.030982796,0.038552504,0.00038205375,0.00043860052,0.0003223989,0.00076981995,0.0005235424,0.012718486],"genre_scores_gemma":[0.9799975,0.004461959,0.011557443,0.000103850056,0.000108742526,0.0000812073,0.00089385756,0.000054916905,0.002740515],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99756926,0.0006912915,0.00015126802,0.0005638547,0.0008422799,0.00018193826],"domain_scores_gemma":[0.9926617,0.0034548554,0.0004566722,0.0003730872,0.0028072444,0.0002464645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023306268,0.00073937,0.0008483289,0.002379262,0.00034912326,0.0009631215,0.0007281288,0.0009792602,0.0019306006],"category_scores_gemma":[0.009992896,0.0001773627,0.00062057487,0.0010790578,0.00027785343,0.0012081215,0.0006508571,0.000318315,0.001070674],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003986343,0.0009011768,0.15062967,0.0020487846,0.00091978046,0.00068154105,0.00078959437,0.00737909,0.011456075,0.00054728094,0.00674133,0.8139193],"study_design_scores_gemma":[0.00020453532,0.01057245,0.64464146,0.0007190249,0.002415761,0.007254747,0.005314187,0.2708846,0.033852123,0.0020938641,0.021799944,0.00024725724],"about_ca_topic_score_codex":0.004091593,"about_ca_topic_score_gemma":0.0034039163,"teacher_disagreement_score":0.004091593,"about_ca_system_score_codex":0.00046038622,"about_ca_system_score_gemma":0.00039902003,"threshold_uncertainty_score":0.012325704},"labels":[],"label_agreement":null},{"id":"W2586793509","doi":"10.1007/s00138-017-0898-3","title":"Tracking using Numerous Anchor Points","year":2017,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; Polytechnique Montréal","funders":"Fonds de Recherche du Québec - Santé","keywords":"Artificial intelligence; Computer vision; Computer science; Minimum bounding box; Video tracking; Point cloud; Outlier; Tracking (education); Pattern recognition (psychology); Object (grammar); Image (mathematics)","score_opus":0.04159116008688852,"score_gpt":0.3773805180813602,"score_spread":0.3357893579944717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2586793509","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012460067,0.00027079027,0.98475546,0.000058035657,0.00009781407,0.00002330691,0.000050515868,0.0007004453,0.0015836393],"genre_scores_gemma":[0.39658245,0.00062327913,0.5940714,0.000100168065,0.00017280804,0.00010164898,0.00059474504,0.0001640784,0.007589463],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983481,0.00025386448,0.00007272853,0.00071115466,0.0005113103,0.00010291748],"domain_scores_gemma":[0.99811053,0.00044461194,0.00018832393,0.0008132817,0.00036862967,0.00007460537],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013882713,0.0014153644,0.0016405064,0.0022523166,0.000920571,0.0016459799,0.0013969443,0.002501955,0.002350844],"category_scores_gemma":[0.006238991,0.0010566752,0.00077788695,0.003343185,0.0008749923,0.0030658324,0.002822037,0.001993528,0.0029542446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000560878,0.00017017548,0.004095394,0.00017808047,0.00021242867,0.00044206722,0.00031066852,0.17125009,0.051437557,0.017474005,0.004320877,0.74954784],"study_design_scores_gemma":[0.00004215945,0.0001540071,0.002436745,0.000033148626,0.000075325675,0.0003717088,0.000060683207,0.9452141,0.025919711,0.018087909,0.0075650117,0.000039445313],"about_ca_topic_score_codex":0.0024915102,"about_ca_topic_score_gemma":0.0027862634,"teacher_disagreement_score":0.002501955,"about_ca_system_score_codex":0.00052136614,"about_ca_system_score_gemma":0.00060834433,"threshold_uncertainty_score":0.007864356},"labels":[],"label_agreement":null},{"id":"W2591833252","doi":"10.1007/978-3-319-54193-8_21","title":"Unsupervised Crowd Counting","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Crowds; Artificial intelligence; Scalability; Image (mathematics); Computer vision; Limiting; Pattern recognition (psychology)","score_opus":0.03348549760387232,"score_gpt":0.2919645830608947,"score_spread":0.25847908545702236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2591833252","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":[0.004623333,0.001310329,0.9654048,0.00022619337,0.00045847008,0.00014532714,0.0010534283,0.0047322223,0.022045983],"genre_scores_gemma":[0.120695524,0.0021519968,0.7746994,0.00036714764,0.00076057896,0.00035195035,0.01121262,0.002566248,0.08719443],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985201,0.00017620578,0.000054349555,0.00063117006,0.00045371815,0.0001643835],"domain_scores_gemma":[0.99904746,0.00016893761,0.00005002251,0.00032752345,0.0003351635,0.000070969036],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011463637,0.0021482164,0.0021917548,0.003473066,0.0013426361,0.002388169,0.0026024375,0.0015931482,0.013260479],"category_scores_gemma":[0.0028895233,0.0010852675,0.0013452639,0.0023989768,0.00087088754,0.0021749847,0.0033941306,0.0016248069,0.014846942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016634013,0.00012791393,0.00081608957,0.00023426063,0.00011960801,0.0001081687,0.00009406073,0.03500177,0.0127167655,0.021962304,0.06286379,0.86578894],"study_design_scores_gemma":[0.000025524252,0.000075936696,0.002236074,0.00016028342,0.00010296157,0.00084022805,0.00010857179,0.7661693,0.03593007,0.07498562,0.119267955,0.000097421085],"about_ca_topic_score_codex":0.0033386138,"about_ca_topic_score_gemma":0.00538106,"teacher_disagreement_score":0.013260479,"about_ca_system_score_codex":0.00072739995,"about_ca_system_score_gemma":0.001222571,"threshold_uncertainty_score":0.044360757},"labels":[],"label_agreement":null},{"id":"W2593553812","doi":"10.1109/lsp.2017.2679208","title":"Adaptive Metric Learning and Probe-Specific Reranking for Person Reidentification","year":2017,"lang":"en","type":"article","venue":"IEEE Signal Processing Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"National Natural Science Foundation of China","keywords":"Metric (unit); Computer science; Artificial intelligence; Machine learning","score_opus":0.07155596924491908,"score_gpt":0.3122934657408728,"score_spread":0.24073749649595372,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2593553812","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008196592,0.00037745555,0.9887687,0.000109478744,0.000062092375,0.000080149926,0.00010446246,0.001602278,0.0006988124],"genre_scores_gemma":[0.2516613,0.00038214592,0.7417258,0.00025465546,0.0002232226,0.00025673036,0.0010077686,0.000391772,0.004096673],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99623173,0.0012552509,0.00017721104,0.0011733657,0.00094253646,0.0002199806],"domain_scores_gemma":[0.9965996,0.0008362118,0.00045560143,0.0011975303,0.00078370527,0.00012735545],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019490931,0.0019441609,0.0023993968,0.0031684027,0.00095129036,0.0010884451,0.0031045852,0.0018714871,0.0019898545],"category_scores_gemma":[0.010021752,0.00051024894,0.0010835795,0.003143767,0.001002119,0.003294844,0.0018617751,0.0015464525,0.001919167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020639936,0.00015195411,0.0024405709,0.000176696,0.0001723765,0.00015492631,0.00022102403,0.09132407,0.015703961,0.01057109,0.008923186,0.86995363],"study_design_scores_gemma":[0.000032000953,0.00020210187,0.0014256244,0.00001277992,0.000052233972,0.00049108715,0.00007718157,0.953992,0.013950105,0.021070067,0.008619543,0.000075191245],"about_ca_topic_score_codex":0.003552158,"about_ca_topic_score_gemma":0.004994269,"teacher_disagreement_score":0.003552158,"about_ca_system_score_codex":0.00092593784,"about_ca_system_score_gemma":0.00092675386,"threshold_uncertainty_score":0.010307908},"labels":[],"label_agreement":null},{"id":"W2605089300","doi":"10.1109/tvcg.2017.2734599","title":"Deep 6-DOF Tracking","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":90,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Computer vision; Video tracking; Deep learning; Tracking (education); Visualization; Object (grammar)","score_opus":0.04166425316061544,"score_gpt":0.32950336187525053,"score_spread":0.2878391087146351,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2605089300","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":[0.004288556,0.00036974778,0.99020314,0.000059857797,0.00011708897,0.00003374813,0.00042006615,0.0022434788,0.0022643253],"genre_scores_gemma":[0.243435,0.0007983016,0.7381856,0.00027274157,0.00016428214,0.00013106674,0.0032226064,0.0004990475,0.0132914195],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992342,0.00005248522,0.000026197362,0.00024346741,0.000337384,0.00010626144],"domain_scores_gemma":[0.9993837,0.00009474225,0.00007438199,0.00022351349,0.00015692115,0.000066842265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006691725,0.0013645549,0.0012562219,0.00094606157,0.0005231206,0.0011195056,0.0015700894,0.0011137835,0.005052874],"category_scores_gemma":[0.0021455563,0.0005530479,0.0008437962,0.0012844764,0.00042767017,0.0011716885,0.0021851552,0.0015532648,0.0032062847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027213249,0.00016035864,0.003119061,0.00018834937,0.0001756728,0.00015455934,0.00010934185,0.18164782,0.045420997,0.0097277,0.017230637,0.74179333],"study_design_scores_gemma":[0.000013346943,0.000057265464,0.0010483732,0.000028438684,0.000019709105,0.00019093185,0.000012672015,0.97134393,0.012824703,0.0041253967,0.010304266,0.000030899326],"about_ca_topic_score_codex":0.0074800677,"about_ca_topic_score_gemma":0.018112667,"teacher_disagreement_score":0.0074800677,"about_ca_system_score_codex":0.00060899026,"about_ca_system_score_gemma":0.0011859942,"threshold_uncertainty_score":0.01690352},"labels":[],"label_agreement":null},{"id":"W2606469090","doi":"10.1007/978-3-319-57351-9_2","title":"Person Identification Using Discriminative Visual Aesthetic","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Discriminative model; Computer science; Identification (biology); Artificial intelligence; Computer vision; Pattern recognition (psychology)","score_opus":0.06123014878419592,"score_gpt":0.34255502235717983,"score_spread":0.2813248735729839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2606469090","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04703866,0.0008574351,0.9285323,0.0001259275,0.00024290784,0.000115318995,0.00041341182,0.0029884174,0.019685602],"genre_scores_gemma":[0.54594696,0.0015559632,0.4167186,0.0003346713,0.00026188197,0.00008299947,0.0016811455,0.00069453655,0.032723162],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99956733,0.00006676274,0.000012411853,0.00015589279,0.00013190348,0.000065748805],"domain_scores_gemma":[0.9997187,0.000043274293,0.00001877296,0.00011354382,0.00008088396,0.000024697618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005130001,0.0008836742,0.0009719896,0.0012781321,0.00027082235,0.0008998386,0.00065920583,0.0006488885,0.008263442],"category_scores_gemma":[0.0008083555,0.0003220436,0.0008255572,0.0011064,0.00043812473,0.0012665505,0.0011576628,0.0006471382,0.00503858],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027187765,0.0000840688,0.0008703844,0.0001290848,0.00005891661,0.00011476125,0.000041206233,0.010543354,0.06052542,0.0036495386,0.00713464,0.9165767],"study_design_scores_gemma":[0.000035956917,0.00035114016,0.007898472,0.000076887234,0.00012990464,0.0021542336,0.00013745786,0.8999787,0.056521006,0.018244691,0.014401189,0.000070380156],"about_ca_topic_score_codex":0.0007847026,"about_ca_topic_score_gemma":0.001662739,"teacher_disagreement_score":0.008263442,"about_ca_system_score_codex":0.0002400917,"about_ca_system_score_gemma":0.00021952722,"threshold_uncertainty_score":0.027643979},"labels":[],"label_agreement":null},{"id":"W2607117265","doi":"10.23977/jaip.2016.11002","title":"Urban Road Congestion Recognition Using Multi-Feature Fusion of Traffic Images","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Histogram; Artificial intelligence; Feature (linguistics); Traffic congestion; Computer vision; Scale-invariant feature transform; Gray level; Pattern recognition (psychology); Feature extraction; Data mining; Image (mathematics); Transport engineering; Engineering","score_opus":0.12180464282793979,"score_gpt":0.3817927935406495,"score_spread":0.2599881507127097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2607117265","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3799766,0.0005267568,0.6146683,0.000115647425,0.00011482682,0.00009493945,0.00029426307,0.0014272034,0.0027814484],"genre_scores_gemma":[0.95147973,0.00020264651,0.04744501,0.000027830129,0.000032984135,0.000026906855,0.00025046687,0.000022432752,0.0005120053],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964523,0.00003449762,0.000020064872,0.00009960403,0.00013457584,0.00006611631],"domain_scores_gemma":[0.9997441,0.000034265693,0.000045490146,0.000025954703,0.00012786675,0.000022194263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035995996,0.0006529012,0.0006353338,0.0023019197,0.00024630094,0.0005506599,0.00042855614,0.00048566316,0.0005596739],"category_scores_gemma":[0.00091433397,0.00024550382,0.000706782,0.0012313378,0.00020335328,0.0012803747,0.0005878822,0.00043915038,0.0002082112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006392027,0.0003936617,0.024661137,0.00026693585,0.00026499399,0.00059556676,0.00022307645,0.10155125,0.15122576,0.0012875233,0.0024698754,0.716421],"study_design_scores_gemma":[0.000018227576,0.00018580197,0.030034354,0.000017134904,0.00012746513,0.0003678355,0.00013411511,0.93251765,0.034630783,0.0009873484,0.00093301694,0.0000463584],"about_ca_topic_score_codex":0.0029650158,"about_ca_topic_score_gemma":0.001926853,"teacher_disagreement_score":0.0029650158,"about_ca_system_score_codex":0.0003145489,"about_ca_system_score_gemma":0.00028474344,"threshold_uncertainty_score":0.0058954954},"labels":[],"label_agreement":null},{"id":"W2612189779","doi":"10.1109/wacv.2017.76","title":"PCA Based Computation of Illumination-Invariant Space for Road Detection","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computation; Computer vision; Computer science; Invariant (physics); Artificial intelligence; Pattern recognition (psychology); Computer graphics (images); Mathematics; Algorithm","score_opus":0.040176799295967576,"score_gpt":0.32650171959837077,"score_spread":0.2863249203024032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612189779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024753636,0.00016185598,0.9692481,0.00006776263,0.000039999934,0.000036342288,0.00016302746,0.0038088812,0.0017203727],"genre_scores_gemma":[0.3509123,0.00035846,0.6444124,0.00008004909,0.000059352566,0.00007377591,0.0010310177,0.00038973283,0.0026829396],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995178,0.000053839256,0.00002104603,0.000109147935,0.00022056523,0.00007754716],"domain_scores_gemma":[0.99948967,0.00010767708,0.00004914752,0.0001268106,0.0001945252,0.000032196247],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029383975,0.00088092644,0.0005792538,0.001538919,0.00037077942,0.0008139958,0.00070212927,0.000362574,0.003937817],"category_scores_gemma":[0.0015492397,0.0003147511,0.0008436325,0.0010928316,0.00034957755,0.00095785776,0.0007963292,0.00094483554,0.0016133094],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014811147,0.00013279173,0.0027159648,0.00012222686,0.00011232931,0.00013400512,0.00008894256,0.08024556,0.112137735,0.0067450767,0.0067680287,0.79064924],"study_design_scores_gemma":[0.0000072267394,0.00006693302,0.0038499315,0.000008441521,0.000028200808,0.00018224168,0.00002716021,0.9054571,0.084229514,0.0029575452,0.0031544352,0.000031245054],"about_ca_topic_score_codex":0.004793085,"about_ca_topic_score_gemma":0.006722117,"teacher_disagreement_score":0.004793085,"about_ca_system_score_codex":0.0004498846,"about_ca_system_score_gemma":0.0010041848,"threshold_uncertainty_score":0.013173282},"labels":[],"label_agreement":null},{"id":"W2612743994","doi":"10.23977/jaip.2016.11001","title":"An automatic people counting method of hotel dining with occlusion","year":2016,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Merge (version control); Computer science; Artificial intelligence; Computer vision; Support vector machine; Segmentation; Information retrieval","score_opus":0.045557689948299965,"score_gpt":0.38526740156041445,"score_spread":0.3397097116121145,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2612743994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05625017,0.0005838457,0.9369772,0.000102262275,0.000249755,0.00016345269,0.00021648283,0.0021202306,0.003336612],"genre_scores_gemma":[0.42440298,0.00088596245,0.5608194,0.00016631985,0.000271479,0.00027893588,0.0012115614,0.0002417575,0.011721629],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925655,0.00006556309,0.000037449823,0.00026547152,0.00028347856,0.000091492766],"domain_scores_gemma":[0.99967325,0.000045922923,0.000041913507,0.00003864845,0.00016949154,0.000030755808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040941424,0.0006806033,0.0011162582,0.0019020613,0.00064906443,0.00063850224,0.0010974277,0.0005519912,0.0015038635],"category_scores_gemma":[0.0007504842,0.00043557203,0.0008595953,0.0013629568,0.00027763285,0.0009054323,0.000694373,0.0005725851,0.00075424643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031336787,0.00015999758,0.0067990148,0.00021918495,0.000092949325,0.00024234207,0.0002370603,0.006966893,0.0520531,0.0018076639,0.0066063832,0.9245021],"study_design_scores_gemma":[0.00009344416,0.0003197511,0.028482059,0.00005452271,0.0002174663,0.0019197238,0.00028165375,0.8811576,0.069252424,0.002377781,0.015699845,0.00014364245],"about_ca_topic_score_codex":0.0044651995,"about_ca_topic_score_gemma":0.004670648,"teacher_disagreement_score":0.0044651995,"about_ca_system_score_codex":0.00037697103,"about_ca_system_score_gemma":0.0006210441,"threshold_uncertainty_score":0.00887841},"labels":[],"label_agreement":null},{"id":"W2619642276","doi":"10.1007/978-3-319-59876-5_26","title":"Sunshine Hours and Sunlight Direction Using Shadow Detection in a Video","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Shadow (psychology); Sunlight; Computer science; Pixel; Computer vision; Artificial intelligence; Tracking (education); Tree (set theory); Histogram; Remote sensing; Geography; Image (mathematics); Mathematics; Physics; Optics","score_opus":0.031864249929926695,"score_gpt":0.2943429173177775,"score_spread":0.2624786673878508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2619642276","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":[0.72350234,0.0022368168,0.253162,0.00012453579,0.0003583222,0.00009896168,0.003557211,0.0015950312,0.015364802],"genre_scores_gemma":[0.9117219,0.00131784,0.07894754,0.000049389757,0.00015937234,0.000031723113,0.0030226992,0.00016832593,0.004581348],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999,0.0000057056927,0.0000040174627,0.000035405214,0.00003363681,0.000021190368],"domain_scores_gemma":[0.9998702,0.000025811023,0.000018740975,0.000009939863,0.00005714571,0.000018221805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009020157,0.00048033724,0.0004294149,0.0012604923,0.00018429506,0.00052585243,0.00029268113,0.00030729157,0.0014176773],"category_scores_gemma":[0.00034074284,0.0001877815,0.0003151889,0.0013175701,0.00012015275,0.00036461718,0.000255057,0.00031280483,0.0007959726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00078608474,0.0002323548,0.052158657,0.0004541397,0.00016251668,0.00052717084,0.00026468874,0.017124487,0.19879553,0.00064441335,0.00615175,0.7226982],"study_design_scores_gemma":[0.000050799037,0.00037865533,0.39466554,0.00014964593,0.00038284014,0.001494931,0.00082863675,0.48997328,0.10017498,0.001730317,0.01004168,0.00012864622],"about_ca_topic_score_codex":0.005071705,"about_ca_topic_score_gemma":0.010824068,"teacher_disagreement_score":0.005071705,"about_ca_system_score_codex":0.0001807792,"about_ca_system_score_gemma":0.0002126516,"threshold_uncertainty_score":0.010084331},"labels":[],"label_agreement":null},{"id":"W2722816739","doi":"10.1007/s11760-017-1135-2","title":"Traffic flow detection and statistics via improved optical flow and connected region analysis","year":2017,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Optical flow; Pixel; Computer science; Flow (mathematics); Computer vision; French horn; Bounding overwatch; Traffic flow (computer networking); Artificial intelligence; Algorithm; Image (mathematics); Mathematics; Acoustics; Physics","score_opus":0.01871672676311833,"score_gpt":0.2887218330424604,"score_spread":0.2700051062793421,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2722816739","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013510246,0.00014342314,0.9850113,0.00004812286,0.00003244482,0.000030120891,0.000084945015,0.0007444563,0.00039503438],"genre_scores_gemma":[0.24860875,0.0004005465,0.747901,0.000059072037,0.0002306348,0.00014030767,0.0005415676,0.00023802991,0.001880116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999131,0.00020677136,0.000048056136,0.00022399807,0.00031645567,0.000073743475],"domain_scores_gemma":[0.9976592,0.0009743285,0.00023740961,0.00026397707,0.00079815806,0.000066949644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013139853,0.0010612579,0.0014783265,0.0052564223,0.00063221913,0.0013028671,0.0013743999,0.0009083507,0.0012833055],"category_scores_gemma":[0.005562173,0.0006746642,0.0010983642,0.0029097577,0.00070145103,0.0024368914,0.00086669426,0.0010609006,0.0007017289],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049652765,0.00034295468,0.0058448967,0.0001429294,0.00016414278,0.00012421842,0.00012388753,0.22103699,0.0349456,0.016892334,0.0036413453,0.7162441],"study_design_scores_gemma":[0.0000066025523,0.000019799412,0.0009891341,0.0000032926876,0.000020124275,0.000039960763,0.00000420422,0.9919716,0.004379621,0.0020639985,0.00048937864,0.000012227403],"about_ca_topic_score_codex":0.008085316,"about_ca_topic_score_gemma":0.006506612,"teacher_disagreement_score":0.008085316,"about_ca_system_score_codex":0.00087451603,"about_ca_system_score_gemma":0.001473414,"threshold_uncertainty_score":0.016076505},"labels":[],"label_agreement":null},{"id":"W2729454835","doi":"10.1109/bigmm.2017.53","title":"A Greedy Data Association Technique for Multiple Object Tracking","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Benchmark (surveying); Computer science; Greedy algorithm; Data association; Video tracking; Focus (optics); Overhead (engineering); Object detection; Object (grammar); Artificial intelligence; Tracking (education); Association (psychology); Computer vision; Data mining; Algorithm; Pattern recognition (psychology)","score_opus":0.13893100183212032,"score_gpt":0.3846965965475141,"score_spread":0.24576559471539378,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2729454835","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":[0.001345063,0.00014040957,0.9977642,0.000033119293,0.00003858469,0.000019680425,0.000022236982,0.00044776424,0.0001889199],"genre_scores_gemma":[0.046428103,0.0002488505,0.9512276,0.00012058174,0.000065783235,0.00012795867,0.00029411374,0.00013511264,0.0013518508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99801874,0.0003112549,0.00012813591,0.00064774835,0.00077988906,0.00011422931],"domain_scores_gemma":[0.99826044,0.0005648058,0.00019333923,0.0004495915,0.00045729737,0.00007448185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018004392,0.0010792834,0.0013233167,0.0020662989,0.0010485522,0.0012214605,0.0021150007,0.0014057577,0.0015432186],"category_scores_gemma":[0.004341038,0.0007880642,0.0012515915,0.0037903409,0.0007629305,0.0016009208,0.0021681706,0.0019430445,0.0020254732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024270217,0.00014264243,0.0015127426,0.0002015498,0.00014911493,0.00013386809,0.00017256541,0.05673602,0.04279265,0.013530169,0.0059515764,0.87843436],"study_design_scores_gemma":[0.0000567224,0.00020137704,0.0010008969,0.00003206785,0.000072253955,0.0009609709,0.000052643776,0.92436266,0.03929375,0.012821899,0.0210698,0.00007496517],"about_ca_topic_score_codex":0.0018541313,"about_ca_topic_score_gemma":0.0024207872,"teacher_disagreement_score":0.0021150007,"about_ca_system_score_codex":0.0006010145,"about_ca_system_score_gemma":0.0018567743,"threshold_uncertainty_score":0.009521723},"labels":[],"label_agreement":null},{"id":"W2736430004","doi":"10.1109/icra.2017.7989514","title":"Real-time visual tracking via robust Kernelized Correlation Filter","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer vision; Eye tracking; Tracking (education); Computer science; Video tracking; Kalman filter; Correlation; Tracking system; Kernel (algebra); Filter (signal processing); Object (grammar); Mathematics","score_opus":0.04165916067906088,"score_gpt":0.3221699389124889,"score_spread":0.280510778233428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736430004","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":[0.006368758,0.00011526976,0.99251324,0.000033007203,0.00001992343,0.00001130776,0.000014024877,0.00061676104,0.0003075992],"genre_scores_gemma":[0.49597767,0.00035636537,0.50106007,0.0000946605,0.000048364174,0.00007642758,0.00016189352,0.00015029054,0.0020742507],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989706,0.00015822545,0.000047498517,0.0003058243,0.00042647356,0.000091333925],"domain_scores_gemma":[0.9988035,0.00029215752,0.000242968,0.00020342696,0.0004050813,0.000052788866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009842282,0.0006674426,0.00095898274,0.00090254337,0.00034531413,0.00075959245,0.0010558175,0.0008760523,0.0007641496],"category_scores_gemma":[0.0038786135,0.00040489272,0.0007458729,0.0012309918,0.00045794385,0.0012666959,0.00090800115,0.0008451025,0.0003957236],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032325636,0.000105694555,0.0019514719,0.00014173877,0.0001236034,0.00017235095,0.00017904025,0.37891456,0.07475902,0.013854762,0.0030059714,0.5264686],"study_design_scores_gemma":[0.000008200298,0.000027268272,0.00030485084,0.0000030422377,0.0000089726855,0.000053418273,0.0000035011733,0.9927858,0.005441358,0.00066951645,0.0006796792,0.000014303497],"about_ca_topic_score_codex":0.00706866,"about_ca_topic_score_gemma":0.003763797,"teacher_disagreement_score":0.00706866,"about_ca_system_score_codex":0.0006640839,"about_ca_system_score_gemma":0.0012939927,"threshold_uncertainty_score":0.014055014},"labels":[],"label_agreement":null},{"id":"W2736908959","doi":"10.1109/cvprw.2017.84","title":"Person Re-identification for Improved Multi-person Multi-camera Tracking by Continuous Entity Association","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"National Science Foundation","keywords":"Computer science; Artificial intelligence; Identification (biology); Computer vision; Inference; Task (project management); Tracking (education); Biometrics; Context (archaeology); Association (psychology); Data association; Face (sociological concept); Machine learning","score_opus":0.08018545141593489,"score_gpt":0.3434193356669277,"score_spread":0.2632338842509928,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2736908959","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020617416,0.00083795976,0.97274,0.0001330151,0.00012965775,0.00006154536,0.00047463356,0.0035946288,0.0014111951],"genre_scores_gemma":[0.37475014,0.0005021372,0.6142702,0.0002542724,0.00023912618,0.00011810728,0.004604663,0.00036406968,0.004897281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99790287,0.0003831323,0.00008997588,0.0009814759,0.0004653928,0.0001771971],"domain_scores_gemma":[0.99727076,0.00069332845,0.00028954455,0.0011869537,0.00044804031,0.00011130239],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019640238,0.0010361528,0.0021789903,0.00235019,0.0008925624,0.0013793829,0.0033585443,0.0015876354,0.0023073305],"category_scores_gemma":[0.004458468,0.0005808444,0.0012164674,0.0035357755,0.00043531173,0.0024873114,0.0019411129,0.002273039,0.0029899704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047583875,0.0005617696,0.0137354145,0.00019212536,0.0004470914,0.000386669,0.00032891682,0.1337494,0.010519497,0.0063821687,0.018516842,0.81470424],"study_design_scores_gemma":[0.000015619999,0.000037468606,0.0022386014,0.000013379735,0.00004532837,0.00024327831,0.0000339275,0.9858964,0.003708906,0.0028655042,0.004877342,0.000024278314],"about_ca_topic_score_codex":0.008697387,"about_ca_topic_score_gemma":0.012137046,"teacher_disagreement_score":0.008697387,"about_ca_system_score_codex":0.00057913514,"about_ca_system_score_gemma":0.00087620167,"threshold_uncertainty_score":0.017293513},"labels":[],"label_agreement":null},{"id":"W2739931550","doi":"10.1109/icc.2017.7996697","title":"A novel video-based application for road markings detection and recognition","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Automation; Classifier (UML); Advanced driver assistance systems; Recall rate; Computer vision; Pattern recognition (psychology); Engineering","score_opus":0.049660153562576255,"score_gpt":0.3117043434960238,"score_spread":0.2620441899334475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2739931550","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054732256,0.00084720296,0.92581165,0.00014396566,0.000375354,0.00033983833,0.00085135066,0.010908403,0.00598998],"genre_scores_gemma":[0.38322908,0.00068878435,0.60465723,0.00024165002,0.00015528263,0.00017176145,0.0014505046,0.000090488364,0.009315316],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99970835,0.000026704316,0.000012932664,0.000097216776,0.000118067925,0.000036655354],"domain_scores_gemma":[0.99975544,0.00002832256,0.000016098802,0.000028293889,0.00014374524,0.000028078952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032094234,0.00057864323,0.0005244296,0.0013451672,0.00022677056,0.0004471236,0.00083764776,0.00057353603,0.0021684025],"category_scores_gemma":[0.0005001418,0.00018926554,0.00035332426,0.0007131202,0.00017010825,0.0005414106,0.00038616094,0.00045865422,0.0014364019],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028366162,0.00016317326,0.0029024365,0.0002644843,0.00004394564,0.00030803058,0.00007524895,0.0037691419,0.1655573,0.0013932048,0.007686281,0.8175531],"study_design_scores_gemma":[0.0000827824,0.0010454764,0.015704319,0.00006251806,0.0001658354,0.0020866261,0.00013953878,0.6760564,0.25806433,0.001304165,0.045181222,0.000106742475],"about_ca_topic_score_codex":0.003372496,"about_ca_topic_score_gemma":0.0050184135,"teacher_disagreement_score":0.003372496,"about_ca_system_score_codex":0.0002654013,"about_ca_system_score_gemma":0.00041419332,"threshold_uncertainty_score":0.007254064},"labels":[],"label_agreement":null},{"id":"W2752306663","doi":"10.1167/17.10.564","title":"Spatial frequency tuning for indoor scene categorization","year":2017,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal; University of Victoria","funders":"","keywords":"Categorization; Computer science; Artificial intelligence; Spatial frequency; Pattern recognition (psychology); Computer vision; Optics; Physics","score_opus":0.036965013670056085,"score_gpt":0.35499599247764,"score_spread":0.3180309788075839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2752306663","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.22556496,0.0011471794,0.76506394,0.00019917484,0.000153167,0.000048815342,0.0002885625,0.0014867367,0.0060474337],"genre_scores_gemma":[0.9002141,0.0003379766,0.095591284,0.00014531259,0.00010444945,0.000036847516,0.00042816837,0.00015985787,0.0029821808],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9996648,0.00006893506,0.000013417818,0.000116534284,0.00006698143,0.00006929548],"domain_scores_gemma":[0.9993297,0.00030291753,0.000043600594,0.00014835664,0.00012519238,0.00005026833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059750106,0.00033186816,0.0005346248,0.0009623125,0.00033289802,0.000631467,0.00058320124,0.00061490404,0.00322458],"category_scores_gemma":[0.0020167986,0.00022777948,0.00033921885,0.0007682739,0.00029322298,0.001001209,0.0008055392,0.0005086949,0.0009235797],"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":[0.0007894062,0.00022862223,0.0043405895,0.0001408322,0.000080252794,0.000048990038,0.00012677939,0.025208466,0.14991537,0.005139653,0.003415721,0.8105653],"study_design_scores_gemma":[0.000052124004,0.00017845994,0.021083238,0.000028513155,0.00008240458,0.00027248354,0.00018937362,0.9228296,0.03563271,0.015638188,0.0039736493,0.00003926168],"about_ca_topic_score_codex":0.0024359098,"about_ca_topic_score_gemma":0.002825974,"teacher_disagreement_score":0.00322458,"about_ca_system_score_codex":0.00034528397,"about_ca_system_score_gemma":0.00033324063,"threshold_uncertainty_score":0.010787308},"labels":[],"label_agreement":null},{"id":"W2753170255","doi":"10.1016/j.eswa.2017.09.006","title":"Enhanced automated body feature extraction from a 2D image using anthropomorphic measures for silhouette analysis","year":2017,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut interdisciplinaire d'innovation technologique; Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Université de Sherbrooke","keywords":"Silhouette; Computer science; Biometrics; Artificial intelligence; Feature extraction; Computer vision; Identification (biology); Feature (linguistics); Pattern recognition (psychology); Image (mathematics)","score_opus":0.03659363111986148,"score_gpt":0.37031999819403427,"score_spread":0.3337263670741728,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2753170255","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.036809918,0.00036379177,0.95979697,0.00005155385,0.00005761184,0.00006897036,0.00030848323,0.0013554629,0.0011872328],"genre_scores_gemma":[0.33821815,0.00074517756,0.65455514,0.000111687914,0.00011202522,0.00014197704,0.0010886069,0.00038827435,0.004639021],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996636,0.00004533953,0.000016733637,0.0000906792,0.00015301889,0.000030737272],"domain_scores_gemma":[0.9997073,0.00008084093,0.000033616794,0.0000458309,0.00011745091,0.000014959875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002702057,0.0006873528,0.0006469042,0.0014666079,0.00018760806,0.0006443258,0.0004348066,0.00054686854,0.0028028216],"category_scores_gemma":[0.00083765324,0.00035821783,0.0006110164,0.00081305084,0.00018361292,0.0005239612,0.00066118623,0.00039153473,0.0016857594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028815458,0.00009688628,0.0029654072,0.00026657173,0.00008840714,0.00026969097,0.00012968254,0.008318384,0.33707774,0.00087563944,0.0027524566,0.6468709],"study_design_scores_gemma":[0.000052511845,0.00036127173,0.05446781,0.00009099064,0.00018169194,0.003626928,0.00016997084,0.76485366,0.16291434,0.0022754844,0.0109058,0.00009958536],"about_ca_topic_score_codex":0.0010659681,"about_ca_topic_score_gemma":0.0023711843,"teacher_disagreement_score":0.0028028216,"about_ca_system_score_codex":0.00014130621,"about_ca_system_score_gemma":0.00030584566,"threshold_uncertainty_score":0.009376347},"labels":[],"label_agreement":null},{"id":"W2757151788","doi":"10.1109/mwscas.2017.8053241","title":"Real-time and event-triggered object detection, recognition, and tracking","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Event (particle physics); Object detection; Computer vision; Video tracking; Tracking (education); Cognitive neuroscience of visual object recognition; Object (grammar); Pattern recognition (psychology); Real-time computing; Psychology","score_opus":0.04457554126962987,"score_gpt":0.30343337343487525,"score_spread":0.2588578321652454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757151788","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013662878,0.0003516084,0.9832917,0.00008215112,0.00004532716,0.000033108085,0.000110638284,0.0009983842,0.0014241989],"genre_scores_gemma":[0.57192045,0.0011700497,0.4200725,0.00020109433,0.00017594443,0.000096357566,0.0008208378,0.00013170128,0.0054110005],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994093,0.000061159226,0.00002137375,0.00023852158,0.00020235265,0.0000674304],"domain_scores_gemma":[0.9994753,0.0001154104,0.00010876912,0.00012949064,0.00013245137,0.000038568345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005104172,0.0006199542,0.0006577696,0.0006386498,0.0002326915,0.0006760989,0.001448919,0.0006734546,0.0012044894],"category_scores_gemma":[0.0012561608,0.00027563577,0.000450802,0.00043336063,0.00037139887,0.0011999164,0.0008365577,0.0007481681,0.00066695816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036153052,0.00022874975,0.0061553363,0.0002500578,0.0001041187,0.00024208528,0.00014416406,0.08747901,0.0927171,0.012003057,0.006029886,0.7942849],"study_design_scores_gemma":[0.000008360137,0.00007926273,0.003922323,0.000019285842,0.000027659173,0.00029483007,0.000031402233,0.95242923,0.032262318,0.0046984064,0.0062072217,0.000019821939],"about_ca_topic_score_codex":0.0027639752,"about_ca_topic_score_gemma":0.0030334655,"teacher_disagreement_score":0.0027639752,"about_ca_system_score_codex":0.0005866645,"about_ca_system_score_gemma":0.00049056223,"threshold_uncertainty_score":0.0054957867},"labels":[],"label_agreement":null},{"id":"W2757165052","doi":"10.48550/arxiv.1709.09283","title":"Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Shadow (psychology); Computer science; Artificial intelligence; Benchmark (surveying); Convolutional neural network; Computer vision; Image (mathematics); Class (philosophy); Deep learning; Pattern recognition (psychology)","score_opus":0.13040749583021327,"score_gpt":0.22807130429183248,"score_spread":0.0976638084616192,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757165052","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":[0.048130732,0.00047425582,0.9459264,0.00013152903,0.00006609022,0.000056973553,0.00018992959,0.0029424713,0.0020815628],"genre_scores_gemma":[0.5871941,0.00072848366,0.40453416,0.0001916399,0.00009490265,0.00007174013,0.0009755341,0.00019673666,0.0060126535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997354,0.000020646114,0.000009319903,0.000093443734,0.000096001764,0.000045155117],"domain_scores_gemma":[0.99973696,0.00005761068,0.000030999418,0.00006709329,0.00008570298,0.000021691169],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030911952,0.0008710278,0.00074394926,0.0008904465,0.0002266063,0.0005006815,0.0010804783,0.0005477896,0.0019302001],"category_scores_gemma":[0.0008641288,0.00044412163,0.00054607657,0.0006329414,0.00033508198,0.0010186053,0.0009485265,0.00079278724,0.00077754684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028724453,0.00012910493,0.0021848078,0.00012158025,0.000109150045,0.0001525096,0.000085065505,0.093937896,0.063912734,0.0018774387,0.0042406064,0.8329619],"study_design_scores_gemma":[0.00000517792,0.000026025455,0.0010876057,0.0000065628556,0.000013367658,0.00007594036,0.000010080425,0.98608935,0.01097119,0.00092805206,0.0007804421,0.000006264017],"about_ca_topic_score_codex":0.0072562173,"about_ca_topic_score_gemma":0.011520013,"teacher_disagreement_score":0.0072562173,"about_ca_system_score_codex":0.00066614785,"about_ca_system_score_gemma":0.00064607424,"threshold_uncertainty_score":0.01442796},"labels":[],"label_agreement":null},{"id":"W2757170290","doi":"10.1080/07038992.2017.1384310","title":"A Review on Various Shadow Detection and Compensation Techniques in Remote Sensing Images","year":2017,"lang":"en","type":"review","venue":"Canadian Journal of Remote Sensing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Shadow (psychology); Computer science; Computer vision; Compensation (psychology); Process (computing); Artificial intelligence; Remote sensing; Shadow mask; Geography; Computer graphics (images)","score_opus":0.07587195515907882,"score_gpt":0.35362066231621264,"score_spread":0.27774870715713385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2757170290","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":[0.0003336316,0.9940521,0.002302496,0.00028773228,0.00034589999,0.000023208735,0.000057132616,0.000028180035,0.0025696317],"genre_scores_gemma":[0.0017749029,0.9936144,0.0029566386,0.00015331738,0.00032329056,0.000019914665,0.0000825473,0.0000085003685,0.0010666111],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9992736,0.000112648835,0.00011333774,0.00012417149,0.00033705574,0.00003928628],"domain_scores_gemma":[0.9980634,0.0010381199,0.00016436208,0.00005967408,0.0006271926,0.000047374124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012892466,0.0010657163,0.0010459098,0.004905215,0.00042054415,0.0013558342,0.0013387257,0.0011380136,0.003848868],"category_scores_gemma":[0.002818887,0.00052928773,0.00087293464,0.0061509744,0.00069832854,0.0023529294,0.00054179237,0.0011163093,0.002486319],"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":[0.000044705102,0.000060133832,0.00048493256,0.021479908,0.00007844434,0.00017014626,0.00011206925,0.00062247424,0.0023525609,0.00392,0.020631975,0.95004255],"study_design_scores_gemma":[0.000008432347,0.00014452857,0.0028818764,0.00781548,0.00022468137,0.0020884047,0.0001407395,0.0006610214,0.0031476708,0.002322068,0.9804882,0.0000769668],"about_ca_topic_score_codex":0.0030284426,"about_ca_topic_score_gemma":0.0033760078,"teacher_disagreement_score":0.004905215,"about_ca_system_score_codex":0.000794244,"about_ca_system_score_gemma":0.0014445914,"threshold_uncertainty_score":0.012875795},"labels":[],"label_agreement":null},{"id":"W2760840257","doi":"10.1007/978-3-319-68345-4_27","title":"Integrating Stereo Vision with a CNN Tracker for a Person-Following Robot","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer vision; Artificial intelligence; Convolutional neural network; Computer science; Robot; Tracking (education); Stereopsis; Psychology","score_opus":0.03824846688546087,"score_gpt":0.311950182287145,"score_spread":0.27370171540168414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2760840257","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03332001,0.00072709844,0.94982547,0.00016640917,0.00042264303,0.00013405316,0.0003330598,0.006544934,0.008526343],"genre_scores_gemma":[0.3850472,0.0006641913,0.5921771,0.00036248783,0.00015480933,0.00011016533,0.0008687938,0.00027222012,0.020343067],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970055,0.000013918241,0.000007446943,0.00012119076,0.0001027171,0.000054242933],"domain_scores_gemma":[0.99984276,0.000014532141,0.000012988419,0.000034895857,0.00007581502,0.000019024437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039158855,0.0006830189,0.0008006709,0.00053020846,0.00036007082,0.0005366144,0.0013907505,0.0012977893,0.0040987907],"category_scores_gemma":[0.0005034745,0.0006081562,0.0006351961,0.00062640966,0.0002194703,0.0006638802,0.00084216194,0.0007524854,0.002710173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030266697,0.00019262669,0.002201121,0.00014265874,0.00012973826,0.00032697764,0.00008468566,0.036710076,0.15563919,0.0019091052,0.010769424,0.7915917],"study_design_scores_gemma":[0.000026178308,0.00021683806,0.0041046576,0.000028520999,0.00008915035,0.0005264702,0.000028211445,0.9261725,0.05673239,0.0018963504,0.010139509,0.00003926016],"about_ca_topic_score_codex":0.012854553,"about_ca_topic_score_gemma":0.018129066,"teacher_disagreement_score":0.012854553,"about_ca_system_score_codex":0.0006168948,"about_ca_system_score_gemma":0.0011948996,"threshold_uncertainty_score":0.025559425},"labels":[],"label_agreement":null},{"id":"W2765933134","doi":"10.1049/iet-ipr.2016.1062","title":"Robust multi‐feature visual tracking via multi‐task kernel‐based sparse learning","year":2017,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Kernel (algebra); Artificial intelligence; Pattern recognition (psychology); Task (project management); Feature (linguistics); Computer vision; Tracking (education); Eye tracking; Feature tracking; Mathematics","score_opus":0.06793259034576017,"score_gpt":0.35185652825224295,"score_spread":0.2839239379064828,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2765933134","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0065349364,0.000076778764,0.9928827,0.00004333105,0.000012756058,0.000012240105,0.000014909104,0.00014784286,0.00027445596],"genre_scores_gemma":[0.6752505,0.00034390372,0.3212111,0.00018065363,0.00007560902,0.0001332365,0.00032170519,0.00010712196,0.0023762013],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935526,0.00013592355,0.00003507377,0.00016438692,0.00023823112,0.00007120278],"domain_scores_gemma":[0.9989497,0.0004014491,0.00018350028,0.00015494418,0.00025203175,0.000058461796],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010144874,0.0007245416,0.0010060766,0.000689352,0.00039738414,0.00075036904,0.0011428702,0.00089215516,0.0007431219],"category_scores_gemma":[0.0038021146,0.00042423504,0.0008746839,0.0009535921,0.00053571037,0.0017999002,0.001544002,0.0010436424,0.00032733395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025829425,0.00017726335,0.001818519,0.00016262288,0.00013453128,0.00011754276,0.00017483848,0.5587839,0.035278734,0.011273301,0.0021562132,0.38966426],"study_design_scores_gemma":[0.0000063548837,0.00002746636,0.00021645357,0.0000029938064,0.0000067507276,0.000026749447,0.0000056280637,0.9953519,0.0022036391,0.0018690373,0.00027610423,0.000006844636],"about_ca_topic_score_codex":0.0030653104,"about_ca_topic_score_gemma":0.0022536586,"teacher_disagreement_score":0.0030653104,"about_ca_system_score_codex":0.00047769584,"about_ca_system_score_gemma":0.00082863955,"threshold_uncertainty_score":0.0060949326},"labels":[],"label_agreement":null},{"id":"W2768261820","doi":"10.1007/978-3-319-70353-4_40","title":"Counting Large Flocks of Birds Using Videos Acquired with Hand-Held Devices","year":2017,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Flock; Computer science; Wildlife; Ground truth; Segmentation; Computer vision; Artificial intelligence; Ecology; Biology","score_opus":0.03305754142590199,"score_gpt":0.2979585461368701,"score_spread":0.2649010047109681,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768261820","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.79062724,0.0023309113,0.19299081,0.00011963961,0.00028761005,0.00029098472,0.0026116646,0.00168851,0.009052585],"genre_scores_gemma":[0.8032623,0.0011492705,0.18400507,0.00011772153,0.00017124119,0.00011078983,0.0027951533,0.000109138775,0.008279408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971575,0.00001955606,0.000013109563,0.00014898219,0.00007004504,0.000032527],"domain_scores_gemma":[0.9994566,0.00027769984,0.00006940603,0.000052548945,0.000085357686,0.000058445516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026226707,0.0008862831,0.00075167516,0.001462573,0.00030205742,0.00046693216,0.0010053038,0.00075429963,0.0028176676],"category_scores_gemma":[0.00082354934,0.00044506288,0.00031924743,0.0008981477,0.000188825,0.0009692781,0.00043783395,0.0002501653,0.001012602],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008234675,0.0003293284,0.061927557,0.00062720594,0.0004542756,0.0004655159,0.00031350786,0.018225906,0.1794543,0.0005358853,0.006007391,0.7308357],"study_design_scores_gemma":[0.00009296521,0.0013076501,0.39393443,0.00021921218,0.00053864997,0.0028030768,0.0010380681,0.48803222,0.098850116,0.0032924048,0.009754353,0.00013693335],"about_ca_topic_score_codex":0.0043725898,"about_ca_topic_score_gemma":0.0137671735,"teacher_disagreement_score":0.0043725898,"about_ca_system_score_codex":0.00024075554,"about_ca_system_score_gemma":0.0001870745,"threshold_uncertainty_score":0.009425998},"labels":[],"label_agreement":null},{"id":"W2768363758","doi":"10.1007/s11760-017-1203-7","title":"MARO: matrix rank optimization for the detection of small-size moving objects from aerial camera platforms","year":2017,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Lagrange multiplier; Aerial image; Principal component analysis; Backtracking; Artificial intelligence; Domain (mathematical analysis); Computer vision; Matrix (chemical analysis); Algorithm; Mathematics; Mathematical optimization; Image (mathematics)","score_opus":0.023497630879091368,"score_gpt":0.29769221978628124,"score_spread":0.2741945889071899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768363758","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":[0.0038795292,0.00028202316,0.9933595,0.00017570081,0.00009133921,0.00004792544,0.00018090011,0.0012966607,0.00068642007],"genre_scores_gemma":[0.13732125,0.00036682998,0.8529009,0.0002867583,0.00028570625,0.00031886247,0.0011177423,0.0005951277,0.0068068667],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992354,0.00025063814,0.000026571652,0.00014612667,0.00025842566,0.000082884704],"domain_scores_gemma":[0.99883014,0.0005917485,0.00011915116,0.00012020437,0.00025690155,0.00008171805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011355755,0.0019997985,0.0015980881,0.00081291486,0.00044946454,0.00093331473,0.0014557233,0.0015641169,0.0036942041],"category_scores_gemma":[0.0043438626,0.0006879315,0.00087185774,0.00089262665,0.0007486531,0.0011613988,0.0013438546,0.0016596193,0.0018454206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009996176,0.00023158603,0.00087375403,0.00048082948,0.00024630962,0.0001921348,0.000120406774,0.38846767,0.024129776,0.015457998,0.044666793,0.524133],"study_design_scores_gemma":[0.000020693016,0.00007489974,0.00018839617,0.000007441931,0.000007502824,0.000031225238,0.000011043878,0.9931258,0.0019605716,0.002717163,0.0018441904,0.000011191976],"about_ca_topic_score_codex":0.0042055473,"about_ca_topic_score_gemma":0.0052985493,"teacher_disagreement_score":0.0042055473,"about_ca_system_score_codex":0.0004166422,"about_ca_system_score_gemma":0.0011500518,"threshold_uncertainty_score":0.012358367},"labels":[],"label_agreement":null},{"id":"W2768513778","doi":"10.1049/iet-cvi.2017.0209","title":"Using mel‐frequency audio features from footstep sound and spatial segmentation techniques to improve frame‐based moving object detection","year":2017,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Computer vision; Artificial intelligence; Computer science; Object detection; Segmentation; Frame (networking); Object (grammar); Frame rate","score_opus":0.028547420207531836,"score_gpt":0.34181091448671813,"score_spread":0.3132634942791863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2768513778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09349664,0.00092957966,0.9017537,0.00008724622,0.00015127345,0.00011891858,0.0001224906,0.0013079566,0.0020322339],"genre_scores_gemma":[0.32745776,0.0010020771,0.66728336,0.00010587603,0.000112593676,0.00008218768,0.00043938457,0.00013279423,0.0033839736],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99962425,0.00002456581,0.000026452313,0.000085637104,0.00019626321,0.000042829557],"domain_scores_gemma":[0.9993511,0.0001830225,0.00008613086,0.00008834646,0.00026226667,0.000029254905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003661551,0.0007337606,0.00050089246,0.002543774,0.00025318246,0.00069068203,0.00059653545,0.00058945117,0.0013344993],"category_scores_gemma":[0.0012632217,0.00026877737,0.0006257175,0.0011959159,0.00029691972,0.00095942145,0.00042575793,0.00051782076,0.00074491964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026489486,0.00012820367,0.0021192,0.0002250274,0.00005853565,0.00027464205,0.00012845309,0.0067215865,0.29775319,0.0012159536,0.0009028327,0.69020754],"study_design_scores_gemma":[0.000066733395,0.0010164228,0.03615671,0.0000727374,0.0003262598,0.0018732656,0.0002577678,0.5661334,0.3725322,0.0021911766,0.019264324,0.000109040986],"about_ca_topic_score_codex":0.0024881684,"about_ca_topic_score_gemma":0.0043715,"teacher_disagreement_score":0.002543774,"about_ca_system_score_codex":0.00025227258,"about_ca_system_score_gemma":0.00035569147,"threshold_uncertainty_score":0.004947424},"labels":[],"label_agreement":null},{"id":"W2769897157","doi":"10.1007/s00138-018-0981-4","title":"Robust UAV-based tracking using hybrid classifiers","year":2018,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Cancer Institute","keywords":"Artificial intelligence; Computer science; Tracking (education); Computer vision; Pattern recognition (psychology); Psychology","score_opus":0.062330736210984346,"score_gpt":0.3413555411205664,"score_spread":0.27902480490958204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2769897157","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026256124,0.00073758775,0.97074234,0.00006169023,0.00014794625,0.000024757996,0.0000557521,0.0006451307,0.00132857],"genre_scores_gemma":[0.57735103,0.0008050455,0.41534746,0.00017059261,0.0001612871,0.00008016641,0.00048679227,0.00015837581,0.005439263],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99902034,0.00016240304,0.000053441476,0.00029609023,0.0003396521,0.00012808015],"domain_scores_gemma":[0.99885356,0.00035742304,0.00012759554,0.00021725422,0.0003982688,0.0000457853],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011515355,0.0008247006,0.0015106867,0.001233577,0.00052691734,0.0015124867,0.0009800853,0.001424899,0.0009281637],"category_scores_gemma":[0.00238796,0.00056414667,0.0008443749,0.0012526356,0.00031777497,0.001586101,0.0009978157,0.00089161296,0.0010660687],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005290005,0.0001526494,0.004013682,0.00012347833,0.00029968185,0.00012847468,0.00008211119,0.14285412,0.062828034,0.0042517595,0.0033828546,0.7813541],"study_design_scores_gemma":[0.000009393334,0.000057337696,0.0011957242,0.000011456565,0.000043956465,0.00009758253,0.00001114191,0.98449624,0.011868145,0.0011053707,0.0010906324,0.000012988205],"about_ca_topic_score_codex":0.0026352808,"about_ca_topic_score_gemma":0.002933706,"teacher_disagreement_score":0.0026352808,"about_ca_system_score_codex":0.0004950603,"about_ca_system_score_gemma":0.0006030116,"threshold_uncertainty_score":0.0060899854},"labels":[],"label_agreement":null},{"id":"W2772533415","doi":"10.3141/2645-12","title":"Optimized Video Tracking for Automated Vehicle Turning Movement Counts","year":2017,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Intersection (aeronautics); Trajectory; Cluster analysis; Ranging; Movement (music); Artificial intelligence; Tracking (education); Generalization; Computer vision; Set (abstract data type); Real-time computing; Simulation; Mathematics; Engineering","score_opus":0.1427789599956605,"score_gpt":0.44305290800674985,"score_spread":0.3002739480110893,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2772533415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032271396,0.00008928378,0.96561086,0.000020365242,0.000021096337,0.000050397874,0.0001196853,0.001113678,0.0007032187],"genre_scores_gemma":[0.37044466,0.00014677236,0.6267624,0.000040465035,0.000034470002,0.00017219229,0.0008301086,0.00020440172,0.001364608],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99907684,0.00014906371,0.0000563328,0.000292291,0.00034598476,0.000079455545],"domain_scores_gemma":[0.9987501,0.00043935023,0.00023531215,0.00011857553,0.00042401775,0.0000327192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070620637,0.0007946032,0.00081290904,0.0016742216,0.00028115188,0.0006499339,0.00091179804,0.00039378143,0.000799821],"category_scores_gemma":[0.0033060373,0.00034676018,0.00044652558,0.0011692602,0.0002447265,0.00072032266,0.00034414316,0.0005368965,0.0004054259],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029848574,0.00019471277,0.008188058,0.00013614031,0.000119997865,0.00007867453,0.000119319324,0.26862603,0.038618427,0.002220146,0.0020701764,0.67932975],"study_design_scores_gemma":[0.000011126563,0.000049354734,0.00530705,0.000011008225,0.00001867127,0.00004988934,0.000021343725,0.9785231,0.01449063,0.0005546264,0.00094484154,0.000018447847],"about_ca_topic_score_codex":0.013084232,"about_ca_topic_score_gemma":0.01307647,"teacher_disagreement_score":0.013084232,"about_ca_system_score_codex":0.0009277332,"about_ca_system_score_gemma":0.00076787284,"threshold_uncertainty_score":0.026016176},"labels":[],"label_agreement":null},{"id":"W2775646422","doi":"10.1109/iros.2017.8206227","title":"Modular tracking framework: A fast library for high precision tracking","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Modular design; BitTorrent tracker; Computer science; Tracking (education); Artificial intelligence; Tracking system; Robotics; Software deployment; Computer vision; Plug-in; Robot; Software engineering; Eye tracking; Kalman filter; Operating system","score_opus":0.05314091921031202,"score_gpt":0.33073045140923407,"score_spread":0.2775895321989221,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2775646422","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":[0.00026828522,0.000105367246,0.94261456,0.000020734391,0.00004538752,0.00004733321,0.0005581379,0.05534066,0.0009994735],"genre_scores_gemma":[0.013082674,0.0003939534,0.95415246,0.0001522926,0.000066908775,0.00046624205,0.006387702,0.018705426,0.0065922453],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985487,0.00014414657,0.00011639041,0.00033652468,0.0006965519,0.00015754713],"domain_scores_gemma":[0.99852175,0.00033428858,0.00015195955,0.00047506863,0.00039682645,0.000120034856],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018682976,0.0023766954,0.0014916103,0.002725378,0.0009549337,0.001936714,0.0050665685,0.0019467338,0.027490547],"category_scores_gemma":[0.0041732695,0.0020219004,0.0028193735,0.0018101757,0.0006641994,0.0032172487,0.0035843416,0.0032165071,0.028374404],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048001565,0.00015192523,0.0014420844,0.00084420503,0.00025338616,0.00040169182,0.00029221864,0.045394555,0.03134788,0.04028269,0.17874365,0.7003657],"study_design_scores_gemma":[0.00019008694,0.00019750124,0.0013008158,0.00023579241,0.00011305937,0.0013941019,0.00004727439,0.5155451,0.060580697,0.03070586,0.38933057,0.00035909264],"about_ca_topic_score_codex":0.004517174,"about_ca_topic_score_gemma":0.0051768953,"teacher_disagreement_score":0.027490547,"about_ca_system_score_codex":0.0010322949,"about_ca_system_score_gemma":0.0021001247,"threshold_uncertainty_score":0.09196508},"labels":[],"label_agreement":null},{"id":"W2778775889","doi":"10.1109/iccv.2017.265","title":"Efficient Online Local Metric Adaptation via Negative Samples for Person Re-identification","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Science North","funders":"Army Research Office; National Science Foundation","keywords":"Metric (unit); Computer science; Matching (statistics); Adaptation (eye); Contrast (vision); Identification (biology); Set (abstract data type); Artificial intelligence; Margin (machine learning); Similarity (geometry); Cover (algebra); Machine learning; Mathematical optimization; Algorithm; Mathematics; Image (mathematics); Statistics","score_opus":0.1233692533169497,"score_gpt":0.3578340000265187,"score_spread":0.23446474670956902,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2778775889","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027228685,0.0005893894,0.9647127,0.00017872253,0.00012892719,0.000104105166,0.00014686868,0.0050549638,0.0018557134],"genre_scores_gemma":[0.5532338,0.00037238703,0.43246925,0.0005230531,0.00016353175,0.00031301245,0.0017865135,0.0008781204,0.01026038],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9974377,0.0006656845,0.00011147358,0.0009718898,0.00062065606,0.00019252826],"domain_scores_gemma":[0.9976457,0.0005739553,0.00020928614,0.00093212887,0.00049002044,0.0001489814],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015200755,0.0021466366,0.0024737536,0.0008522787,0.00058206695,0.00096651254,0.0027895044,0.0014648994,0.0036163619],"category_scores_gemma":[0.0078327395,0.00066224125,0.0008454545,0.0008337819,0.0010525775,0.0026912738,0.0032727274,0.002402086,0.003913933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00077907986,0.00034694804,0.0030441126,0.00021220294,0.00010473767,0.00031290788,0.00022963696,0.12419156,0.023070665,0.0059343055,0.0115093775,0.8302645],"study_design_scores_gemma":[0.000024662055,0.00014491363,0.0008068001,0.00001413582,0.000017551238,0.00035749542,0.00007424737,0.97809726,0.010450307,0.006949221,0.003032601,0.0000306964],"about_ca_topic_score_codex":0.0024104351,"about_ca_topic_score_gemma":0.003020556,"teacher_disagreement_score":0.0036163619,"about_ca_system_score_codex":0.0006947589,"about_ca_system_score_gemma":0.0008331755,"threshold_uncertainty_score":0.012097955},"labels":[],"label_agreement":null},{"id":"W2779464705","doi":"10.1109/iccv.2017.548","title":"Moving Object Detection in Time-Lapse or Motion Trigger Image Sequences Using Low-Rank and Invariant Sparse Decomposition","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":29,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Background subtraction; Computer science; Outlier; Computer vision; Sparse approximation; Invariant (physics); Pattern recognition (psychology); Object detection; Benchmark (surveying); Representation (politics); Pixel; Mathematics","score_opus":0.03850510906511011,"score_gpt":0.32439721253090503,"score_spread":0.2858921034657949,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2779464705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05427084,0.00037538484,0.9434202,0.000113638314,0.00004297703,0.00004916156,0.00018295024,0.00078037585,0.0007644862],"genre_scores_gemma":[0.41059145,0.0010086658,0.58345854,0.00017607395,0.00017260763,0.00010716674,0.002079294,0.000113073904,0.0022931239],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99965596,0.000052044135,0.000020083618,0.0000927772,0.00013402308,0.00004507151],"domain_scores_gemma":[0.99949753,0.00013304046,0.00011708923,0.000074734344,0.00013374536,0.000043717555],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044431852,0.0007310574,0.0008681113,0.0016311808,0.00023122763,0.0005954607,0.00067928433,0.00066820957,0.000610677],"category_scores_gemma":[0.0015874079,0.00025289654,0.0005901997,0.001378807,0.0003914269,0.00082930905,0.00057071075,0.0007972422,0.00046436815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004943746,0.0003103592,0.003431969,0.00025347213,0.00014665602,0.00041482874,0.000153864,0.1156353,0.1674631,0.0062449444,0.00430567,0.7011454],"study_design_scores_gemma":[0.000013718697,0.00011951445,0.003210843,0.000013896448,0.000028635417,0.000244777,0.00004083007,0.96939296,0.022907663,0.0024384286,0.0015685648,0.000020173758],"about_ca_topic_score_codex":0.0023522133,"about_ca_topic_score_gemma":0.0031289642,"teacher_disagreement_score":0.0023522133,"about_ca_system_score_codex":0.0002854937,"about_ca_system_score_gemma":0.00047105225,"threshold_uncertainty_score":0.004677117},"labels":[],"label_agreement":null},{"id":"W2782660655","doi":"10.1145/3182179","title":"Representation, Analysis, and Recognition of 3D Humans","year":2018,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Exploit; Representation (politics); Focus (optics); Human–computer interaction; External Data Representation; Artificial intelligence; Facial recognition system; Data science; Face (sociological concept); Taxonomy (biology); Machine learning; Pattern recognition (psychology)","score_opus":0.058801613580330744,"score_gpt":0.3587469231175845,"score_spread":0.29994530953725373,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2782660655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0053626625,0.003187278,0.9880299,0.0002569141,0.00008810476,0.00006419465,0.00031553904,0.00048334434,0.0022120774],"genre_scores_gemma":[0.16321541,0.016338628,0.811695,0.00036973626,0.00047364004,0.00025063337,0.0026250589,0.00020728263,0.004824637],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991203,0.00017385243,0.000055624892,0.00023885819,0.0003548829,0.000056471126],"domain_scores_gemma":[0.99954444,0.00011397575,0.000074974574,0.00014475701,0.00010169146,0.000020207232],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005478149,0.0008631961,0.00074810465,0.0027835695,0.00028403333,0.0018788942,0.0011222328,0.0010585276,0.0023576228],"category_scores_gemma":[0.0018698259,0.00040350962,0.0010538975,0.0021559217,0.0009960519,0.00201444,0.0016370392,0.00073034223,0.0016037419],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007244209,0.00006239231,0.0020328583,0.00064690877,0.0000828491,0.00020000339,0.00038227398,0.029513307,0.035102308,0.023627914,0.009002607,0.8992741],"study_design_scores_gemma":[0.000024575002,0.00026536902,0.014113898,0.0004600618,0.00014481734,0.0033292747,0.0009841091,0.7348884,0.053608533,0.08732089,0.10464949,0.00021060935],"about_ca_topic_score_codex":0.0017992483,"about_ca_topic_score_gemma":0.0020112463,"teacher_disagreement_score":0.0027835695,"about_ca_system_score_codex":0.00029050632,"about_ca_system_score_gemma":0.0005949209,"threshold_uncertainty_score":0.007887065},"labels":[],"label_agreement":null},{"id":"W2786213927","doi":"10.1109/crv.2017.31","title":"Scale-Corrected Background Modeling","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Senstar (Canada)","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Frame (networking); Video tracking; Image resolution; Scale (ratio); Tracking (education); Calibration; Precision and recall; Analytics; Object detection; Object (grammar); Pattern recognition (psychology); Data mining","score_opus":0.08544769517222671,"score_gpt":0.3431931127861283,"score_spread":0.2577454176139016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2786213927","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":[0.013034219,0.00045175452,0.98158455,0.00008975453,0.00010594203,0.000053281667,0.0002100645,0.0027145795,0.001755888],"genre_scores_gemma":[0.34774753,0.001391599,0.6375013,0.00038006264,0.00016491614,0.00011949134,0.0023554356,0.001229461,0.009110237],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904877,0.00013169518,0.000039330476,0.0003034562,0.00034805766,0.00012876146],"domain_scores_gemma":[0.9990864,0.00013661319,0.00007820483,0.00030296945,0.00035995946,0.000035862227],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000951377,0.0011824758,0.000983365,0.0015672724,0.00049151335,0.0017264533,0.0019027321,0.0010291835,0.00234275],"category_scores_gemma":[0.002835404,0.00054454716,0.0014567481,0.0015258917,0.0004227895,0.0014362903,0.0010315699,0.0014254574,0.0022373123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002468898,0.00012512607,0.0033981279,0.00016203793,0.00020446666,0.00025432603,0.00014720946,0.28310302,0.0507454,0.011259179,0.009954756,0.6403994],"study_design_scores_gemma":[0.0000066675098,0.000018060504,0.0009805621,0.000010779339,0.000034038414,0.00015623185,0.0000156536,0.9805314,0.011725914,0.002074593,0.0044313585,0.000014722822],"about_ca_topic_score_codex":0.010377423,"about_ca_topic_score_gemma":0.011529215,"teacher_disagreement_score":0.010377423,"about_ca_system_score_codex":0.00084547466,"about_ca_system_score_gemma":0.0009356483,"threshold_uncertainty_score":0.020634055},"labels":[],"label_agreement":null},{"id":"W2787176632","doi":"10.1109/crv.2017.55","title":"Person Following Robot Using Selected Online Ada-Boosting with Stereo Camera","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Computer vision; Artificial intelligence; Robustness (evolution); Robot; Boosting (machine learning); Mobile robot; Social robot; Stereo camera; Task (project management); Robot control; Engineering","score_opus":0.09113412998519271,"score_gpt":0.3395870362719064,"score_spread":0.24845290628671368,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2787176632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08606082,0.00073433865,0.90113896,0.00019266672,0.00021669683,0.00018952313,0.00030826396,0.007818559,0.003340089],"genre_scores_gemma":[0.4964045,0.00017229379,0.4972844,0.00033168335,0.0000720347,0.00009557493,0.0012120776,0.00025425322,0.004173214],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999243,0.00012821946,0.000024929735,0.00026826104,0.00022189139,0.000113667],"domain_scores_gemma":[0.99936527,0.00009300531,0.00006035331,0.00014313892,0.0002617264,0.000076426346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013058821,0.00087934104,0.0014352642,0.0007683951,0.00046928955,0.0005593286,0.001565686,0.00083192054,0.0015682351],"category_scores_gemma":[0.0013081235,0.00041807728,0.00079427724,0.00052533165,0.00025659802,0.0005484923,0.00070006266,0.0007679006,0.0014538268],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006101829,0.00063185464,0.0060515525,0.000106475076,0.00017048063,0.0001888687,0.00009782843,0.12738954,0.025503006,0.0011251679,0.01364633,0.82447875],"study_design_scores_gemma":[0.000022366914,0.00010690582,0.0013322693,0.000007006586,0.000015972539,0.00011245799,0.000015730502,0.9909395,0.005096071,0.00069191447,0.0016498757,0.000010003065],"about_ca_topic_score_codex":0.0075281956,"about_ca_topic_score_gemma":0.008835295,"teacher_disagreement_score":0.0075281956,"about_ca_system_score_codex":0.00061273127,"about_ca_system_score_gemma":0.00091976085,"threshold_uncertainty_score":0.014968753},"labels":[],"label_agreement":null},{"id":"W2789722309","doi":"10.3390/rs10040510","title":"Total Variation Regularization Term-Based Low-Rank and Sparse Matrix Representation Model for Infrared Moving Target Tracking","year":2018,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"State Key Laboratory of Networking and Switching Technology; China Scholarship Council; Beijing University of Posts and Telecommunications; National Natural Science Foundation of China","keywords":"Computer science; Sparse approximation; Artificial intelligence; Term (time); Regularization (linguistics); Computer vision; Robust principal component analysis; Matrix (chemical analysis); Infrared; Total variation denoising; Pattern recognition (psychology); Algorithm; Principal component analysis; Noise reduction; Optics; Physics","score_opus":0.031429407058198376,"score_gpt":0.307876687722733,"score_spread":0.27644728066453467,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789722309","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0022418452,0.0001144957,0.9969975,0.000053583484,0.000017084874,0.000011595204,0.000016924167,0.00012317568,0.00042386993],"genre_scores_gemma":[0.45787194,0.0011149745,0.5264164,0.0002774203,0.00017628072,0.000344613,0.0007094973,0.0002083142,0.012880552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994093,0.00014405964,0.000028327502,0.000128322,0.00023569798,0.000054237837],"domain_scores_gemma":[0.9994772,0.00021414203,0.000077734454,0.000049743972,0.00015855272,0.000022643815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009841955,0.00074380456,0.00093493535,0.00048277422,0.00031727794,0.00080462976,0.0013849548,0.000892835,0.0013897589],"category_scores_gemma":[0.002282129,0.00034964402,0.00086047826,0.00085755705,0.00062315515,0.001146029,0.0007391625,0.0016923927,0.00066704105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008332768,0.00006055751,0.0005744545,0.00011560675,0.000056486257,0.000075525924,0.000094066345,0.8471776,0.008975686,0.028034698,0.0030441363,0.111707926],"study_design_scores_gemma":[0.0000019974339,0.000013556139,0.00005000896,0.0000016634079,0.000003646839,0.000009833159,0.0000015688568,0.9979504,0.00039508016,0.0012503064,0.00031822402,0.0000038379803],"about_ca_topic_score_codex":0.0068976195,"about_ca_topic_score_gemma":0.0048624654,"teacher_disagreement_score":0.0068976195,"about_ca_system_score_codex":0.00059282884,"about_ca_system_score_gemma":0.0011297265,"threshold_uncertainty_score":0.013714969},"labels":[],"label_agreement":null},{"id":"W2789901503","doi":"10.1049/iet-its.2017.0329","title":"Deep learning‐based real‐time fine‐grained pedestrian recognition using stream processing","year":2018,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Fundamental Research Funds for the Central Universities; PetroChina Innovation Foundation","keywords":"Pedestrian; Computer science; Artificial intelligence; Pedestrian detection; Deep learning; Stream processing; Computer vision; Pattern recognition (psychology); Real-time computing; Engineering; Transport engineering; Operating system","score_opus":0.06115951194126923,"score_gpt":0.3062039023659343,"score_spread":0.24504439042466508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2789901503","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":[0.07695049,0.00029926022,0.91563034,0.00018203299,0.00018133696,0.00009587434,0.0003178553,0.004002473,0.0023403051],"genre_scores_gemma":[0.7321118,0.00040326017,0.25942606,0.000228082,0.00008875286,0.00008841916,0.0010735119,0.00013718731,0.0064429943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981517,0.000016048549,0.000010412143,0.00005757256,0.000060214123,0.000040628216],"domain_scores_gemma":[0.9997466,0.000040774245,0.00003094138,0.000049151295,0.000100349185,0.00003216355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030445826,0.0007864727,0.00056053285,0.0006570247,0.0002145483,0.0005812869,0.0008648109,0.00043768602,0.0015718858],"category_scores_gemma":[0.00063449395,0.0002902953,0.00053508236,0.00051576464,0.00023458674,0.00083070213,0.00056910014,0.00065011723,0.00059650757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00082379393,0.0004846674,0.0075320723,0.00012522294,0.00012526939,0.00032277775,0.00008228642,0.19118707,0.082988776,0.0033926002,0.01000246,0.702933],"study_design_scores_gemma":[0.000008745509,0.000060639373,0.00093299843,0.00000405344,0.000015489171,0.000050175397,0.000011165916,0.97850823,0.018237391,0.0009674241,0.001194673,0.000009017701],"about_ca_topic_score_codex":0.006039871,"about_ca_topic_score_gemma":0.007430384,"teacher_disagreement_score":0.006039871,"about_ca_system_score_codex":0.00068340276,"about_ca_system_score_gemma":0.0007451113,"threshold_uncertainty_score":0.012009382},"labels":[],"label_agreement":null},{"id":"W2793098633","doi":"","title":"A new approach to robust human motion detection","year":2005,"lang":"en","type":"article","venue":"PolyU Institutional Research Archive (Hong Kong Polytechnic University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Human motion; Artificial intelligence; Motion (physics); Computer vision","score_opus":0.0922313296411679,"score_gpt":0.32298858557567917,"score_spread":0.23075725593451127,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793098633","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0006283481,0.00009787322,0.99857414,0.000037389644,0.000030053709,0.000014206457,0.0000147665605,0.00016384711,0.00043933565],"genre_scores_gemma":[0.034465488,0.0003641361,0.96171695,0.000118110576,0.00015281493,0.00008399578,0.00010492155,0.00012684797,0.0028666784],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9985226,0.0001688044,0.00007641362,0.0005254715,0.0006352375,0.00007145638],"domain_scores_gemma":[0.9990269,0.0002903201,0.00012014117,0.00021631125,0.00031094655,0.000035339603],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010472718,0.0011065908,0.0014772846,0.0019270447,0.00042444238,0.0012842683,0.00191237,0.0013995509,0.0023480821],"category_scores_gemma":[0.0026471876,0.00061073666,0.0013079167,0.001012985,0.0008661564,0.002183748,0.0012752411,0.001593978,0.001142067],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013020594,0.00010776833,0.00057762314,0.00026327142,0.0001954824,0.000158066,0.00018035324,0.0900003,0.083025575,0.067796975,0.0042055715,0.7533588],"study_design_scores_gemma":[0.000022707522,0.00012405048,0.0006221238,0.00002963614,0.000070636816,0.0003425421,0.000029924146,0.92947465,0.025342144,0.023071105,0.020810382,0.000060132315],"about_ca_topic_score_codex":0.0013855783,"about_ca_topic_score_gemma":0.0010996206,"teacher_disagreement_score":0.0023480821,"about_ca_system_score_codex":0.00062735693,"about_ca_system_score_gemma":0.0005360331,"threshold_uncertainty_score":0.007855177},"labels":[],"label_agreement":null},{"id":"W2793885786","doi":"10.1109/globalsip.2017.8308671","title":"HEVC intra features for human detection","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Histogram; Decoding methods; Artificial intelligence; Pixel; Support vector machine; Bitstream; Pattern recognition (psychology); Object detection; Computer vision; Quantization (signal processing); Algorithm; Image (mathematics)","score_opus":0.04504393569897545,"score_gpt":0.3641478825607423,"score_spread":0.31910394686176685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2793885786","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026894294,0.0015308063,0.9488533,0.0003195881,0.00031688248,0.00023279728,0.0015095373,0.0059311134,0.014411656],"genre_scores_gemma":[0.49069598,0.0016132919,0.4726425,0.00046390924,0.0003268931,0.0003866747,0.0058649774,0.0009697354,0.02703603],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994867,0.00004896795,0.000017003693,0.000064240645,0.0003288053,0.000054140433],"domain_scores_gemma":[0.99936706,0.00013163227,0.000038893326,0.00012396445,0.00031087952,0.000027741253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043364574,0.00076758536,0.0004718987,0.0020015647,0.0002536739,0.0006743637,0.000687607,0.0005179501,0.0059017097],"category_scores_gemma":[0.002034453,0.00018416875,0.00027098792,0.00090768153,0.0002337027,0.0006630831,0.0004502218,0.0007203272,0.0025172327],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026535566,0.00010677665,0.0009951801,0.00012080736,0.00004237495,0.000101750185,0.000035852136,0.012398601,0.08199792,0.003849984,0.02902244,0.87106293],"study_design_scores_gemma":[0.00006738983,0.0002684874,0.012706963,0.00010669044,0.000086609594,0.0010698652,0.000052390336,0.68513125,0.21393827,0.00659333,0.079853475,0.00012522709],"about_ca_topic_score_codex":0.002916726,"about_ca_topic_score_gemma":0.0060402174,"teacher_disagreement_score":0.0059017097,"about_ca_system_score_codex":0.00035846946,"about_ca_system_score_gemma":0.00052470685,"threshold_uncertainty_score":0.019743264},"labels":[],"label_agreement":null},{"id":"W2794847029","doi":"10.1007/978-3-030-01252-6_36","title":"A Framework for Evaluating 6-DOF Object Trackers","year":2018,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"BitTorrent tracker; Computer science; Artificial intelligence; Robustness (evolution); Computer vision; Ground truth; Pipeline (software); Video tracking; Object (grammar); Synthetic data; Fiducial marker; Eye tracking","score_opus":0.07635977411340937,"score_gpt":0.3993683626942123,"score_spread":0.3230085885808029,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2794847029","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":[0.0092117535,0.00046129496,0.98677397,0.000056554596,0.000051218834,0.00017055304,0.00026612115,0.0020557344,0.00095283607],"genre_scores_gemma":[0.22904277,0.00041362052,0.76601046,0.00008313425,0.0000824961,0.0003510576,0.0013129798,0.0003691606,0.0023344446],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9922367,0.0018719059,0.00059293606,0.0014123351,0.0033794902,0.0005066222],"domain_scores_gemma":[0.99207205,0.0027581782,0.0007631103,0.0013427123,0.002614236,0.00044971734],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01036644,0.0028165313,0.0029039462,0.004344547,0.0012928803,0.0039962297,0.004206328,0.0038690914,0.00452936],"category_scores_gemma":[0.02625751,0.0010872046,0.0017485706,0.0024090675,0.0012316052,0.0036265238,0.0039137304,0.0021010842,0.0017669062],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000886465,0.000412173,0.0076446016,0.0003101386,0.00042410532,0.00018410412,0.0001614616,0.37860855,0.021537045,0.021395037,0.004576827,0.5638594],"study_design_scores_gemma":[0.000043556443,0.00031781098,0.0014216917,0.000032803993,0.000063540974,0.000110885834,0.000032088064,0.98193526,0.0051122047,0.009262667,0.0016323134,0.00003522397],"about_ca_topic_score_codex":0.012256702,"about_ca_topic_score_gemma":0.013330572,"teacher_disagreement_score":0.012256702,"about_ca_system_score_codex":0.0018328609,"about_ca_system_score_gemma":0.0023398276,"threshold_uncertainty_score":0.054823637},"labels":[],"label_agreement":null},{"id":"W2796085209","doi":"10.1007/978-3-319-89656-4_38","title":"Real-Time Deep Learning Pedestrians Classification on a Micro-Controller","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Deep learning; Pedestrian detection; Artificial neural network; Software deployment; Raspberry pi; Object detection; Machine learning; Real-time computing; Pedestrian; Pattern recognition (psychology); Embedded system; Operating system; Internet of Things; Engineering","score_opus":0.026116084078971945,"score_gpt":0.28022851556938616,"score_spread":0.25411243149041424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2796085209","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":[0.153807,0.0005706645,0.8258037,0.0003681545,0.00058129913,0.00015788968,0.00051468576,0.01188044,0.0063162916],"genre_scores_gemma":[0.8509912,0.00012470582,0.14025274,0.00017846921,0.000060806862,0.000076591416,0.0005324901,0.0001282622,0.007654859],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997758,0.0000147414885,0.0000057690627,0.0000940362,0.00005836519,0.00005122528],"domain_scores_gemma":[0.9997621,0.00005790285,0.000014372653,0.000044389475,0.000085250016,0.000035996818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002761271,0.0008047022,0.00071959884,0.0003346571,0.0003161227,0.0005388372,0.0012227742,0.00057707523,0.006353785],"category_scores_gemma":[0.00058140577,0.00036034934,0.00030022426,0.00036298885,0.0002037387,0.0005774755,0.00060438324,0.0010159633,0.0013871824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010031975,0.00044408298,0.0030445831,0.00009945166,0.00008616671,0.00019459565,0.000050430266,0.113117404,0.057696037,0.0019944808,0.013138458,0.8091311],"study_design_scores_gemma":[0.0000122174715,0.000080150305,0.0008793984,0.0000036179342,0.000010360332,0.00002868056,0.000007500982,0.9904383,0.0070077227,0.00064376986,0.0008815921,0.000006717748],"about_ca_topic_score_codex":0.011456759,"about_ca_topic_score_gemma":0.019670615,"teacher_disagreement_score":0.011456759,"about_ca_system_score_codex":0.0006938947,"about_ca_system_score_gemma":0.0008599402,"threshold_uncertainty_score":0.02278012},"labels":[],"label_agreement":null},{"id":"W2798401272","doi":"10.1109/mmsp.2018.8547125","title":"MV-YOLO: Motion Vector-Aided Tracking by Semantic Object Detection","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Computer vision; Video tracking; BitTorrent tracker; Artificial intelligence; Tracking (education); Object detection; Eye tracking; Simplicity; Object (grammar); Construct (python library); Retargeting; Tracking system; Kalman filter; Pattern recognition (psychology)","score_opus":0.03299537247924055,"score_gpt":0.2991602007638184,"score_spread":0.2661648282845778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2798401272","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0059101023,0.0005583096,0.987854,0.000051166888,0.000108489025,0.000049757276,0.00025580297,0.004220911,0.0009915036],"genre_scores_gemma":[0.1270946,0.0007093707,0.8638354,0.00014675957,0.00013860362,0.00013875029,0.002919296,0.0004323254,0.004584925],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942696,0.00008234823,0.00002494065,0.0001958502,0.00020826465,0.00006171599],"domain_scores_gemma":[0.99950576,0.00014369222,0.000067212575,0.00013413976,0.00011623506,0.000032876873],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010484238,0.0011096732,0.0012374682,0.0016710081,0.00031273713,0.0009587429,0.0014736181,0.00074608094,0.0019252241],"category_scores_gemma":[0.0025795065,0.00042280622,0.0005106788,0.0016021591,0.00039997746,0.0011431124,0.0015359538,0.0010377973,0.0017098979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041820057,0.00015875041,0.002476515,0.00021099416,0.00013832875,0.000101548525,0.000095185904,0.05335941,0.03861177,0.007861358,0.015134822,0.8814331],"study_design_scores_gemma":[0.00003654684,0.00008404407,0.0010704811,0.000026448968,0.000022384675,0.00011026564,0.000019177509,0.9718409,0.013998138,0.0037418844,0.009025939,0.000023857036],"about_ca_topic_score_codex":0.0048422012,"about_ca_topic_score_gemma":0.008537688,"teacher_disagreement_score":0.0048422012,"about_ca_system_score_codex":0.00036859317,"about_ca_system_score_gemma":0.0010541611,"threshold_uncertainty_score":0.009627998},"labels":[],"label_agreement":null},{"id":"W2799107345","doi":"10.1007/978-3-030-01261-8_12","title":"Domain Adaptation Through Synthesis for Unsupervised Person Re-identification","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":245,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"","keywords":"Computer science; Identification (biology); Artificial intelligence; Domain adaptation; Adaptation (eye); Domain (mathematical analysis); Scale (ratio); Training set; Pattern recognition (psychology); Machine learning; Computer vision; Mathematics","score_opus":0.06365457729492724,"score_gpt":0.2993547881583341,"score_spread":0.23570021086340687,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2799107345","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0046374956,0.0001925894,0.9926152,0.00003231902,0.00006564471,0.000022662136,0.00008324711,0.0011434059,0.0012074157],"genre_scores_gemma":[0.2054443,0.0006141189,0.7774212,0.00019410142,0.00011437521,0.00013094537,0.0012505391,0.0006279401,0.014202388],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951637,0.000099403114,0.000017222123,0.0001984661,0.000109539025,0.000058956666],"domain_scores_gemma":[0.9995455,0.00014087422,0.000025040184,0.00016713042,0.00010175376,0.000019676077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005892054,0.0007156494,0.0009280426,0.0006283431,0.00028000213,0.0005821552,0.00086483976,0.00084419333,0.0041216854],"category_scores_gemma":[0.001244054,0.0004156796,0.0008957918,0.0007576452,0.0003976592,0.00082664157,0.0013372214,0.00113338,0.004474168],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031145674,0.00012012457,0.0006198016,0.00012826094,0.00010286535,0.00011958462,0.00010917895,0.07482547,0.08283928,0.0057597226,0.0060839956,0.82898027],"study_design_scores_gemma":[0.000015897042,0.00007118745,0.00082538696,0.000018701856,0.000042042477,0.00025331147,0.000056767494,0.9412118,0.04075976,0.008126242,0.00859422,0.000024583056],"about_ca_topic_score_codex":0.0013448418,"about_ca_topic_score_gemma":0.002197801,"teacher_disagreement_score":0.0041216854,"about_ca_system_score_codex":0.00023459435,"about_ca_system_score_gemma":0.00036124533,"threshold_uncertainty_score":0.013788402},"labels":[],"label_agreement":null},{"id":"W2802389629","doi":"10.1007/978-3-319-90053-7_9","title":"Using the Internet of Things to Monitor Human and Animal Uses of Industrial Linear Features","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Open source hardware; Wildlife; Interoperability; Internet of Things; The Internet; Open source; Computer security; World Wide Web; Operating system; Ecology","score_opus":0.07316514079629334,"score_gpt":0.3373029184132535,"score_spread":0.26413777761696017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802389629","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34762615,0.007007722,0.4898094,0.00084122457,0.0008919138,0.00024216087,0.0051912093,0.0051763016,0.14321385],"genre_scores_gemma":[0.76022786,0.004238445,0.19642363,0.0005020495,0.00023007562,0.00014407077,0.0029993884,0.00029594038,0.034938615],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99988043,0.000011629577,0.000004600839,0.000034630353,0.00005775164,0.000010872404],"domain_scores_gemma":[0.99986386,0.000047274556,0.00002149033,0.00002060822,0.000036404286,0.000010415142],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011394742,0.0004345837,0.00021245869,0.0010803584,0.00023124415,0.000492744,0.00042066904,0.0004800272,0.002140011],"category_scores_gemma":[0.00032608493,0.00016724704,0.000298148,0.001162447,0.00014974526,0.0008423011,0.00036787527,0.0002640973,0.0010042024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019363737,0.000106763684,0.03201239,0.0004912816,0.0001559777,0.0004430982,0.00041413485,0.0050018146,0.07725951,0.003711638,0.014185707,0.8660241],"study_design_scores_gemma":[0.000047238173,0.00093871116,0.24418023,0.0005493787,0.0006663979,0.006739466,0.0021606577,0.24547945,0.22177584,0.03194851,0.24522066,0.00029362433],"about_ca_topic_score_codex":0.00089708896,"about_ca_topic_score_gemma":0.0033264773,"teacher_disagreement_score":0.002140011,"about_ca_system_score_codex":0.00012250789,"about_ca_system_score_gemma":0.00008757262,"threshold_uncertainty_score":0.0071590543},"labels":[],"label_agreement":null},{"id":"W2802961479","doi":"10.1049/iet-ipr.2017.1055","title":"Background subtraction using Gaussian–Bernoulli restricted Boltzmann machine","year":2018,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Institute for Information and Communications Technology Promotion","keywords":"Background subtraction; Bernoulli's principle; Computer science; Boltzmann machine; Artificial intelligence; Subtraction; Frame (networking); Pixel; Gaussian; Variance (accounting); Mixture model; Restricted Boltzmann machine; Gaussian process; Pattern recognition (psychology); Generative model; Computer vision; Algorithm; Mathematics; Generative grammar; Artificial neural network","score_opus":0.05016170115045385,"score_gpt":0.3473259039171228,"score_spread":0.297164202766669,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2802961479","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":[0.0051751575,0.00020640995,0.9925776,0.00008132065,0.000048254653,0.000022469685,0.000025598345,0.000986949,0.000876206],"genre_scores_gemma":[0.36506996,0.00045557253,0.62860596,0.00044628384,0.00011313577,0.00016472858,0.00040638907,0.00039603747,0.004342043],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99918157,0.00019715854,0.000034511824,0.00023790935,0.00025083867,0.00009795406],"domain_scores_gemma":[0.9995116,0.00020798217,0.000039036004,0.00007278295,0.0001357466,0.000032831857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008888833,0.001047185,0.0015984534,0.00070585613,0.00048304745,0.0010066422,0.0024522464,0.0012232122,0.0015127878],"category_scores_gemma":[0.002498939,0.0005693888,0.0011946486,0.000771809,0.0006640969,0.0014614412,0.0012778912,0.0019020449,0.00086110126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033457763,0.00013049909,0.0010813649,0.00015135387,0.00021052541,0.00016869279,0.00012292505,0.56193066,0.026627598,0.021803552,0.0041428767,0.3832954],"study_design_scores_gemma":[0.000008101652,0.000013017344,0.0001084019,0.0000043394684,0.000010126245,0.000034774614,0.0000052718156,0.99046665,0.0039506806,0.0045561353,0.0008314665,0.000011064208],"about_ca_topic_score_codex":0.0044602463,"about_ca_topic_score_gemma":0.0035106475,"teacher_disagreement_score":0.0044602463,"about_ca_system_score_codex":0.00081552565,"about_ca_system_score_gemma":0.001023002,"threshold_uncertainty_score":0.008868575},"labels":[],"label_agreement":null},{"id":"W2804443156","doi":"10.1007/s00371-018-1563-1","title":"Object tracking based on Huber loss function","year":2018,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Cancer Institute; National Natural Science Foundation of China","keywords":"Computer graphics; Computer science; Computer graphics (images); Computer vision; Object (grammar); Artificial intelligence; Tracking (education); Function (biology); Psychology","score_opus":0.032014491467703625,"score_gpt":0.32341738096657513,"score_spread":0.2914028894988715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2804443156","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":[0.008411888,0.00023250189,0.9901821,0.000058300568,0.00003518978,0.000013936854,0.0000244449,0.00041970547,0.00062200706],"genre_scores_gemma":[0.53086644,0.0013788198,0.4478293,0.00027663689,0.00017980946,0.00013566313,0.0005829712,0.00024912416,0.018501198],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994955,0.00009248494,0.000023938888,0.00017085175,0.00015922553,0.000058031674],"domain_scores_gemma":[0.99935514,0.00020425099,0.00006389982,0.00012896527,0.00021241154,0.00003534946],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015881404,0.000871646,0.001303114,0.0012790377,0.0005220305,0.0011024515,0.001252854,0.001492548,0.0012654936],"category_scores_gemma":[0.0020537789,0.00043542965,0.00056658406,0.0010413523,0.0005464533,0.002157342,0.0010471761,0.0010119631,0.0007657624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004530841,0.00017758724,0.002476594,0.00013889831,0.00014712673,0.00014313284,0.0001626429,0.30732754,0.032312617,0.035077117,0.006082453,0.61550117],"study_design_scores_gemma":[0.0000051155635,0.000027462147,0.00045607288,0.000004263856,0.000016737862,0.000041848583,0.0000050225653,0.9923314,0.0041410765,0.0023884457,0.0005735069,0.000009141256],"about_ca_topic_score_codex":0.004151776,"about_ca_topic_score_gemma":0.0028999352,"teacher_disagreement_score":0.004151776,"about_ca_system_score_codex":0.00092198513,"about_ca_system_score_gemma":0.00096643524,"threshold_uncertainty_score":0.00839895},"labels":[],"label_agreement":null},{"id":"W2805622579","doi":"10.1007/s11042-018-6198-8","title":"Visual tracking via robust multi-task multi-feature joint sparse representation","year":2018,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Feature (linguistics); Task (project management); Sparse approximation; Joint (building); Representation (politics); Pattern recognition (psychology); Computer vision","score_opus":0.10884832705415881,"score_gpt":0.3523420443751323,"score_spread":0.24349371732097347,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2805622579","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0035517034,0.00015025091,0.9955331,0.000078956175,0.000034066452,0.000013609962,0.000044643224,0.00025229208,0.00034131148],"genre_scores_gemma":[0.3473483,0.0007777658,0.6444418,0.0003809641,0.00022008049,0.00015750524,0.0011524792,0.00022187972,0.0052992026],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921966,0.00015990475,0.000040883046,0.00022443598,0.0002549179,0.00010017671],"domain_scores_gemma":[0.9986382,0.00051455403,0.00021439121,0.00025098887,0.0003112803,0.000070556525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010517506,0.0010861326,0.0014725568,0.0010117876,0.0004390253,0.0010728755,0.0014529625,0.0016117056,0.001160727],"category_scores_gemma":[0.00450543,0.0006806867,0.0010153851,0.0017722675,0.0005801959,0.0015805666,0.0017451737,0.0016326926,0.00089471415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00049440295,0.00025319817,0.0012064822,0.00023826722,0.00023291474,0.00014142616,0.00014780514,0.30016795,0.049223706,0.011735585,0.0063392,0.62981915],"study_design_scores_gemma":[0.000011649972,0.000035925153,0.0003109969,0.000007079515,0.000019285822,0.000047624875,0.000009390558,0.99212134,0.00338541,0.0033193033,0.00072003,0.000011934367],"about_ca_topic_score_codex":0.0044403053,"about_ca_topic_score_gemma":0.0045630764,"teacher_disagreement_score":0.0044403053,"about_ca_system_score_codex":0.00041126116,"about_ca_system_score_gemma":0.0010317586,"threshold_uncertainty_score":0.008828938},"labels":[],"label_agreement":null},{"id":"W2810450673","doi":"10.1109/uic-atc.2017.8397650","title":"MapReduce-based techniques for multiple object tracking in video analytics","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Speedup; Scalability; Video tracking; Cloud computing; Analytics; Artificial intelligence; Node (physics); Object (grammar); Distributed computing; Real-time computing; Data mining; Database; Parallel computing; Operating system","score_opus":0.07487489570285195,"score_gpt":0.367142623535799,"score_spread":0.2922677278329471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2810450673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009789067,0.000422728,0.9834925,0.00020435808,0.00007474783,0.000108122884,0.00017336285,0.0035368076,0.0021982153],"genre_scores_gemma":[0.30215326,0.00081838487,0.69111854,0.00014379274,0.00011394431,0.00030151938,0.0011543428,0.0004958731,0.0037004177],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999418,0.00010779079,0.000033175584,0.00011178824,0.0002670327,0.00006234379],"domain_scores_gemma":[0.9994209,0.00017228993,0.000036478843,0.00015286026,0.00017872248,0.000038848528],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008074666,0.0007023552,0.0005604332,0.0010065228,0.0009488256,0.000870416,0.0015736686,0.00038342227,0.0016256332],"category_scores_gemma":[0.0015484529,0.00033501047,0.0009662253,0.0015189382,0.00048927637,0.0011254433,0.0012055305,0.000934923,0.00061712303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041612017,0.00035669506,0.0026448278,0.0005676455,0.00030658234,0.00028361398,0.0006485287,0.265605,0.047375947,0.036736093,0.023183635,0.6218753],"study_design_scores_gemma":[0.00005437596,0.00008069453,0.0012351363,0.000019280957,0.000042219322,0.0001747499,0.00020462586,0.923026,0.022980828,0.03425249,0.017894667,0.000034820794],"about_ca_topic_score_codex":0.010857412,"about_ca_topic_score_gemma":0.013154874,"teacher_disagreement_score":0.010857412,"about_ca_system_score_codex":0.0007262577,"about_ca_system_score_gemma":0.0015184395,"threshold_uncertainty_score":0.021588385},"labels":[],"label_agreement":null},{"id":"W2811025443","doi":"10.1109/icra.2018.8462884","title":"End-to-end Learning of Multi-sensor 3D Tracking by Detection","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Exploit; Lidar; Tracking (education); Artificial intelligence; Matching (statistics); Computer vision; End-to-end principle; Remote sensing","score_opus":0.050857315987970664,"score_gpt":0.32607328444748224,"score_spread":0.2752159684595116,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2811025443","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015465309,0.00021104599,0.9779553,0.00016867295,0.0000592687,0.00007391424,0.00023253217,0.0047067655,0.0011272421],"genre_scores_gemma":[0.4623039,0.00030785167,0.52314055,0.000559939,0.00011089817,0.00023026568,0.0026070517,0.0005270864,0.010212518],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989267,0.00011016543,0.000038467704,0.0005497841,0.00021898503,0.00015589899],"domain_scores_gemma":[0.9987872,0.00041409745,0.00013593987,0.0003341527,0.00022368727,0.000104854116],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013023093,0.0021862336,0.0021267587,0.00087428186,0.0005732644,0.001478623,0.0039291373,0.0028615817,0.003390779],"category_scores_gemma":[0.0034442965,0.0013635865,0.0011227583,0.0012938079,0.00091162085,0.002507682,0.0024548091,0.002797485,0.0028299678],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035477112,0.0004025098,0.0028038998,0.00014485806,0.00016254895,0.00019574788,0.00009044563,0.4284938,0.016030477,0.004245567,0.007852405,0.5392229],"study_design_scores_gemma":[0.00000727815,0.000035886886,0.0002221125,0.0000058846686,0.000006394781,0.000040226874,0.000008994946,0.9929576,0.0030453873,0.0030937714,0.00057063176,0.0000058451915],"about_ca_topic_score_codex":0.009589773,"about_ca_topic_score_gemma":0.014401152,"teacher_disagreement_score":0.009589773,"about_ca_system_score_codex":0.0011787327,"about_ca_system_score_gemma":0.0018217466,"threshold_uncertainty_score":0.019067883},"labels":[],"label_agreement":null},{"id":"W2864882831","doi":"10.1142/s1793351x18400135","title":"A Fast-Iterative Data Association Technique for Multiple Object Tracking","year":2018,"lang":"en","type":"article","venue":"International Journal of Semantic Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Video tracking; Benchmark (surveying); Overhead (engineering); Artificial intelligence; Focus (optics); Data association; Computer vision; Tracking (education); Set (abstract data type); Object (grammar); Data set; Iterative method; Object detection; Data mining; Real-time computing; Pattern recognition (psychology); Algorithm; Filter (signal processing)","score_opus":0.0547362788536908,"score_gpt":0.3703555261339156,"score_spread":0.3156192472802248,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2864882831","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":[0.0014758983,0.00016258503,0.997292,0.000035211007,0.000045452198,0.00003566179,0.00003782449,0.0006428649,0.0002725442],"genre_scores_gemma":[0.030216683,0.00021743345,0.9672876,0.000070278365,0.00004403783,0.00013639977,0.00037225964,0.00014955174,0.0015058382],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967637,0.0005072692,0.00023562103,0.0008988809,0.0014215215,0.00017301383],"domain_scores_gemma":[0.99666685,0.0007668055,0.00036470045,0.0007919312,0.0012849569,0.00012466153],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024562178,0.0013822201,0.0015929594,0.002825262,0.0014174626,0.0015603683,0.0027885959,0.0014062284,0.0020499884],"category_scores_gemma":[0.0077456357,0.00085299206,0.0020707184,0.0045470507,0.0008579783,0.0021547864,0.0034100553,0.0030919546,0.002506314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018784647,0.00015414767,0.0020034548,0.0001996338,0.00022712133,0.00015520355,0.00036412678,0.04136684,0.03510718,0.009829305,0.0062530814,0.90415215],"study_design_scores_gemma":[0.000029238363,0.00015863948,0.0014717075,0.000045409903,0.000069896996,0.0011872,0.00011037927,0.92393404,0.040112164,0.01041947,0.022365136,0.0000967189],"about_ca_topic_score_codex":0.0039776973,"about_ca_topic_score_gemma":0.005701962,"teacher_disagreement_score":0.0039776973,"about_ca_system_score_codex":0.00070537935,"about_ca_system_score_gemma":0.0026388564,"threshold_uncertainty_score":0.012989879},"labels":[],"label_agreement":null},{"id":"W2885185277","doi":"10.1145/3293353.3293369","title":"Online Illumination Invariant Moving Object Detection by Generative Neural Network","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Invariant (physics); Benchmark (surveying); Batch processing; Computer vision; Representation (politics); Generative model; Pattern recognition (psychology); Image (mathematics); Generative grammar; Mathematics","score_opus":0.0330031697146688,"score_gpt":0.29834079083816856,"score_spread":0.26533762112349973,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885185277","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02693874,0.00060441357,0.96782494,0.00017454755,0.000058421734,0.000048912676,0.00011054739,0.0029907806,0.0012487974],"genre_scores_gemma":[0.5882291,0.00038471172,0.4032485,0.00050306146,0.00013786921,0.000115625255,0.0010060802,0.00046848154,0.005906614],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995622,0.00005693877,0.0000150684355,0.00020386241,0.000099933124,0.0000619391],"domain_scores_gemma":[0.9994254,0.00025056966,0.000078978745,0.0001155499,0.0000938848,0.000035639954],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063083804,0.0012228569,0.0013873555,0.00075030036,0.00028088313,0.0007179445,0.0022418802,0.0011045865,0.0014580208],"category_scores_gemma":[0.0016845681,0.0007532566,0.0010577424,0.000806339,0.0007168384,0.00094264874,0.0010771706,0.0016469031,0.0006581333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025800918,0.00017358777,0.0023742483,0.00009710138,0.00017165086,0.00013925813,0.000068414935,0.42858222,0.015825864,0.0037002284,0.0049947673,0.54361475],"study_design_scores_gemma":[0.00000475814,0.000010550709,0.00021864737,0.0000020888006,0.000007176805,0.000024262035,0.0000020219723,0.99723035,0.001350167,0.0009800403,0.0001662606,0.0000035828748],"about_ca_topic_score_codex":0.006384738,"about_ca_topic_score_gemma":0.009927149,"teacher_disagreement_score":0.006384738,"about_ca_system_score_codex":0.0010103292,"about_ca_system_score_gemma":0.0007459042,"threshold_uncertainty_score":0.012695134},"labels":[],"label_agreement":null},{"id":"W2885205791","doi":"10.1049/iet-cvi.2018.5376","title":"Robust tracking of multiple objects in video by adaptive fusion of subband particle filters","year":2018,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer vision; Artificial intelligence; Particle filter; Computer science; Robustness (evolution); Wavelet; Video tracking; Tracking (education); Wavelet transform; Frame (networking); Eye tracking; Filter (signal processing); Object (grammar)","score_opus":0.038810591601188715,"score_gpt":0.2827385296543283,"score_spread":0.24392793805313956,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885205791","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015936427,0.00016590543,0.98304176,0.000040797713,0.000053775384,0.00002180972,0.000017834456,0.00026498546,0.00045677004],"genre_scores_gemma":[0.38681802,0.000582194,0.60959417,0.00010796078,0.00008466684,0.000091119895,0.00019080004,0.00006285345,0.0024682097],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950016,0.00007188576,0.000035318462,0.00013133093,0.00022154498,0.00003989489],"domain_scores_gemma":[0.99940586,0.0001913938,0.00008890424,0.00010552053,0.00018241878,0.000025906285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010728973,0.0008444972,0.0009785505,0.0010015296,0.0002614527,0.0007159737,0.00074174826,0.00097503915,0.00042016647],"category_scores_gemma":[0.0022704795,0.00042606547,0.0010774318,0.0009804211,0.0003210773,0.00074493885,0.0007722603,0.0008023068,0.0002654795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029203916,0.00015347188,0.0022792032,0.00012567254,0.00024513534,0.00013917263,0.00016698072,0.44771433,0.07088924,0.0072891037,0.001341591,0.46936408],"study_design_scores_gemma":[0.0000070137166,0.000046397665,0.00061874144,0.00000387027,0.00002041027,0.00002692589,0.0000060426505,0.99248546,0.0053717834,0.0007058615,0.00069862406,0.0000089058885],"about_ca_topic_score_codex":0.0040811663,"about_ca_topic_score_gemma":0.0034365929,"teacher_disagreement_score":0.0040811663,"about_ca_system_score_codex":0.0005860271,"about_ca_system_score_gemma":0.0006856792,"threshold_uncertainty_score":0.008114815},"labels":[],"label_agreement":null},{"id":"W2885403272","doi":"10.1109/bmsb.2018.8436673","title":"Novel Automatic Human-Height Measurement Using a Digital Camera","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"","keywords":"Computer science; Computer vision; Digital camera; Artificial intelligence; Computer graphics (images)","score_opus":0.11941745704445496,"score_gpt":0.3353701540451647,"score_spread":0.21595269700070974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885403272","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":[0.059123788,0.00059621054,0.93494123,0.000094530355,0.00013968522,0.00010327479,0.00019228068,0.0014911583,0.0033178576],"genre_scores_gemma":[0.36085227,0.0003716081,0.63516396,0.00013381075,0.000085371255,0.00008598677,0.00025488873,0.000045506065,0.0030066143],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9992638,0.00011166914,0.000021744638,0.00022965195,0.000318416,0.00005464318],"domain_scores_gemma":[0.999634,0.00007370767,0.00004780331,0.00007410031,0.0001215605,0.000048850063],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003088335,0.0005250606,0.00050578284,0.000988369,0.00033688598,0.00047955083,0.0008444605,0.0005156629,0.0015439946],"category_scores_gemma":[0.00071501435,0.0002852306,0.00030027778,0.00069694896,0.00029207818,0.0007806594,0.00096120103,0.00042306367,0.0006406023],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030197008,0.00013610092,0.0039681774,0.00024502535,0.00007961517,0.00014670141,0.00015488316,0.005490347,0.33969784,0.00237115,0.0033051227,0.6441031],"study_design_scores_gemma":[0.00017444002,0.0006551562,0.020970399,0.000043565233,0.00014229574,0.0021495773,0.000117837895,0.6275292,0.3279778,0.0017576283,0.018322874,0.00015922057],"about_ca_topic_score_codex":0.0016794365,"about_ca_topic_score_gemma":0.0025921492,"teacher_disagreement_score":0.0016794365,"about_ca_system_score_codex":0.00030653723,"about_ca_system_score_gemma":0.0004688203,"threshold_uncertainty_score":0.0051652193},"labels":[],"label_agreement":null},{"id":"W2885848781","doi":"10.1007/978-3-319-98352-3_25","title":"A Benchmark of Motion Detection Algorithms for Static Camera: Application on CDnet 2012 Dataset","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Benchmark (surveying); Ranging; Computer science; Computer vision; Artificial intelligence; Motion (physics); Simple (philosophy); Structure from motion; Algorithm; Geography; Cartography","score_opus":0.027165499554023406,"score_gpt":0.2852775053319123,"score_spread":0.2581120057778889,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885848781","genre_codex":"dataset","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27573976,0.021463469,0.06081914,0.0020500822,0.005737416,0.0021745812,0.5256105,0.0784642,0.02794087],"genre_scores_gemma":[0.07947892,0.0016072701,0.06510202,0.00033994816,0.0003122375,0.0004897334,0.84416616,0.0010531757,0.0074504954],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99671054,0.0003999126,0.0002586587,0.001359667,0.0008484708,0.00042270782],"domain_scores_gemma":[0.99788034,0.00035043005,0.00012733841,0.00064128224,0.00081039587,0.0001902929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028849437,0.0057347007,0.0025401819,0.006208087,0.0017281017,0.0022641013,0.004740133,0.0031663508,0.005280109],"category_scores_gemma":[0.0059926216,0.0007715958,0.0017005638,0.0048971986,0.0005985461,0.0024963669,0.0017829584,0.0016061005,0.008769392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012194228,0.0013686315,0.009048578,0.0017353108,0.00089348387,0.00030027248,0.000086798565,0.02430786,0.008088429,0.00089526817,0.6403154,0.31174064],"study_design_scores_gemma":[0.0014325975,0.0017249206,0.056950036,0.00084771,0.001001909,0.0029672957,0.0010957223,0.59433913,0.057560675,0.0057366015,0.27595842,0.00038503564],"about_ca_topic_score_codex":0.059804663,"about_ca_topic_score_gemma":0.09024549,"teacher_disagreement_score":0.059804663,"about_ca_system_score_codex":0.00219693,"about_ca_system_score_gemma":0.0023964264,"threshold_uncertainty_score":0.11891317},"labels":[],"label_agreement":null},{"id":"W2885992220","doi":"10.1049/iet-cvi.2017.0554","title":"Extended cuckoo search‐based kernel correlation filter for abrupt motion tracking","year":2018,"lang":"en","type":"article","venue":"IET Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Wilfrid Laurier University; University of Ottawa","funders":"Zhengzhou University; National Natural Science Foundation of China","keywords":"Cuckoo search; BitTorrent tracker; Artificial intelligence; Computer vision; Computer science; Kernel (algebra); Eye tracking; Tracking (education); Gaussian; Mathematics; Algorithm","score_opus":0.042922499060591424,"score_gpt":0.34455179650659007,"score_spread":0.30162929744599865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2885992220","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":[0.010204,0.0002719728,0.9883929,0.00004232311,0.000035894638,0.00001816701,0.000019318513,0.00029519945,0.0007202281],"genre_scores_gemma":[0.5569559,0.00059434195,0.43461737,0.00011107403,0.00007209778,0.00014124798,0.0002851435,0.00016978993,0.0070530637],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995431,0.00008386147,0.000023585782,0.000099380944,0.0001989061,0.000051197047],"domain_scores_gemma":[0.9991786,0.00027315144,0.000101240264,0.00009848121,0.0002936771,0.000054839293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006797178,0.00063477305,0.00080995227,0.000870722,0.00042425684,0.0005567733,0.0010754557,0.0009261068,0.0011796947],"category_scores_gemma":[0.00224279,0.00031556416,0.00063093123,0.001188199,0.00048598388,0.00086578913,0.00064580434,0.000793063,0.00042836257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023733915,0.00007669936,0.002273055,0.00015070692,0.00012285874,0.00015583841,0.00016584594,0.6004504,0.026389917,0.028072571,0.0038925544,0.3380122],"study_design_scores_gemma":[0.0000041894377,0.000015406295,0.0001761705,0.0000022393413,0.000004385304,0.000023200468,0.0000026936727,0.9972154,0.0011767106,0.0006551149,0.000716758,0.0000076281244],"about_ca_topic_score_codex":0.013037058,"about_ca_topic_score_gemma":0.009570733,"teacher_disagreement_score":0.013037058,"about_ca_system_score_codex":0.0009592916,"about_ca_system_score_gemma":0.0014736862,"threshold_uncertainty_score":0.025922358},"labels":[],"label_agreement":null},{"id":"W2886007972","doi":"10.5220/0006832301030110","title":"Autonomous Trail Following using a Pre-trained Deep Neural Network","year":2018,"lang":"en","type":"article","venue":"Proceedings of the 15th International Conference on Informatics in Control, Automation and Robotics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Artificial neural network; Artificial intelligence","score_opus":0.03358950275421544,"score_gpt":0.30793274119516384,"score_spread":0.2743432384409484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886007972","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1208519,0.00038525887,0.85826427,0.00024413296,0.0003695661,0.00019981194,0.00030264645,0.0062724105,0.013109937],"genre_scores_gemma":[0.85902935,0.00012963731,0.123730086,0.00016159154,0.000031733773,0.00008983149,0.00036255093,0.00008293631,0.016382208],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987185,0.00000506958,0.0000053360136,0.00004626639,0.00003378297,0.000037658632],"domain_scores_gemma":[0.999795,0.000032254862,0.000018911902,0.000041092426,0.00008222864,0.00003052207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021376889,0.0006138213,0.00066678843,0.00038209333,0.0006090945,0.0005476583,0.0012948791,0.0008487186,0.003441898],"category_scores_gemma":[0.00043158847,0.0005768693,0.0004386638,0.00031710826,0.0002936143,0.00051755627,0.0008372711,0.00087027124,0.0011364889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041636016,0.00034239938,0.00301033,0.000082699604,0.00010659638,0.00019926605,0.00007630977,0.42777294,0.03864101,0.0019079981,0.004867732,0.5225763],"study_design_scores_gemma":[0.0000087432645,0.0000592879,0.00045079453,0.000006310228,0.00000993765,0.00002248605,0.0000065751483,0.99484295,0.0034535725,0.0005476292,0.0005845485,0.000007191656],"about_ca_topic_score_codex":0.020226965,"about_ca_topic_score_gemma":0.033672284,"teacher_disagreement_score":0.020226965,"about_ca_system_score_codex":0.000587084,"about_ca_system_score_gemma":0.0017778897,"threshold_uncertainty_score":0.040218472},"labels":[],"label_agreement":null},{"id":"W2886213316","doi":"10.3390/s18082560","title":"Vehicle Counting Based on Vehicle Detection and Tracking from Aerial Videos","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Computer vision; Computer science; Artificial intelligence; Object detection; Detector; Vehicle tracking system; Tracking (education); Foreground detection; Pixel; Perspective (graphical); Image sensor; Background subtraction; Kalman filter; Pattern recognition (psychology)","score_opus":0.022360762357936312,"score_gpt":0.2694368697879695,"score_spread":0.24707610743003317,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2886213316","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.13460952,0.00040574963,0.85975444,0.0000585718,0.00010128681,0.00014164302,0.00021378548,0.0024313773,0.0022836505],"genre_scores_gemma":[0.6535771,0.0005608766,0.34177652,0.000072261006,0.00007390306,0.00009333771,0.0007465304,0.000084632404,0.0030148567],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996289,0.000027958637,0.00001842626,0.000134544,0.00013239392,0.000057696157],"domain_scores_gemma":[0.9996871,0.000055527616,0.000053934098,0.000043370426,0.00013203609,0.00002801028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022140794,0.00071275805,0.00066532654,0.0015941118,0.00029057416,0.00052086246,0.00096125685,0.00041577965,0.0006051905],"category_scores_gemma":[0.00083713623,0.00024221551,0.0003126167,0.0009580426,0.00020464834,0.0006420026,0.00046704346,0.00034208986,0.00034159634],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002265749,0.00014521938,0.0063597066,0.00016179396,0.000058623486,0.0004917764,0.0001305169,0.04827275,0.088774286,0.0018796873,0.0026218796,0.8508772],"study_design_scores_gemma":[0.000010728663,0.000073666,0.0044858004,0.000014313228,0.00003881233,0.0003237501,0.000076842705,0.9446121,0.047694627,0.0006678789,0.0019819771,0.000019559087],"about_ca_topic_score_codex":0.008437788,"about_ca_topic_score_gemma":0.009318672,"teacher_disagreement_score":0.008437788,"about_ca_system_score_codex":0.00031514623,"about_ca_system_score_gemma":0.0004700679,"threshold_uncertainty_score":0.016777337},"labels":[],"label_agreement":null},{"id":"W2887240040","doi":"10.1016/j.asoc.2018.07.049","title":"Collaborative model based UAV tracking via local kernel feature","year":2018,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Kernel (algebra); Feature (linguistics); Tracking (education); Computer vision; Pattern recognition (psychology); Mathematics","score_opus":0.014748783552039107,"score_gpt":0.280064953107014,"score_spread":0.2653161695549749,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2887240040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021016758,0.00008085691,0.97806203,0.00003430741,0.000024485453,0.00001057239,0.000013884307,0.00025418395,0.00050298916],"genre_scores_gemma":[0.85031515,0.000122313,0.14675938,0.00004996418,0.000030482546,0.00005466713,0.0001169993,0.00007316038,0.0024778685],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993555,0.00012054089,0.000029108314,0.00023283242,0.00018217959,0.00007989202],"domain_scores_gemma":[0.99877685,0.0004300042,0.0001989201,0.0002732133,0.00025706406,0.00006389903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000770939,0.00062595966,0.0013905672,0.0006514995,0.0005207366,0.0009979488,0.0012722306,0.0009935616,0.00087211735],"category_scores_gemma":[0.0026144537,0.0005210412,0.0008848466,0.00095834746,0.0005541425,0.0019413762,0.0017656358,0.00095459755,0.0005024023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004314512,0.00022110269,0.0030819036,0.00010508508,0.00017814258,0.00016392258,0.00026724907,0.651076,0.025862902,0.01114714,0.0020772223,0.30538782],"study_design_scores_gemma":[0.0000026699233,0.00001710622,0.00015549707,0.000001263477,0.000006301025,0.000016772283,0.000005695358,0.99792993,0.0010718958,0.00067209365,0.00011690492,0.000003946749],"about_ca_topic_score_codex":0.004553407,"about_ca_topic_score_gemma":0.0035437932,"teacher_disagreement_score":0.004553407,"about_ca_system_score_codex":0.0005574995,"about_ca_system_score_gemma":0.00065044104,"threshold_uncertainty_score":0.009053826},"labels":[],"label_agreement":null},{"id":"W2890205431","doi":"10.1007/978-3-030-03801-4_63","title":"Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector","year":2018,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Object (grammar); Computer vision; Artificial intelligence; Computer science; Tracking (education); Video tracking; Object detection; Task (project management); Detector; Position (finance); Field (mathematics); Pattern recognition (psychology); Mathematics; Engineering; Psychology","score_opus":0.027060494954520488,"score_gpt":0.2928495479674822,"score_spread":0.2657890530129617,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890205431","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19347583,0.0005349132,0.80282426,0.00014565604,0.00014398641,0.00004565158,0.000103073246,0.0010181972,0.0017084669],"genre_scores_gemma":[0.70674115,0.0002977323,0.28920823,0.00007622208,0.0000647487,0.000030521926,0.00023134526,0.00009438463,0.003255675],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99952483,0.00006387611,0.000013854803,0.00016750817,0.0001270338,0.000102941056],"domain_scores_gemma":[0.9995165,0.00015900914,0.000042726628,0.00008894478,0.00014335198,0.000049389317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010055408,0.000538443,0.0010817008,0.001073656,0.0005309203,0.001062816,0.0008592334,0.0012072041,0.00082640524],"category_scores_gemma":[0.0014476714,0.00056482566,0.000539366,0.0011682609,0.00034770893,0.00077778165,0.0009260486,0.0008531071,0.00044637112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010351537,0.0003864634,0.010697798,0.00015027839,0.00025133844,0.00036686053,0.000197863,0.16308129,0.11775508,0.0031485844,0.0026199683,0.70030934],"study_design_scores_gemma":[0.000009232494,0.00004013064,0.0022787035,0.000003650605,0.000026829415,0.0000856398,0.00001679906,0.9895426,0.007019858,0.0005548382,0.00041375574,0.000007852685],"about_ca_topic_score_codex":0.0056891753,"about_ca_topic_score_gemma":0.007371332,"teacher_disagreement_score":0.0056891753,"about_ca_system_score_codex":0.00044518663,"about_ca_system_score_gemma":0.0007220119,"threshold_uncertainty_score":0.011312127},"labels":[],"label_agreement":null},{"id":"W2890533060","doi":"10.1109/access.2018.2868610","title":"Real-Time Android Application for Traffic Density Estimation","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Android (operating system); Computer science; Notation; Artificial intelligence; Embedded system; Real-time computing; Computer graphics (images); Operating system; Mathematics; Arithmetic","score_opus":0.032588695646795625,"score_gpt":0.3560541833460245,"score_spread":0.3234654876992289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2890533060","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":[0.0827993,0.0028732535,0.43207827,0.00046854548,0.0006365476,0.0033472313,0.017468555,0.39322641,0.067101896],"genre_scores_gemma":[0.6587216,0.0016049756,0.22860368,0.0010278365,0.0004613537,0.005178309,0.022228034,0.00686307,0.07531111],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997074,0.000035758934,0.000022734857,0.00007285597,0.00011540011,0.00004577862],"domain_scores_gemma":[0.9995939,0.00011691562,0.000029860646,0.00005755971,0.00016203281,0.00003962179],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023508447,0.0010944199,0.0006377997,0.0010151322,0.0001661525,0.0003589833,0.00089262665,0.00048661753,0.021548849],"category_scores_gemma":[0.0011098566,0.00022706024,0.00032931403,0.00037497326,0.0001002122,0.00049918186,0.0005058684,0.00037642254,0.012372132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017286951,0.00038102086,0.0063206456,0.0015530321,0.0001308749,0.0013706532,0.0006194726,0.004560077,0.09610575,0.002462411,0.21749304,0.6672744],"study_design_scores_gemma":[0.0008143677,0.0010923848,0.07662687,0.00046123544,0.0003851867,0.003953022,0.00045474814,0.25761876,0.19220406,0.0031538215,0.4626477,0.00058789604],"about_ca_topic_score_codex":0.0020003763,"about_ca_topic_score_gemma":0.0021173158,"teacher_disagreement_score":0.021548849,"about_ca_system_score_codex":0.00018834058,"about_ca_system_score_gemma":0.00031295817,"threshold_uncertainty_score":0.07208806},"labels":[],"label_agreement":null},{"id":"W2891624393","doi":"10.1109/icip.2018.8451757","title":"Robust Scoring and Ranking of Object Tracking Techniques","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Outlier; Ranking (information retrieval); Benchmark (surveying); BitTorrent tracker; Computer science; Estimator; Artificial intelligence; Tracking (education); Object (grammar); Video tracking; Pattern recognition (psychology); Rank (graph theory); Measure (data warehouse); Data mining; Computer vision; Statistics; Mathematics; Eye tracking","score_opus":0.06043956693016391,"score_gpt":0.30306720215978533,"score_spread":0.2426276352296214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891624393","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":[0.09728892,0.00315672,0.8895108,0.00019455176,0.00033185864,0.00048342923,0.00070584484,0.004439288,0.0038885367],"genre_scores_gemma":[0.51633316,0.0010507664,0.47470132,0.00010001257,0.00023309661,0.0003111975,0.003154946,0.00043935503,0.0036761917],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.98510736,0.00348357,0.0013391434,0.0022124427,0.0070513105,0.000806217],"domain_scores_gemma":[0.98335165,0.004426466,0.0024132377,0.0022358105,0.0068085026,0.0007644072],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010283509,0.0020743387,0.002505785,0.007920965,0.00094942836,0.0032877298,0.0022311034,0.0016347854,0.0018025545],"category_scores_gemma":[0.027265612,0.00034268052,0.0012092049,0.004106453,0.00065343396,0.0017419693,0.0020196172,0.0010086528,0.0020413217],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047103563,0.0003798483,0.026300818,0.00060091045,0.00049778784,0.00018459554,0.00019520507,0.08237041,0.028137503,0.0030854866,0.009154462,0.8486219],"study_design_scores_gemma":[0.00009344958,0.0013947996,0.025071293,0.000119644144,0.00031022707,0.00073894777,0.00023718456,0.9318466,0.02668488,0.0060241497,0.0073203365,0.00015848642],"about_ca_topic_score_codex":0.003341001,"about_ca_topic_score_gemma":0.004773353,"teacher_disagreement_score":0.010283509,"about_ca_system_score_codex":0.0009872284,"about_ca_system_score_gemma":0.0020070595,"threshold_uncertainty_score":0.054385066},"labels":[],"label_agreement":null},{"id":"W2891670750","doi":"10.1109/icra.2018.8461181","title":"The Hands-Free Push-Cart: Autonomous Following in Front by Predicting User Trajectory Around Obstacles","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Georgia Institute of Technology","keywords":"Computer science; Trajectory; Robot; Artificial intelligence; Mobile robot; Motion (physics); Obstacle; Modular design; Computer vision; Motion planning; Human–computer interaction; Real-time computing","score_opus":0.015927360110233036,"score_gpt":0.26614491834128295,"score_spread":0.2502175582310499,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2891670750","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32324576,0.00035907375,0.6652214,0.000292216,0.00010133058,0.00019595229,0.00016331038,0.0059384317,0.004482553],"genre_scores_gemma":[0.8861157,0.00012102756,0.11056483,0.00007327725,0.000017946713,0.000054973065,0.000096725606,0.00008158935,0.0028739565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998987,0.00002108463,0.0000028687252,0.000027450911,0.000029794555,0.000020116255],"domain_scores_gemma":[0.9997826,0.00007489517,0.000021567093,0.000037299615,0.00003373466,0.000049934733],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024167205,0.0005204612,0.00047834194,0.00020381878,0.0003922068,0.00035683095,0.0006953961,0.0007303377,0.0016391706],"category_scores_gemma":[0.0006927945,0.00031527365,0.00020331568,0.00013038891,0.00036557135,0.00044334945,0.00066694536,0.00041148803,0.00049018196],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019857294,0.00041564406,0.013895674,0.0003207716,0.00023592311,0.0029580852,0.0009947768,0.35723403,0.16480894,0.0032867452,0.007766797,0.44609693],"study_design_scores_gemma":[0.00003630181,0.00023374004,0.002276891,0.000011402008,0.000024789682,0.00034481296,0.00006862723,0.9816206,0.013018424,0.0006757283,0.0016582983,0.000030199708],"about_ca_topic_score_codex":0.007145528,"about_ca_topic_score_gemma":0.00856929,"teacher_disagreement_score":0.007145528,"about_ca_system_score_codex":0.00013243957,"about_ca_system_score_gemma":0.0005505004,"threshold_uncertainty_score":0.01420784},"labels":[],"label_agreement":null},{"id":"W2893295587","doi":"10.1109/access.2018.2871659","title":"Adaptive Framework for Robust Visual Tracking","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Robustness (evolution); BitTorrent tracker; Video tracking; Eye tracking; Trajectory; Particle filter; Tracking system; Benchmark (surveying); Object detection; Object (grammar); Pattern recognition (psychology); Kalman filter","score_opus":0.14689571985309702,"score_gpt":0.420419875378432,"score_spread":0.273524155525335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2893295587","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00072012795,0.00017256827,0.99809927,0.000023562316,0.000027262631,0.000021129183,0.00002343628,0.0004937727,0.000418907],"genre_scores_gemma":[0.19840927,0.0008031223,0.7927516,0.00020949777,0.00020681706,0.00037991698,0.0006435761,0.00035001626,0.0062461537],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99831355,0.0002665075,0.00006870074,0.00054274226,0.0006406952,0.00016779234],"domain_scores_gemma":[0.9993094,0.00017522155,0.0000838789,0.00012846089,0.00025633306,0.000046716857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018709076,0.0014123626,0.0013951255,0.001612358,0.0004403514,0.0011808684,0.0030940748,0.001534498,0.002914425],"category_scores_gemma":[0.0028970358,0.0005422832,0.0013929112,0.0015582925,0.0008013212,0.0012234162,0.0019949346,0.0019135872,0.001588455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016202588,0.00009918262,0.0005426796,0.00013930455,0.00013097914,0.00016183933,0.00009154436,0.5399038,0.013967495,0.039287377,0.006329384,0.39918435],"study_design_scores_gemma":[0.000009826193,0.00002889649,0.00008756603,0.0000048819225,0.000009145786,0.000037982056,0.000004715765,0.9925014,0.000980212,0.0041801506,0.0021456832,0.000009582837],"about_ca_topic_score_codex":0.009972547,"about_ca_topic_score_gemma":0.0057721846,"teacher_disagreement_score":0.009972547,"about_ca_system_score_codex":0.00097562,"about_ca_system_score_gemma":0.001575341,"threshold_uncertainty_score":0.019828975},"labels":[],"label_agreement":null},{"id":"W2894149753","doi":"","title":"Deep Collaborative Tracking Networks.","year":2018,"lang":"en","type":"article","venue":"British Machine Vision Conference","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Tracking (education); Artificial intelligence","score_opus":0.018271627945819505,"score_gpt":0.30554946637922886,"score_spread":0.28727783843340937,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2894149753","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":[0.010049292,0.0030563292,0.9673657,0.0007713352,0.00048832037,0.00006873584,0.0009728912,0.00476669,0.012460656],"genre_scores_gemma":[0.45861873,0.002591274,0.46085247,0.0009556512,0.000573309,0.00023176472,0.0073278374,0.000944983,0.06790401],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989753,0.00018039417,0.000045267974,0.00041606918,0.0002065732,0.00017646122],"domain_scores_gemma":[0.99804866,0.00051929266,0.00016677355,0.0006655183,0.00047126267,0.00012847182],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014785082,0.0016417339,0.0015176415,0.0012043507,0.0010112827,0.0020729771,0.0032365415,0.0026652822,0.0077979234],"category_scores_gemma":[0.0045818235,0.001147094,0.0010580462,0.0017442312,0.0008099341,0.0028373878,0.0023675798,0.0026191194,0.005677962],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004250155,0.00025567092,0.0017969442,0.00021408574,0.0003458239,0.00015989829,0.00013022817,0.2024019,0.006815073,0.026183829,0.05896539,0.7023061],"study_design_scores_gemma":[0.000021064367,0.00004078596,0.00045206628,0.00002738838,0.00005553745,0.000064233405,0.0000244186,0.9639967,0.0046439115,0.021652035,0.009006954,0.000014920784],"about_ca_topic_score_codex":0.02046552,"about_ca_topic_score_gemma":0.040027123,"teacher_disagreement_score":0.02046552,"about_ca_system_score_codex":0.0012286543,"about_ca_system_score_gemma":0.0015115947,"threshold_uncertainty_score":0.040692806},"labels":[],"label_agreement":null},{"id":"W2896492166","doi":"10.1109/mmsp.2018.8547080","title":"CPNet: A Context Preserver Convolutional Neural Network for Detecting Shadows in Single RGB Images","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Shadow (psychology); Artificial intelligence; Computer science; Convolutional neural network; RGB color model; Exploit; Computer vision; Context (archaeology); Segmentation; Deep learning; Task (project management); Pixel; Image (mathematics); Pattern recognition (psychology); Geography","score_opus":0.06932561310747279,"score_gpt":0.31860750636495755,"score_spread":0.24928189325748476,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2896492166","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19784664,0.0047342973,0.7608372,0.00045171168,0.0005704216,0.00033193702,0.0040684184,0.021876585,0.00928287],"genre_scores_gemma":[0.6372999,0.0016448236,0.33761227,0.00046941554,0.00015009625,0.00018078854,0.010211101,0.00054701243,0.011884535],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997391,0.000023376642,0.000008995298,0.000109570654,0.00007079367,0.00004821658],"domain_scores_gemma":[0.9997974,0.000035048688,0.00002705665,0.00005298956,0.0000690649,0.000018530334],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037900085,0.0012918796,0.00063397165,0.0009078606,0.00032717636,0.000461079,0.0015860829,0.0006697372,0.0017814866],"category_scores_gemma":[0.0007723969,0.00037362924,0.00046091268,0.000669302,0.0003211468,0.00091078127,0.0007632239,0.0010345697,0.000816782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058092107,0.00037009406,0.0040211664,0.00026965322,0.00026272936,0.00026124896,0.00006131159,0.083452806,0.06792899,0.0022963705,0.02440823,0.8160864],"study_design_scores_gemma":[0.000026597832,0.00010467947,0.0032627515,0.000027274895,0.000050448598,0.00017600015,0.000015106301,0.95893633,0.030566402,0.00146797,0.005346515,0.000019901108],"about_ca_topic_score_codex":0.01649058,"about_ca_topic_score_gemma":0.029315617,"teacher_disagreement_score":0.01649058,"about_ca_system_score_codex":0.0007858445,"about_ca_system_score_gemma":0.0009913613,"threshold_uncertainty_score":0.03278917},"labels":[],"label_agreement":null},{"id":"W2897198596","doi":"10.1109/jiot.2018.2876695","title":"UAV-Enabled Spatial Data Sampling in Large-Scale IoT Systems Using Denoising Autoencoder Neural Network","year":2018,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","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":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cluster analysis; Real-time computing; Sampling (signal processing); Wireless sensor network; Cloud computing; Autoencoder; Artificial neural network; Data mining; Artificial intelligence; Computer network; Computer vision","score_opus":0.08848148682237848,"score_gpt":0.3491733359616639,"score_spread":0.2606918491392854,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897198596","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1022738,0.0003196262,0.8952218,0.0001407279,0.000037701437,0.000022755172,0.000025254185,0.00022420715,0.0017341089],"genre_scores_gemma":[0.93531895,0.00016391295,0.063639745,0.000037926515,0.000012737237,0.000020191183,0.00004027155,0.000008454587,0.0007578539],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983513,0.000037662965,0.000009795537,0.00004060718,0.000056848065,0.00001995943],"domain_scores_gemma":[0.9997831,0.00008530939,0.00003195047,0.000027869024,0.000061035586,0.000010739098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003162662,0.0002742849,0.00029989643,0.0001356555,0.00019724143,0.0002580662,0.00038203888,0.0003385864,0.0002164004],"category_scores_gemma":[0.0006752248,0.0001423018,0.00023710683,0.00020008517,0.00029039,0.0004934232,0.00039660413,0.00038319058,0.000054258573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017155727,0.00006224341,0.0024963259,0.00008246367,0.00004136368,0.00019092366,0.00013427918,0.8496202,0.03497456,0.004731325,0.00058748503,0.10690726],"study_design_scores_gemma":[0.0000013373276,0.000014128827,0.00013690298,0.0000014028926,0.0000020890184,0.0000113083015,0.0000058669716,0.99748576,0.0020187914,0.00022530902,0.000095208605,0.0000018067348],"about_ca_topic_score_codex":0.004045823,"about_ca_topic_score_gemma":0.003960882,"teacher_disagreement_score":0.004045823,"about_ca_system_score_codex":0.00028726572,"about_ca_system_score_gemma":0.0003197415,"threshold_uncertainty_score":0.008044541},"labels":[],"label_agreement":null},{"id":"W2897664326","doi":"10.1007/s00500-018-3571-5","title":"Object tracking via dense SIFT features and low-rank representation","year":2018,"lang":"en","type":"article","venue":"Soft Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Scale-invariant feature transform; Artificial intelligence; Computer vision; Computer science; Rank (graph theory); Representation (politics); Tracking (education); Object (grammar); Pattern recognition (psychology); Mathematics; Feature extraction; Combinatorics; Psychology","score_opus":0.024356877350524695,"score_gpt":0.31648559384296776,"score_spread":0.29212871649244304,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2897664326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009997834,0.00015443935,0.98867214,0.000069714304,0.000025093896,0.000017491051,0.000052337888,0.0004429321,0.0005680853],"genre_scores_gemma":[0.47314122,0.0006234879,0.5180361,0.00017581531,0.00013920994,0.00009014604,0.00067892467,0.00015479763,0.0069602486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937505,0.00008592339,0.000027303622,0.00015711761,0.000277326,0.00007726938],"domain_scores_gemma":[0.99923885,0.00021059558,0.00013147652,0.00020012971,0.00017857783,0.00004033864],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061062607,0.00068063027,0.0010821425,0.0012266267,0.00039518392,0.0013934491,0.0010409848,0.0009456015,0.0012615775],"category_scores_gemma":[0.0026059675,0.00053647324,0.00062342186,0.0017042268,0.0005368459,0.0017793501,0.0012401586,0.0009533746,0.00097207975],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003180111,0.00018071388,0.0018134773,0.00014897206,0.00009161558,0.00009338316,0.00011105273,0.12661327,0.048744682,0.015234428,0.0044954894,0.80215484],"study_design_scores_gemma":[0.000010458916,0.000041298375,0.0007886482,0.000006786407,0.000015864178,0.000057652447,0.0000139910135,0.98669225,0.0058254655,0.005754084,0.0007827348,0.000010682169],"about_ca_topic_score_codex":0.004578128,"about_ca_topic_score_gemma":0.006183723,"teacher_disagreement_score":0.004578128,"about_ca_system_score_codex":0.0006041189,"about_ca_system_score_gemma":0.0008726352,"threshold_uncertainty_score":0.009102941},"labels":[],"label_agreement":null},{"id":"W2898762690","doi":"10.1155/2018/8486906","title":"Real-Time Pedestrian Tracking and Counting with TLD","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Tracking (education); Pedestrian; Computer science; Pedestrian detection; Thermoluminescent dosimeter; Similarity (geometry); Measure (data warehouse); Artificial intelligence; Position (finance); Computer vision; Simulation; Real-time computing; Data mining; Engineering; Transport engineering; Image (mathematics); Optics","score_opus":0.014065876184696775,"score_gpt":0.2814104654732547,"score_spread":0.2673445892885579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2898762690","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017787376,0.00026298143,0.97061723,0.000097045784,0.00015019697,0.00007632962,0.00039407378,0.008720766,0.0018939313],"genre_scores_gemma":[0.32270154,0.00037522768,0.6697827,0.0002230508,0.00011831201,0.00018211994,0.0019553814,0.00023749907,0.004424186],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885607,0.00015389208,0.00006697488,0.00034379153,0.00045915696,0.000120163946],"domain_scores_gemma":[0.99914765,0.00013105894,0.000089941845,0.00029682778,0.0002712413,0.00006329454],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007689338,0.00081395626,0.0010264238,0.0026264926,0.00044230066,0.0010232052,0.0017640573,0.00074410805,0.0031070653],"category_scores_gemma":[0.0015999295,0.00048588013,0.00055435306,0.0016655378,0.00029305945,0.0011369972,0.0016448371,0.00057122315,0.002478763],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006955719,0.00022990984,0.007389133,0.0002235327,0.00008802001,0.00024468853,0.00011987765,0.013844158,0.043631867,0.0013450286,0.011013767,0.92117435],"study_design_scores_gemma":[0.00009151271,0.00024768026,0.0074596717,0.000042080414,0.00009642007,0.0015010732,0.00011612726,0.897874,0.06801813,0.0023223814,0.022158923,0.00007188987],"about_ca_topic_score_codex":0.0022918372,"about_ca_topic_score_gemma":0.002887068,"teacher_disagreement_score":0.0031070653,"about_ca_system_score_codex":0.00044879454,"about_ca_system_score_gemma":0.00064410444,"threshold_uncertainty_score":0.010394216},"labels":[],"label_agreement":null},{"id":"W2899954294","doi":"10.1007/s11045-018-0621-1","title":"A vehicle detection scheme based on two-dimensional HOG features in the DFT and DCT domains","year":2018,"lang":"en","type":"article","venue":"Multidimensional Systems and Signal Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Discrete cosine transform; Pattern recognition (psychology); Computer science; Classifier (UML); Histogram; Computer vision; Frequency domain; Pixel; Object detection; Mathematics; Image (mathematics)","score_opus":0.019464028749760057,"score_gpt":0.2837042297007303,"score_spread":0.2642402009509702,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2899954294","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":[0.034012344,0.0004069211,0.9625571,0.00011920773,0.00019380647,0.000106556916,0.00012632196,0.0006983307,0.0017793893],"genre_scores_gemma":[0.29902968,0.0006242768,0.6913434,0.00016794876,0.00013178478,0.00010778037,0.00062846125,0.00005723675,0.007909318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998248,0.000020323441,0.000009550419,0.000044405722,0.000072851464,0.000028091026],"domain_scores_gemma":[0.9998093,0.000025201818,0.000012649906,0.000030406733,0.000098788776,0.000023683158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030919025,0.00037703916,0.0006664949,0.00092263985,0.00028849943,0.00045255478,0.0005281415,0.0004885405,0.0013514902],"category_scores_gemma":[0.00046091335,0.00023493636,0.0003147868,0.00070993387,0.00024182255,0.0005976409,0.00058276474,0.00044389453,0.0009387299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003347726,0.00015256104,0.001236713,0.000103334925,0.00005846302,0.00007826411,0.000038759434,0.007933338,0.21771024,0.0037646303,0.0027669703,0.76582193],"study_design_scores_gemma":[0.000103582206,0.0006818195,0.006933969,0.00003525804,0.00012717715,0.0008039576,0.00005912398,0.84997785,0.12688215,0.0026559588,0.011639442,0.000099682264],"about_ca_topic_score_codex":0.002031575,"about_ca_topic_score_gemma":0.0034849376,"teacher_disagreement_score":0.002031575,"about_ca_system_score_codex":0.0001960768,"about_ca_system_score_gemma":0.00056663423,"threshold_uncertainty_score":0.0045211315},"labels":[],"label_agreement":null},{"id":"W2900239596","doi":"10.1016/j.scitotenv.2018.11.038","title":"Terrain-influenced incremental watchtower expansion for wildfire detection","year":2018,"lang":"en","type":"article","venue":"The Science of The Total Environment","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Jiangsu Overseas Research and Training Program for University Prominent Young and Middle-aged Teachers and Presidents","keywords":"Maximization; Submodular set function; Set (abstract data type); Process (computing); Computer science; Function (biology); Matching (statistics); Mathematical optimization; Mathematics","score_opus":0.01460607402448854,"score_gpt":0.25612576555923405,"score_spread":0.2415196915347455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900239596","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":[0.51411986,0.00050688355,0.48155996,0.00009830623,0.00009785015,0.00005478834,0.00024006212,0.0010865451,0.0022357053],"genre_scores_gemma":[0.93053114,0.000106878564,0.068187855,0.00002578059,0.000029496261,0.000021000767,0.00021003722,0.000058206377,0.0008296774],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985445,0.000018903396,0.000004978489,0.000050742197,0.000042271575,0.000028559614],"domain_scores_gemma":[0.99953294,0.0002545745,0.00004935392,0.00004324406,0.00008839034,0.00003152653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031223457,0.00040493478,0.00042066583,0.00070977927,0.0002425506,0.00037972178,0.000614397,0.000357825,0.00072017335],"category_scores_gemma":[0.0016680992,0.00020034748,0.00021901375,0.0005706471,0.00021420594,0.0005176981,0.00039961384,0.00040255353,0.00017541074],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068494666,0.00034960438,0.024982443,0.0001337611,0.000073958356,0.00026893333,0.00020532082,0.3175506,0.07106865,0.0021436994,0.0026496395,0.57988846],"study_design_scores_gemma":[0.000004439384,0.000032827804,0.0044361935,0.000003891617,0.000009830287,0.000039619834,0.00002233171,0.99135274,0.0035262497,0.0003054845,0.00026087993,0.000005404124],"about_ca_topic_score_codex":0.0040412797,"about_ca_topic_score_gemma":0.010996304,"teacher_disagreement_score":0.0040412797,"about_ca_system_score_codex":0.00024949596,"about_ca_system_score_gemma":0.0003709861,"threshold_uncertainty_score":0.008035541},"labels":[],"label_agreement":null},{"id":"W2900883900","doi":"10.1051/matecconf/201823202046","title":"Visual Target Tracking using Robust Information Interaction between Single Tracker and Online Model","year":2018,"lang":"en","type":"article","venue":"MATEC Web of Conferences","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval","funders":"State Key Laboratory of Networking and Switching Technology; China Scholarship Council; Beijing University of Posts and Telecommunications; National Natural Science Foundation of China","keywords":"Discriminative model; Computer science; BitTorrent tracker; Artificial intelligence; Particle filter; Sigmoid function; Support vector machine; Computer vision; Eye tracking; Classifier (UML); Pattern recognition (psychology); Histogram; Maximum a posteriori estimation; A priori and a posteriori; Tracking (education); Filter (signal processing); Mathematics; Maximum likelihood; Artificial neural network; Image (mathematics)","score_opus":0.10536588722879722,"score_gpt":0.33979145620431006,"score_spread":0.23442556897551284,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2900883900","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0052050236,0.00011225355,0.99339205,0.000034166023,0.000020872645,0.000013085957,0.000010870574,0.0006821351,0.0005295239],"genre_scores_gemma":[0.5454601,0.00026485755,0.44951528,0.00018305267,0.00010896115,0.00015749021,0.00024079473,0.00023508463,0.0038343372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985819,0.00017724751,0.000057344158,0.0005376092,0.0005081068,0.0001377935],"domain_scores_gemma":[0.9988794,0.00031567825,0.0001799995,0.0003222605,0.00024335773,0.0000592763],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013249612,0.00094033574,0.0019082633,0.00088081893,0.00056505913,0.0013051808,0.002173154,0.0013483558,0.0011625311],"category_scores_gemma":[0.0031476663,0.0006845601,0.0009822068,0.0010380172,0.0005719818,0.0024879922,0.0021830613,0.0011681592,0.0008885768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000357785,0.00023140886,0.0020993727,0.00013271897,0.00015829639,0.00024155922,0.00027508903,0.2641318,0.052269008,0.012138621,0.0030265898,0.66493773],"study_design_scores_gemma":[0.00001005553,0.00005793582,0.0003241308,0.000004596383,0.00001924389,0.0000941091,0.00001002252,0.990595,0.005510715,0.0022107249,0.0011499685,0.000013443473],"about_ca_topic_score_codex":0.0029594908,"about_ca_topic_score_gemma":0.0023581984,"teacher_disagreement_score":0.0029594908,"about_ca_system_score_codex":0.00077564665,"about_ca_system_score_gemma":0.0010730416,"threshold_uncertainty_score":0.0070071816},"labels":[],"label_agreement":null},{"id":"W2901512101","doi":"10.1109/access.2018.2881723","title":"Visual Tracking Based on Correlation Filter and Robust Coding in Bilateral 2DPCA Subspace","year":2018,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Artificial intelligence; Computer science; Subspace topology; Pattern recognition (psychology); Affine transformation; Computer vision; Robustness (evolution); Eye tracking; Coding (social sciences); Generative model; Mathematics; Generative grammar","score_opus":0.07652545256430476,"score_gpt":0.3552685109375929,"score_spread":0.2787430583732881,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2901512101","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":[0.0049000797,0.0001816006,0.9937295,0.000053859283,0.00003149245,0.00002641536,0.000052107753,0.00044974664,0.00057521305],"genre_scores_gemma":[0.26923656,0.000818023,0.72317666,0.00025190265,0.00011161771,0.00028694642,0.000892456,0.00025245623,0.0049733305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928916,0.00008630641,0.000031379914,0.00020003921,0.00032271259,0.000070461734],"domain_scores_gemma":[0.9992812,0.00017187891,0.0001046334,0.00014630084,0.00024471123,0.00005132695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007939311,0.0008879568,0.0010850922,0.0013056558,0.0004565093,0.0009931059,0.0011906504,0.00094775896,0.0014744175],"category_scores_gemma":[0.0026158069,0.00044503255,0.0011322809,0.0019296382,0.0006139261,0.0012697356,0.0012217503,0.0011621929,0.00075293257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020550656,0.00009101755,0.0012498741,0.00011165336,0.00009458589,0.00010224482,0.0001455042,0.29714087,0.035922293,0.016918479,0.0042031882,0.64381474],"study_design_scores_gemma":[0.000007291327,0.000033767785,0.0002901842,0.0000073334268,0.0000111360705,0.000055579716,0.000007173824,0.9919626,0.00402431,0.002094676,0.0014891318,0.000016890277],"about_ca_topic_score_codex":0.013974096,"about_ca_topic_score_gemma":0.010021388,"teacher_disagreement_score":0.013974096,"about_ca_system_score_codex":0.0006875146,"about_ca_system_score_gemma":0.0015927182,"threshold_uncertainty_score":0.02778554},"labels":[],"label_agreement":null},{"id":"W2902502676","doi":"10.1109/mmsp.2018.8547101","title":"Reliability Analysis of Io VT Based Intelligent Video Surveillance System","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Reliability (semiconductor); Component (thermodynamics); Variety (cybernetics); Reliability engineering; Internet of Things; The Internet; Reliability theory; Function (biology); Computer security; Distributed computing; Failure rate; Artificial intelligence; Engineering; World Wide Web","score_opus":0.023565371785438414,"score_gpt":0.2969631370703784,"score_spread":0.27339776528494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2902502676","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.48174122,0.0016405755,0.5052346,0.00044588084,0.00007400871,0.00009754525,0.00020626358,0.0006033901,0.009956548],"genre_scores_gemma":[0.99720615,0.0001340667,0.0019442728,0.000012060181,0.000008142921,0.000015321932,0.0000473272,0.000010416791,0.0006222109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992842,0.0001627418,0.000039415452,0.00014107439,0.00026929923,0.00010324924],"domain_scores_gemma":[0.99874747,0.00049402175,0.00018840513,0.00007607682,0.00045883522,0.000035245317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080733007,0.00043506076,0.0004952125,0.0007232429,0.00036003612,0.0005367926,0.0005941984,0.0004382022,0.0008170701],"category_scores_gemma":[0.0021647636,0.00017255741,0.000450996,0.00030939816,0.0004317068,0.0004887175,0.00033533285,0.00030782935,0.00018528228],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029219725,0.00003636916,0.008864631,0.00021539356,0.00006888713,0.0005795791,0.00027375694,0.9243031,0.027620714,0.008417757,0.0009043086,0.028423345],"study_design_scores_gemma":[0.0000020643329,0.000065747445,0.0016510987,0.0000062713657,0.00001152944,0.000069781854,0.000027905462,0.99556255,0.0016695269,0.0007486161,0.00017731728,0.000007582276],"about_ca_topic_score_codex":0.0056774644,"about_ca_topic_score_gemma":0.0018546025,"teacher_disagreement_score":0.0056774644,"about_ca_system_score_codex":0.0008820544,"about_ca_system_score_gemma":0.00045714685,"threshold_uncertainty_score":0.011288822},"labels":[],"label_agreement":null},{"id":"W2903123076","doi":"10.1155/2018/8703576","title":"Predicting Pedestrian Counts for Crossing Scenario Based on Fused Infrared-Visual Videos","year":2018,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Pedestrian; Computer science; Computer vision; Pedestrian detection; Artificial intelligence; Feature (linguistics); Task (project management); Transport engineering; Engineering","score_opus":0.02242681986354569,"score_gpt":0.32830374237661897,"score_spread":0.3058769225130733,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2903123076","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":[0.8319081,0.0003366815,0.16473797,0.000057583376,0.000075472366,0.000050236515,0.0005288534,0.0007595597,0.0015454447],"genre_scores_gemma":[0.9767012,0.00014058579,0.022205696,0.000016568221,0.000019747738,0.000015285023,0.00045834697,0.000010463328,0.00043202165],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997501,0.000033280547,0.000013098016,0.00008548411,0.00007461305,0.000043419263],"domain_scores_gemma":[0.9995951,0.00010207459,0.00008551358,0.00003053967,0.00014475828,0.000041980395],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038587302,0.0007230847,0.00040408297,0.0014456366,0.00015842791,0.00039958165,0.000386399,0.0004428213,0.0004140662],"category_scores_gemma":[0.0012104893,0.00017469334,0.00033177578,0.0005812892,0.00014240333,0.0005808563,0.00031834657,0.0003520538,0.00031821185],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013866428,0.00071132864,0.16776599,0.00033860997,0.00019206158,0.00087017484,0.0003294822,0.24023956,0.08883153,0.00099747,0.003481096,0.49485606],"study_design_scores_gemma":[0.000006578469,0.00017954013,0.037387695,0.000013055191,0.00005247117,0.00015879229,0.00011722742,0.95157754,0.010016853,0.00023750997,0.00023368128,0.000019026718],"about_ca_topic_score_codex":0.003740895,"about_ca_topic_score_gemma":0.0053636115,"teacher_disagreement_score":0.003740895,"about_ca_system_score_codex":0.00024365043,"about_ca_system_score_gemma":0.00022377666,"threshold_uncertainty_score":0.0074382424},"labels":[],"label_agreement":null},{"id":"W2904574873","doi":"10.5334/jors.230","title":"Turtle Sport: An Open-Source Software for Communicating with GPS Sport Watches","year":2018,"lang":"en","type":"article","venue":"Journal of Open Research Software","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Université de Montréal; Institut National de la Recherche Scientifique","funders":"","keywords":"Computer science; Global Positioning System; Software; Turtle (robot); Java; Upload; Open source; Open source software; World Wide Web; Computer security; Operating system; Artificial intelligence","score_opus":0.1884667438700491,"score_gpt":0.4606484975303515,"score_spread":0.2721817536603024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904574873","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007217533,0.00034181454,0.324581,0.00021364763,0.00024679687,0.0006851325,0.0071250293,0.65220284,0.0073861866],"genre_scores_gemma":[0.14880484,0.0013982432,0.46063337,0.0011771196,0.00044681825,0.0042688157,0.07730712,0.26177505,0.044188663],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9989612,0.00015771415,0.000147046,0.000227192,0.0003773437,0.00012938814],"domain_scores_gemma":[0.9968244,0.0011264957,0.00024156447,0.0006439833,0.00086107623,0.00030255283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016920046,0.0015577453,0.00083524507,0.002108617,0.0005411712,0.001208114,0.00261565,0.0010159977,0.035131376],"category_scores_gemma":[0.007381964,0.0009539229,0.001045346,0.0009525969,0.0005909596,0.002341637,0.0035997354,0.0015112105,0.023679603],"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":[0.002040763,0.000547971,0.01002712,0.002551225,0.0003643718,0.0013297265,0.002224646,0.0057408283,0.045026604,0.008246187,0.405951,0.5159496],"study_design_scores_gemma":[0.00089451665,0.00056922843,0.026553605,0.0009313057,0.00030559965,0.002762165,0.00042208497,0.09667466,0.088427834,0.013598175,0.76814497,0.0007159538],"about_ca_topic_score_codex":0.0027774295,"about_ca_topic_score_gemma":0.002225927,"teacher_disagreement_score":0.035131376,"about_ca_system_score_codex":0.00047061397,"about_ca_system_score_gemma":0.0012348237,"threshold_uncertainty_score":0.117526114},"labels":[],"label_agreement":null},{"id":"W2904810076","doi":"10.1109/crv.2018.00028","title":"Systematic Street View Sampling: High Quality Annotation of Power Infrastructure in Rural Ontario","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Annotation; Data science; Limiting; Resource (disambiguation); Quality (philosophy); Data mining; Sampling (signal processing); Amazon rainforest; Machine learning; Artificial intelligence; World Wide Web; Telecommunications; Engineering; Computer network","score_opus":0.03254528978917898,"score_gpt":0.3324588177796625,"score_spread":0.29991352799048354,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2904810076","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":[0.693995,0.0011713393,0.057244547,0.0012707015,0.00014248707,0.0007023228,0.19922294,0.0050170543,0.041233707],"genre_scores_gemma":[0.7738113,0.0006478448,0.044807844,0.00011461988,0.000042949756,0.00025026881,0.17036717,0.0005432675,0.009414699],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9992311,0.00005713746,0.000031670792,0.00019377413,0.00030628854,0.00018008519],"domain_scores_gemma":[0.9986339,0.00013731292,0.000108082415,0.00027309108,0.00074374373,0.0001037691],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003604622,0.0005465892,0.00038115765,0.0021616553,0.001619847,0.0010368895,0.0011497746,0.00061244227,0.002692267],"category_scores_gemma":[0.002992546,0.00035126292,0.00036587572,0.0049008736,0.00076348725,0.00058750546,0.0012625235,0.00046978926,0.0013749531],"study_design_candidate":"observational","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090285856,0.00030591863,0.28416342,0.0013669764,0.00025015997,0.0021362398,0.005692523,0.060830478,0.026380079,0.007479295,0.24769919,0.36279285],"study_design_scores_gemma":[0.00011288896,0.00007812173,0.5777959,0.00027744728,0.000106239975,0.0007771456,0.007986029,0.19272824,0.016198814,0.0066799084,0.19707386,0.00018540211],"about_ca_topic_score_codex":0.8522836,"about_ca_topic_score_gemma":0.948028,"teacher_disagreement_score":0.1477164,"about_ca_system_score_codex":0.005268674,"about_ca_system_score_gemma":0.0057709836,"threshold_uncertainty_score":0.2971726},"labels":[],"label_agreement":null},{"id":"W2905187875","doi":"10.1109/csci.2017.81","title":"Object Movement Detection by Real-Time Deep Learning for Security Surveillance Camera","year":2017,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Cloud computing; Object (grammar); Deep learning; Support vector machine; Object detection; Smart camera; Computer vision; Video tracking; Cluster analysis; Real-time computing; Pattern recognition (psychology); Operating system","score_opus":0.013519790647422543,"score_gpt":0.28751236387470125,"score_spread":0.2739925732272787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905187875","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":[0.1393889,0.00040567268,0.8562907,0.00016784758,0.000044603676,0.00004542523,0.00013435647,0.0024760594,0.0010464846],"genre_scores_gemma":[0.7367289,0.00026212106,0.25980604,0.00008950213,0.000024430305,0.000054917415,0.0003241528,0.00007245947,0.0026375712],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997776,0.000029203653,0.000009161563,0.000059594597,0.00008007981,0.000044379118],"domain_scores_gemma":[0.9997818,0.00005605753,0.000034307566,0.000036325004,0.00006941895,0.000022188178],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041437065,0.00035944988,0.00036641833,0.0005554627,0.00015351862,0.000338481,0.00067838986,0.00039958983,0.0009093432],"category_scores_gemma":[0.0007557044,0.00023103302,0.00025487298,0.00048204337,0.00020582795,0.00054011244,0.0002921411,0.0005149784,0.0003217999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000477412,0.0004152414,0.0056076283,0.00009375617,0.00009898009,0.00014326203,0.00007398999,0.10457191,0.11954776,0.0020677054,0.0032298875,0.7636724],"study_design_scores_gemma":[0.00000455939,0.000047778514,0.0013270209,0.0000034439392,0.0000069636662,0.000024594974,0.0000076270417,0.9833565,0.014354882,0.00042411406,0.0004377891,0.0000047675576],"about_ca_topic_score_codex":0.0062552695,"about_ca_topic_score_gemma":0.009878395,"teacher_disagreement_score":0.0062552695,"about_ca_system_score_codex":0.00069506356,"about_ca_system_score_gemma":0.0005140258,"threshold_uncertainty_score":0.012437701},"labels":[],"label_agreement":null},{"id":"W2905201025","doi":"10.1109/crv.2018.00050","title":"Deep People Detection: A Comparative Study of SSD and LSTM-decoder","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Benchmark (surveying); Object detection; Artificial intelligence; Context (archaeology); Generalization; Feature (linguistics); Feature extraction; Pattern recognition (psychology)","score_opus":0.04450951153922436,"score_gpt":0.3378353667582241,"score_spread":0.2933258552189998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905201025","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32588884,0.03648982,0.59103817,0.0018321034,0.0011288208,0.0005087281,0.0022564256,0.017730402,0.02312674],"genre_scores_gemma":[0.75554186,0.006402627,0.22332513,0.00070867105,0.00024543767,0.00014666938,0.0033695824,0.00044970762,0.009810324],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9983038,0.00042510827,0.00009638167,0.0004583153,0.00054465333,0.00017173373],"domain_scores_gemma":[0.9964245,0.0021186378,0.00013093954,0.0004184606,0.0007317171,0.00017581067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0031437683,0.0021193912,0.0013635235,0.0019437639,0.0003446123,0.0012735042,0.0019361969,0.0021045774,0.002587002],"category_scores_gemma":[0.008619556,0.00067345204,0.00053758296,0.0011518005,0.0005757152,0.003751003,0.0015681149,0.0016915778,0.001308387],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015332883,0.00037570758,0.0069576045,0.000944681,0.0005663723,0.00016551948,0.00016149785,0.084743336,0.013863939,0.0035423378,0.007285504,0.87986016],"study_design_scores_gemma":[0.00007412647,0.0010531386,0.0036245673,0.00010383678,0.00018872473,0.0004666318,0.00014119173,0.9616531,0.024189694,0.003895679,0.0045545404,0.000054744545],"about_ca_topic_score_codex":0.008709655,"about_ca_topic_score_gemma":0.012436194,"teacher_disagreement_score":0.008709655,"about_ca_system_score_codex":0.0013034206,"about_ca_system_score_gemma":0.0012588528,"threshold_uncertainty_score":0.01731795},"labels":[],"label_agreement":null},{"id":"W2905227814","doi":"10.1109/lsc.2018.8572169","title":"Segmentation of Patient Images in the Neonatal Intensive Care Unit","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Children's Hospital of Eastern Ontario; Carleton University","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Segmentation; Leverage (statistics); Computer vision; Transfer of learning; RGB color model; Neonatal intensive care unit; Image segmentation; Overhead (engineering); Machine learning; Medicine","score_opus":0.027266329559909153,"score_gpt":0.31955109564843515,"score_spread":0.292284766088526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2905227814","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":[0.8748392,0.0010719851,0.115689136,0.00065583497,0.000071976436,0.00025909106,0.0020026925,0.0010636419,0.0043464173],"genre_scores_gemma":[0.91916525,0.0007029309,0.07670711,0.00015808323,0.00002353169,0.00006877574,0.0016803946,0.00009208154,0.0014018689],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998741,0.000024792529,0.000011785287,0.000033498673,0.000031770367,0.00002398901],"domain_scores_gemma":[0.99987626,0.00004067133,0.000018514871,0.000015012429,0.000031436222,0.000018075249],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017760012,0.00031247252,0.00025318388,0.000796317,0.00018288831,0.0003859243,0.0002956455,0.00057488645,0.0014963377],"category_scores_gemma":[0.0007328535,0.00015011727,0.0002482611,0.0004481374,0.00014212416,0.0001776228,0.00029456033,0.00026498968,0.00039532242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014760252,0.00024130403,0.07088919,0.00048338398,0.0001244694,0.007443374,0.0011837878,0.048256658,0.31243107,0.0014369476,0.0072456137,0.54878825],"study_design_scores_gemma":[0.00004275137,0.0005475543,0.26979923,0.00019011296,0.00010515127,0.010942935,0.0018827844,0.4310366,0.2684552,0.001596704,0.01532317,0.00007775501],"about_ca_topic_score_codex":0.0051455833,"about_ca_topic_score_gemma":0.007086276,"teacher_disagreement_score":0.0051455833,"about_ca_system_score_codex":0.00044763766,"about_ca_system_score_gemma":0.00045546045,"threshold_uncertainty_score":0.0102312565},"labels":[],"label_agreement":null},{"id":"W2907241742","doi":"10.1007/s00521-018-3954-7","title":"Crowd density estimation based on classification activation map and patch density level","year":2019,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":40,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Density estimation; Artificial intelligence; Scale (ratio); Pattern recognition (psychology); Image (mathematics); Perspective (graphical); Regression; Artificial neural network; Discriminant; Task (project management); Mathematics; Statistics; Geography; Cartography","score_opus":0.04819773911735281,"score_gpt":0.3100122474674801,"score_spread":0.26181450835012726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907241742","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15926662,0.00033588472,0.8348213,0.0002514955,0.00012969531,0.00013498994,0.00017975646,0.0011566884,0.0037235415],"genre_scores_gemma":[0.89475954,0.0002378398,0.100168504,0.000081733204,0.000083315674,0.00009559873,0.0003280747,0.0001054621,0.0041399226],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996979,0.00003426673,0.000010033229,0.00010839359,0.00008147064,0.00006793462],"domain_scores_gemma":[0.99960214,0.00009224252,0.000031995816,0.000032973432,0.00020138628,0.000039220533],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004582487,0.0007050591,0.00094431406,0.0014379271,0.0005540335,0.00090368185,0.00089653686,0.00086221495,0.0015307778],"category_scores_gemma":[0.0015732499,0.00035068239,0.00060192397,0.0007744882,0.0004245688,0.00089549366,0.0009583904,0.00059757364,0.0006088481],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007751563,0.0005095438,0.01872042,0.00015847346,0.00018496599,0.00029237848,0.00040371704,0.195135,0.05542274,0.005707071,0.0072553605,0.71543515],"study_design_scores_gemma":[0.0000047331437,0.00002595542,0.0034968983,0.000007214141,0.000018952249,0.000064391286,0.000029003786,0.9905565,0.004294821,0.0012023632,0.0002902303,0.0000089691575],"about_ca_topic_score_codex":0.008940342,"about_ca_topic_score_gemma":0.0073193545,"teacher_disagreement_score":0.008940342,"about_ca_system_score_codex":0.0005546123,"about_ca_system_score_gemma":0.0006282404,"threshold_uncertainty_score":0.017776608},"labels":[],"label_agreement":null},{"id":"W2907265544","doi":"10.3390/rs11010072","title":"Automatic Shadow Detection in Urban Very-High-Resolution Images Using Existing 3D Models for Free Training","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek; University of Twente; York University","keywords":"Computer science; Artificial intelligence; Computer vision; Shadow (psychology); Pattern recognition (psychology); Support vector machine; Rendering (computer graphics); Robustness (evolution)","score_opus":0.06964076164923773,"score_gpt":0.30245433293836393,"score_spread":0.23281357128912622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907265544","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.30326426,0.0007671962,0.6851262,0.00016384895,0.00009998173,0.0001889422,0.00060990476,0.0053692786,0.0044103954],"genre_scores_gemma":[0.71845376,0.0006310347,0.2775077,0.00007366411,0.000026255004,0.00007817728,0.0014790823,0.00025125867,0.0014990612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942774,0.00007155008,0.000023585535,0.00013468429,0.00024199286,0.00010045207],"domain_scores_gemma":[0.9995233,0.000103880346,0.00004527303,0.0001376825,0.0001613848,0.000028545744],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005888007,0.00077000883,0.0007815634,0.0015022518,0.00036244476,0.0010172762,0.0009213323,0.000679944,0.0014562038],"category_scores_gemma":[0.0012456035,0.00046981926,0.0008103198,0.0010198797,0.00042172262,0.0010662224,0.0008199883,0.0005890715,0.0011691148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004953193,0.00032198,0.008965769,0.00042703387,0.00009378569,0.00031392308,0.0003813198,0.15388726,0.092785776,0.0016077782,0.0036011082,0.73711884],"study_design_scores_gemma":[0.000014397824,0.00006464514,0.007914187,0.000026185215,0.000026553167,0.00014877482,0.00013762439,0.9609208,0.02774658,0.00065893977,0.0023139522,0.000027471668],"about_ca_topic_score_codex":0.0044203,"about_ca_topic_score_gemma":0.006607004,"teacher_disagreement_score":0.0044203,"about_ca_system_score_codex":0.00045491615,"about_ca_system_score_gemma":0.000699143,"threshold_uncertainty_score":0.008789182},"labels":[],"label_agreement":null},{"id":"W2907567415","doi":"10.1109/cjece.2018.2875142","title":"Mean Shift Tracker With Grey Prediction for Visual Object Tracking","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Bhattacharyya distance; Computer vision; Tracking (education); Artificial intelligence; Eye tracking; Particle filter; Video tracking; Computer science; Object (grammar); Mean-shift; Computation; Mathematics; Filter (signal processing); Pattern recognition (psychology); Algorithm","score_opus":0.01122335391793357,"score_gpt":0.22714129557261983,"score_spread":0.21591794165468625,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2907567415","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00938474,0.0006769196,0.98816246,0.000077026656,0.00010579355,0.000015817563,0.000021023865,0.0006160261,0.0009402033],"genre_scores_gemma":[0.6366661,0.0015293886,0.3548016,0.0001993757,0.0002323736,0.00008881456,0.00024610505,0.0001715577,0.0060645514],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99942786,0.00007938619,0.000021099719,0.00019115466,0.00023506327,0.00004545932],"domain_scores_gemma":[0.999592,0.00012978273,0.000047753,0.000061374216,0.00014345841,0.000025558365],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076700625,0.0006551332,0.0009001847,0.00071665295,0.0004236884,0.00060594373,0.0009969239,0.00097635837,0.00082042796],"category_scores_gemma":[0.0017900824,0.00040932526,0.0007252814,0.0010967128,0.00046953248,0.0011256814,0.0009477246,0.00096531055,0.00053095166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033679555,0.00006692242,0.003625679,0.00020487889,0.00015072199,0.00023378832,0.00024248386,0.29323998,0.056122344,0.014351265,0.0049245907,0.62650067],"study_design_scores_gemma":[0.000009826318,0.00004159382,0.0005427742,0.000006280494,0.000019298828,0.00009118126,0.0000071889917,0.98977286,0.005467613,0.0019845243,0.0020420286,0.000014835642],"about_ca_topic_score_codex":0.005224892,"about_ca_topic_score_gemma":0.003007857,"teacher_disagreement_score":0.005224892,"about_ca_system_score_codex":0.0005470244,"about_ca_system_score_gemma":0.0008337422,"threshold_uncertainty_score":0.01038897},"labels":[],"label_agreement":null},{"id":"W2908755344","doi":"10.1109/iemcon.2018.8614990","title":"Understanding Tracking Methodology of Kernelized Correlation Filter","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"BitTorrent tracker; Clutter; Eye tracking; Computer science; Tracking (education); Kernel (algebra); Artificial intelligence; Filter (signal processing); Computer vision; Field (mathematics); Circulant matrix; Computation; Correlation; Machine learning; Algorithm; Mathematics; Radar","score_opus":0.43944883202623547,"score_gpt":0.3866131240761635,"score_spread":0.052835707950071975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2908755344","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":[0.0010232801,0.00031602546,0.99762326,0.00006721823,0.000032155713,0.000012415637,0.000014357191,0.00009654054,0.00081466266],"genre_scores_gemma":[0.23169295,0.004010449,0.7531177,0.0002666668,0.00030737158,0.00023350716,0.00028134955,0.0002066698,0.009883328],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995382,0.000089441346,0.000027843338,0.00015906357,0.0001448699,0.00004054645],"domain_scores_gemma":[0.9993812,0.0001956913,0.00008169757,0.000070457805,0.0002374989,0.00003335684],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010814087,0.00044515196,0.000548087,0.00098871,0.00037121872,0.0010776026,0.0011630118,0.0011797779,0.0021859705],"category_scores_gemma":[0.0024080686,0.00035798262,0.00074458297,0.0013007858,0.00060094433,0.0015193037,0.0007532092,0.0009984495,0.0009767072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008759285,0.00004991295,0.002722648,0.00040570795,0.00010240934,0.00038165553,0.0005011872,0.2860759,0.028934507,0.33397847,0.0075190603,0.33924097],"study_design_scores_gemma":[0.000007753817,0.000043525946,0.0006672862,0.000025201745,0.000018176284,0.00032019842,0.000020008565,0.96428883,0.0031154957,0.02212878,0.009333362,0.000031457723],"about_ca_topic_score_codex":0.0055748317,"about_ca_topic_score_gemma":0.0017838478,"teacher_disagreement_score":0.0055748317,"about_ca_system_score_codex":0.0008005217,"about_ca_system_score_gemma":0.0011222167,"threshold_uncertainty_score":0.011084735},"labels":[],"label_agreement":null},{"id":"W2909031416","doi":"10.1109/icmla.2018.00194","title":"Object Counting on Low Quality Images: A Case Study of Near Real-Time Traffic Monitoring","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"Ministère de l'Économie, de la Science et de l'Innovation - Québec","keywords":"Computer science; USable; Crowdsourcing; Quality (philosophy); Real-time computing; Object detection; Transport engineering; Artificial intelligence; Multimedia; Engineering; World Wide Web; Pattern recognition (psychology)","score_opus":0.05218436187016322,"score_gpt":0.37190993760302865,"score_spread":0.3197255757328654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909031416","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9623136,0.0005579637,0.028092744,0.00067933614,0.00008811386,0.00021842401,0.0013903093,0.0009343915,0.005725207],"genre_scores_gemma":[0.95710707,0.00033184516,0.038451184,0.000120593955,0.000060237395,0.00004764821,0.0012522428,0.00014596587,0.0024832757],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99859387,0.000316367,0.00007977603,0.00033821992,0.00048009466,0.00019162385],"domain_scores_gemma":[0.99667406,0.0015095676,0.00033723842,0.00047257222,0.00080965634,0.00019694265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013739027,0.0007623985,0.00054303935,0.0015791,0.00096248573,0.0010466115,0.0015124313,0.0017321118,0.00096437114],"category_scores_gemma":[0.0052260747,0.00028780356,0.00034002247,0.0019467553,0.00069015,0.0009203421,0.00060899527,0.00079834333,0.0005419261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022194677,0.0027445515,0.16475537,0.0021056687,0.00038398843,0.030504216,0.0072956644,0.13899502,0.12363208,0.0045294235,0.03218014,0.4906545],"study_design_scores_gemma":[0.00015459617,0.00095819874,0.2579299,0.00031768455,0.0002657241,0.01161628,0.007026084,0.5320934,0.14550139,0.0055191093,0.038421366,0.00019623338],"about_ca_topic_score_codex":0.026559655,"about_ca_topic_score_gemma":0.054179255,"teacher_disagreement_score":0.026559655,"about_ca_system_score_codex":0.0011674906,"about_ca_system_score_gemma":0.00059048244,"threshold_uncertainty_score":0.052810133},"labels":[],"label_agreement":null},{"id":"W2909408920","doi":"10.1109/iemcon.2018.8615054","title":"Video Predictive Object Detector","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Object detection; Detector; Computer vision; Feature (linguistics); Video tracking; Object (grammar); Pattern recognition (psychology); Feature extraction; Tracking (education); Layer (electronics)","score_opus":0.02042811941407555,"score_gpt":0.2903070092912015,"score_spread":0.26987888987712594,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909408920","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03829538,0.002096465,0.93698686,0.0005366602,0.00065640773,0.00023762678,0.0024363815,0.010278418,0.008475847],"genre_scores_gemma":[0.50063056,0.001324765,0.4677485,0.0010692432,0.00034353416,0.0003205855,0.00794579,0.0004879891,0.020129],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994578,0.00003864506,0.000015573893,0.00023174899,0.00015991754,0.00009635901],"domain_scores_gemma":[0.9993111,0.00018928808,0.000052694446,0.000112110116,0.0002912036,0.00004376934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00091499585,0.0012548366,0.0010897191,0.0014691232,0.00036968567,0.0011005638,0.0021430168,0.0013298633,0.0034441329],"category_scores_gemma":[0.0021627005,0.00046421346,0.000660528,0.0012107897,0.0003727242,0.0013701213,0.0011345645,0.0013535732,0.0023731184],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005903231,0.00023048924,0.004759232,0.00019940789,0.0001211578,0.00030193513,0.00006326001,0.05143786,0.033607334,0.006318327,0.038570542,0.8638],"study_design_scores_gemma":[0.00002237331,0.00011544334,0.0019620045,0.000032208103,0.000044739045,0.00025287076,0.000032848428,0.95325154,0.031195087,0.004551107,0.0085128965,0.00002688315],"about_ca_topic_score_codex":0.007390932,"about_ca_topic_score_gemma":0.009226903,"teacher_disagreement_score":0.007390932,"about_ca_system_score_codex":0.0010310246,"about_ca_system_score_gemma":0.0010259716,"threshold_uncertainty_score":0.014695823},"labels":[],"label_agreement":null},{"id":"W2909576138","doi":"10.1007/s00521-018-03996-8","title":"Adaptive sampling for UAV tracking","year":2019,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computational Science and Engineering; Computer science; Adaptive sampling; Tracking (education); Sampling (signal processing); Artificial intelligence; Computer vision; Machine learning; Mathematics; Statistics; Monte Carlo method; Psychology","score_opus":0.06405725881614496,"score_gpt":0.34684570381334473,"score_spread":0.2827884449971998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2909576138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013424502,0.00048301366,0.98475283,0.000084919164,0.000091617796,0.00001308638,0.000034202178,0.00011307248,0.0010027966],"genre_scores_gemma":[0.7370879,0.0008920468,0.25386012,0.00014738613,0.00020532911,0.00010397221,0.0001973614,0.000064573826,0.007441242],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998178,0.000043212098,0.000007198446,0.000049079612,0.00006138843,0.000021353997],"domain_scores_gemma":[0.9995158,0.00029689918,0.00003708418,0.000043860087,0.00008504389,0.000021355812],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037466202,0.00033109015,0.00047860717,0.00030270635,0.00023579519,0.00035633278,0.0005619528,0.0004883519,0.0011288186],"category_scores_gemma":[0.002105592,0.0002409441,0.00029497934,0.00050828245,0.00031570796,0.0005314519,0.00049619836,0.0007180144,0.0002044568],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030651502,0.00007626322,0.0012680971,0.00012603561,0.000055647208,0.00007949792,0.00007680245,0.6133805,0.01788324,0.03315112,0.0036368596,0.32995942],"study_design_scores_gemma":[0.0000028393972,0.000008602149,0.00011379473,0.0000019284757,0.0000019388394,0.0000076291326,0.0000018356423,0.9971474,0.0005491606,0.0018740532,0.00028911198,0.0000017140641],"about_ca_topic_score_codex":0.00696946,"about_ca_topic_score_gemma":0.0056653777,"teacher_disagreement_score":0.00696946,"about_ca_system_score_codex":0.00047764368,"about_ca_system_score_gemma":0.000369535,"threshold_uncertainty_score":0.013857782},"labels":[],"label_agreement":null},{"id":"W2910102176","doi":"10.1007/s00138-019-01004-0","title":"Hard negative mining for correlation filters in visual tracking","year":2019,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Eye tracking; Tracking (education); Frame (networking); Computer vision; Video tracking; Task (project management); Correlation; Pattern recognition (psychology); Noise (video); Object (grammar); Image (mathematics); Mathematics; Engineering","score_opus":0.019031599627251216,"score_gpt":0.3370084789500506,"score_spread":0.3179768793227994,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910102176","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014319578,0.0004627954,0.9832008,0.00028152746,0.00008882909,0.000061554885,0.00009988552,0.0005847426,0.0009003175],"genre_scores_gemma":[0.4748196,0.0007953743,0.5075773,0.00074504106,0.0005003106,0.00031809843,0.0013423433,0.00050230545,0.013399525],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9965558,0.0009706518,0.00024586642,0.00089374935,0.0009690434,0.0003648537],"domain_scores_gemma":[0.9836565,0.011604749,0.0010287,0.0014715715,0.0018197219,0.00041863543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006362965,0.0016623782,0.0028366984,0.0019585097,0.0014727854,0.0026876146,0.0035992772,0.0028289212,0.003514448],"category_scores_gemma":[0.02472143,0.0012487771,0.0015064125,0.0016976058,0.0019519913,0.003378786,0.0027651559,0.0030476307,0.0013285151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011922364,0.0007181611,0.0055749146,0.0006120087,0.0002959138,0.00037680124,0.00022762011,0.21697895,0.014877565,0.056335837,0.013954214,0.68885577],"study_design_scores_gemma":[0.000030945826,0.000070996466,0.00061805744,0.00002845686,0.000028825682,0.00009779952,0.000023797831,0.97886413,0.0024980903,0.01668491,0.0010390263,0.000014907974],"about_ca_topic_score_codex":0.0046780175,"about_ca_topic_score_gemma":0.0066068405,"teacher_disagreement_score":0.006362965,"about_ca_system_score_codex":0.0012396754,"about_ca_system_score_gemma":0.0026352522,"threshold_uncertainty_score":0.033650935},"labels":[],"label_agreement":null},{"id":"W2910312205","doi":"10.1109/nics.2018.8606878","title":"Deep Learning-based Multiple Objects Detection and Tracking System for Socially Aware Mobile Robot Navigation Framework","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"","keywords":"Computer science; Artificial intelligence; Mobile robot; Computer vision; Convolutional neural network; Mobile robot navigation; Object detection; Deep learning; Robot; Tracking system; Service robot; Social robot; Robot control; Pattern recognition (psychology); Kalman filter","score_opus":0.020077039049713332,"score_gpt":0.29591811293962694,"score_spread":0.27584107388991363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910312205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055649847,0.0002398123,0.9396732,0.00016668187,0.00005129966,0.00005677802,0.000079970785,0.0019923826,0.0020900858],"genre_scores_gemma":[0.80323267,0.0002064134,0.19152127,0.00021572794,0.000036965277,0.000117451425,0.00026923543,0.000039077942,0.0043612705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999724,0.000033591816,0.000013386237,0.0000827123,0.00009252707,0.000053897627],"domain_scores_gemma":[0.9998011,0.00002390361,0.000034259352,0.00002338487,0.00009288584,0.000024547693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031913363,0.0004992157,0.0005076,0.00044036316,0.00032353733,0.0003222804,0.0009918819,0.0007044231,0.0009163486],"category_scores_gemma":[0.00053093117,0.00022891795,0.00034508636,0.0002619884,0.00024831627,0.0007280837,0.0008017107,0.00056648813,0.00035516563],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003432071,0.00040489883,0.008239937,0.00013386036,0.00014291973,0.00039322555,0.00026352648,0.1554352,0.09369041,0.0060686087,0.0059927977,0.72889143],"study_design_scores_gemma":[0.000008104016,0.00006709811,0.0013033244,0.000006255243,0.00002458755,0.00007617488,0.000024758141,0.9851365,0.010815707,0.0012312819,0.0012914188,0.000014722839],"about_ca_topic_score_codex":0.008961881,"about_ca_topic_score_gemma":0.011910564,"teacher_disagreement_score":0.008961881,"about_ca_system_score_codex":0.00072604977,"about_ca_system_score_gemma":0.000980351,"threshold_uncertainty_score":0.017819464},"labels":[],"label_agreement":null},{"id":"W2910563408","doi":"10.1109/iros.2018.8593551","title":"Real-Time Edge Template Tracking via Homography Estimation","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Homography; Artificial intelligence; Computer science; Computer vision; Pixel; Enhanced Data Rates for GSM Evolution; Frame (networking); Feature (linguistics); Tracking (education); Frame rate; Template; Feature extraction; Edge detection; Code (set theory); Pattern recognition (psychology); Image (mathematics); Image processing; Mathematics; Set (abstract data type)","score_opus":0.022730163096543212,"score_gpt":0.2992147196005832,"score_spread":0.27648455650404,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2910563408","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00765058,0.0002566216,0.98970616,0.000023726136,0.000038767866,0.00002482471,0.00009125036,0.0016373595,0.00057072006],"genre_scores_gemma":[0.16611387,0.0005189445,0.8280964,0.00008203709,0.00005692795,0.00007385905,0.00092892896,0.00038831137,0.003740763],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99933213,0.00006359935,0.000030605097,0.00022604641,0.0002905088,0.000056969217],"domain_scores_gemma":[0.9994568,0.000105346626,0.000077792116,0.00016049569,0.00017236813,0.000027086437],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043244325,0.00075034646,0.0010303205,0.0015183389,0.0002907166,0.0009683573,0.0015085168,0.00084813935,0.0019842498],"category_scores_gemma":[0.00162963,0.0005192957,0.0006462134,0.0015000384,0.00029916907,0.0015393676,0.00085916906,0.0008975925,0.0016875315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001825067,0.000078122575,0.0015871777,0.0001312263,0.00008677211,0.000100798905,0.000083920226,0.058699097,0.0526096,0.00343689,0.0038208305,0.87918305],"study_design_scores_gemma":[0.000022058488,0.000075314616,0.0023279507,0.000018193283,0.000030059336,0.00044567126,0.00003429504,0.9345067,0.05368821,0.0026028482,0.006209304,0.000039427858],"about_ca_topic_score_codex":0.0032478676,"about_ca_topic_score_gemma":0.0036545242,"teacher_disagreement_score":0.0032478676,"about_ca_system_score_codex":0.00037671134,"about_ca_system_score_gemma":0.0006334043,"threshold_uncertainty_score":0.006637931},"labels":[],"label_agreement":null},{"id":"W2911115917","doi":"10.1109/iemcon.2018.8614874","title":"Real-Time Experimental Study of Kernelized Correlation Filter Tracker using RGB Kinect Camera","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Artificial intelligence; RGB color model; Computer science; Computer graphics (images)","score_opus":0.047301292066557525,"score_gpt":0.3438960280469895,"score_spread":0.29659473598043196,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911115917","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":[0.8423394,0.00096334354,0.1461142,0.00030638286,0.0005195137,0.00026631978,0.0010261214,0.0027371016,0.0057275053],"genre_scores_gemma":[0.9637255,0.0003267414,0.032522846,0.00009479105,0.000030169484,0.0001323906,0.00083389797,0.00013511421,0.0021984086],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99869126,0.00018079752,0.00009453046,0.00034078496,0.0005214783,0.00017118217],"domain_scores_gemma":[0.99808735,0.0004423702,0.00016808214,0.00023042456,0.0008883849,0.00018323208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013795727,0.00064433593,0.00081305,0.0008490038,0.000373406,0.0006649893,0.0008482194,0.00081863144,0.002357291],"category_scores_gemma":[0.004114208,0.00028187144,0.00034135734,0.000756501,0.0004416018,0.0008240329,0.00059916853,0.000602297,0.00061646727],"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":[0.0042513344,0.001707971,0.025381174,0.001548612,0.00031604656,0.0018470872,0.0013928745,0.075081594,0.5432914,0.0035721026,0.011073829,0.330536],"study_design_scores_gemma":[0.00021563223,0.003907985,0.06734659,0.00014449317,0.00014241156,0.0014908657,0.00059792027,0.6893283,0.22927286,0.0010875353,0.00627044,0.00019498033],"about_ca_topic_score_codex":0.003975597,"about_ca_topic_score_gemma":0.0029813335,"teacher_disagreement_score":0.003975597,"about_ca_system_score_codex":0.00036703874,"about_ca_system_score_gemma":0.0007134608,"threshold_uncertainty_score":0.007904947},"labels":[],"label_agreement":null},{"id":"W2911372736","doi":"10.1007/978-3-030-11021-5_28","title":"VisDrone-SOT2018: The Vision Meets Drone Single-Object Tracking Challenge Results","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":52,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Clutter; BitTorrent tracker; Drone; Video tracking; Tracking (education); Benchmark (surveying); Eye tracking; Object (grammar); Bounding overwatch; Radar; Cartography","score_opus":0.03775203663814282,"score_gpt":0.2911304752830152,"score_spread":0.2533784386448724,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2911372736","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2280257,0.038922556,0.38191214,0.011456158,0.022753157,0.0028408158,0.10247498,0.06929953,0.14231487],"genre_scores_gemma":[0.23936717,0.004033643,0.25384894,0.0028858446,0.0014673519,0.0007461442,0.42503262,0.0033867923,0.06923155],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9974074,0.00045062925,0.00010857648,0.00081227673,0.00085566286,0.0003655155],"domain_scores_gemma":[0.9980411,0.00051946833,0.000044548866,0.00054667034,0.0005281754,0.00032014246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035134095,0.003903086,0.004462701,0.001448323,0.0016119251,0.0031082574,0.0029978002,0.0037728033,0.006560622],"category_scores_gemma":[0.005818095,0.000582027,0.0015923618,0.0014158039,0.000856353,0.0025933834,0.0029657334,0.0030548018,0.0071107894],"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":[0.001516427,0.0010220592,0.0017306971,0.0013088217,0.0005271949,0.00035538888,0.00018998656,0.040173773,0.010374583,0.006494842,0.534661,0.4016452],"study_design_scores_gemma":[0.0009165231,0.0014597541,0.0075977044,0.00037829808,0.000366011,0.0010326547,0.0006997561,0.625251,0.0302682,0.029691827,0.3021602,0.00017812946],"about_ca_topic_score_codex":0.026649352,"about_ca_topic_score_gemma":0.04015525,"teacher_disagreement_score":0.026649352,"about_ca_system_score_codex":0.00120143,"about_ca_system_score_gemma":0.002301232,"threshold_uncertainty_score":0.05298847},"labels":[],"label_agreement":null},{"id":"W2913356243","doi":"10.1007/s11042-018-7116-9","title":"Unsupervised learning of finite full covariance multivariate generalized Gaussian mixture models for human activity recognition","year":2019,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Covariance; Multivariate statistics; Mixture model; Multivariate normal distribution; Gaussian; Artificial intelligence; Pattern recognition (psychology); Machine learning; Gaussian process; Applied mathematics; Statistics; Mathematics","score_opus":0.06527805924327358,"score_gpt":0.312679266687955,"score_spread":0.24740120744468141,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913356243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009282383,0.00020014565,0.9896381,0.00006195039,0.00001971606,0.000018565932,0.000086951695,0.00048828265,0.00020387374],"genre_scores_gemma":[0.54955506,0.00065119175,0.44147924,0.0002470759,0.0001390368,0.00030227526,0.0020110523,0.00043798613,0.005177148],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987697,0.00049636594,0.00006278422,0.00037429307,0.0001745402,0.00012236339],"domain_scores_gemma":[0.99822503,0.0010270337,0.00014604742,0.00029871086,0.00023945072,0.00006373212],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017179773,0.0008122308,0.0015059005,0.0009902493,0.00036889318,0.000939792,0.002527019,0.0013319782,0.0012753007],"category_scores_gemma":[0.0057551693,0.0008638716,0.00205447,0.0011476497,0.0008971825,0.0014433764,0.0016644591,0.002070947,0.0009850848],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036090385,0.00031282724,0.0030196477,0.00014582544,0.0003443132,0.00008955568,0.0002531565,0.51252764,0.008110779,0.017244823,0.004144878,0.45344564],"study_design_scores_gemma":[0.0000043400164,0.0000135861055,0.00039952397,0.00000575979,0.000009815127,0.000017931641,0.000008245941,0.993972,0.00052357157,0.0047814087,0.0002561372,0.0000076748975],"about_ca_topic_score_codex":0.008667103,"about_ca_topic_score_gemma":0.0144287115,"teacher_disagreement_score":0.008667103,"about_ca_system_score_codex":0.0007331148,"about_ca_system_score_gemma":0.0011675507,"threshold_uncertainty_score":0.017233312},"labels":[],"label_agreement":null},{"id":"W2913401361","doi":"10.1007/s11760-018-1406-6","title":"Real-time pedestrian detection with deep supervision in the wild","year":2019,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Pedestrian; Pedestrian detection; Artificial intelligence; Computer science; Real-time computing; Computer vision; Transport engineering; Engineering","score_opus":0.011851109658672945,"score_gpt":0.26484189062428315,"score_spread":0.25299078096561023,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913401361","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.220451,0.0006290315,0.7647448,0.00040214672,0.00037344967,0.00007859928,0.0008469308,0.008770438,0.0037035644],"genre_scores_gemma":[0.8590932,0.00014608425,0.13344157,0.00017343204,0.00008961013,0.000037525413,0.0017000183,0.00019727724,0.005121171],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993919,0.00010185335,0.000014384702,0.0002231277,0.00012902894,0.00013970195],"domain_scores_gemma":[0.9993125,0.00015542319,0.000051282026,0.00017081967,0.000220012,0.00008993574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00082520687,0.0011741415,0.001329189,0.0006687852,0.00036833517,0.0006196234,0.0014970589,0.000973701,0.0023452735],"category_scores_gemma":[0.0015210682,0.0007599439,0.00054515194,0.0005868918,0.00048765208,0.0012840899,0.0013385441,0.0012962334,0.0014613152],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0018587986,0.0010462644,0.007133591,0.00015604548,0.00019277814,0.00034726583,0.00011163752,0.121610716,0.05261019,0.002096447,0.023506101,0.7893302],"study_design_scores_gemma":[0.000010236768,0.00005161712,0.0011660958,0.000003787326,0.0000097026395,0.000058876907,0.000010295197,0.9923034,0.004790381,0.001081639,0.00050745,0.000006492269],"about_ca_topic_score_codex":0.007081191,"about_ca_topic_score_gemma":0.013510423,"teacher_disagreement_score":0.007081191,"about_ca_system_score_codex":0.00042433615,"about_ca_system_score_gemma":0.0009906575,"threshold_uncertainty_score":0.014079928},"labels":[],"label_agreement":null},{"id":"W2913466723","doi":"10.3390/app9030470","title":"An Indoor Room Classification System for Social Robots via Integration of CNN and ECOC","year":2019,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Classifier (UML); Robot; Humanoid robot; Pattern recognition (psychology); Machine learning","score_opus":0.05374945286205018,"score_gpt":0.325780461708405,"score_spread":0.27203100884635484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913466723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.32303086,0.00093455764,0.64292437,0.0004959123,0.00051590917,0.00030701363,0.0012085973,0.015490287,0.015092433],"genre_scores_gemma":[0.8286237,0.00020872221,0.16107449,0.000332023,0.000065952205,0.00016120743,0.0012446018,0.00013031255,0.008159042],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999775,0.000018928808,0.000007542764,0.000088789704,0.00006113751,0.000048642538],"domain_scores_gemma":[0.99978703,0.00002346559,0.000030095724,0.000035623278,0.00010186341,0.000021794523],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026819526,0.0009093223,0.00045149305,0.00065248593,0.00029943185,0.00031810658,0.00094103615,0.00055673934,0.0016385262],"category_scores_gemma":[0.00057523855,0.0002383921,0.00039706886,0.0003745665,0.000209383,0.0006574644,0.0009358423,0.00046063334,0.00087519665],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048590192,0.00029005174,0.016590632,0.00016166737,0.00016918732,0.000687937,0.00015823713,0.052151047,0.07579958,0.0013308215,0.012064124,0.8401108],"study_design_scores_gemma":[0.00002185255,0.00023940539,0.014849752,0.000041303552,0.00010663767,0.000300028,0.00015328902,0.93564236,0.040394943,0.0014612741,0.006737266,0.000051808835],"about_ca_topic_score_codex":0.0104452735,"about_ca_topic_score_gemma":0.016831407,"teacher_disagreement_score":0.0104452735,"about_ca_system_score_codex":0.0005324404,"about_ca_system_score_gemma":0.0005189726,"threshold_uncertainty_score":0.02076894},"labels":[],"label_agreement":null},{"id":"W2913568723","doi":"10.1109/cisp-bmei.2018.8633130","title":"Foreground Segmentation in Video Sequences with a Dynamic Background","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Pixel; Computer science; Segmentation; Computer vision; Foreground detection; Image segmentation; Pattern recognition (psychology); Background subtraction; Feature (linguistics); Hue; HSL and HSV; Ground truth","score_opus":0.028772467003271924,"score_gpt":0.32181438201533585,"score_spread":0.29304191501206395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913568723","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11091407,0.001367969,0.8847039,0.000119544784,0.000091422124,0.00008822249,0.0001045914,0.0008927695,0.0017174932],"genre_scores_gemma":[0.42724988,0.0013244338,0.5687703,0.00012561756,0.00008864434,0.0000580044,0.00045617338,0.00013937935,0.0017876439],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969745,0.000052821302,0.000021591082,0.00007952924,0.00010803801,0.000040556544],"domain_scores_gemma":[0.99951863,0.00018844956,0.00008872275,0.000047517187,0.0001255498,0.00003110484],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004566375,0.0006233084,0.0006139668,0.0015110306,0.0004112508,0.0005942182,0.00050128275,0.00054432347,0.00067085994],"category_scores_gemma":[0.0015491032,0.0002734092,0.0003516955,0.0010729316,0.0005285935,0.0007435477,0.00038170087,0.00044570526,0.00037246206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005438454,0.00008418067,0.0020054923,0.0004043093,0.000053143453,0.00081116933,0.00038979764,0.04571863,0.45933315,0.004638814,0.0012839185,0.4847335],"study_design_scores_gemma":[0.000026654632,0.00025130127,0.009423727,0.000102146674,0.00007394983,0.0014588885,0.00034270267,0.7368009,0.23573504,0.0065323734,0.009208346,0.000043887383],"about_ca_topic_score_codex":0.002449291,"about_ca_topic_score_gemma":0.0031969778,"teacher_disagreement_score":0.002449291,"about_ca_system_score_codex":0.00037822858,"about_ca_system_score_gemma":0.00037459985,"threshold_uncertainty_score":0.004870057},"labels":[],"label_agreement":null},{"id":"W2913977709","doi":"10.3390/s19040750","title":"Deep Attention Models for Human Tracking Using RGBD","year":2019,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Camouflage; Artificial intelligence; Computer science; Computer vision; RGB color model; Object (grammar); Feature (linguistics); Tracking (education); Active appearance model; Video tracking; Layer (electronics); Modular design; Eye tracking; Property (philosophy); Pattern recognition (psychology); Image (mathematics)","score_opus":0.07473545932203214,"score_gpt":0.3354438198992686,"score_spread":0.26070836057723645,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2913977709","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039391704,0.0014526531,0.9518909,0.00043670912,0.00014543335,0.0000387489,0.00030093684,0.0029243766,0.0034185045],"genre_scores_gemma":[0.8977961,0.00083646964,0.09146192,0.00035796946,0.00009363125,0.00010191908,0.00048285682,0.00012987231,0.008739319],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979466,0.00002819028,0.000008270816,0.00008261204,0.000045923636,0.00004044068],"domain_scores_gemma":[0.999764,0.000084047366,0.000031972842,0.000029637804,0.000071206574,0.000019212093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005312321,0.00095519435,0.00054543075,0.0006162258,0.00028476462,0.00070097926,0.0016095166,0.001025758,0.0027809595],"category_scores_gemma":[0.0013172238,0.00049333804,0.0007822679,0.00071363855,0.00043431018,0.0009753918,0.0009582384,0.0013380131,0.00086662365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018589433,0.00008544043,0.0011802925,0.00006398729,0.000060679373,0.00006212848,0.00007856862,0.8296397,0.007285606,0.0049737114,0.002506584,0.15387747],"study_design_scores_gemma":[0.0000017912441,0.000008515821,0.00016864135,0.000002614605,0.0000046887053,0.0000052140485,0.0000013220623,0.99794906,0.00046367457,0.0012076467,0.0001845174,0.0000023194707],"about_ca_topic_score_codex":0.031430397,"about_ca_topic_score_gemma":0.027853375,"teacher_disagreement_score":0.031430397,"about_ca_system_score_codex":0.0017803445,"about_ca_system_score_gemma":0.00071447185,"threshold_uncertainty_score":0.062494874},"labels":[],"label_agreement":null},{"id":"W2914207527","doi":"10.1007/s11042-019-7275-3","title":"Bayesian frameworks for traffic scenes monitoring via view-based 3D cars models recognition","year":2019,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Taif University","keywords":"Computer science; Machine learning; Markov chain Monte Carlo; Inference; Artificial intelligence; Flexibility (engineering); Bayesian inference; Reversible-jump Markov chain Monte Carlo; Bayesian probability; Dirichlet distribution; Dirichlet process; Data mining","score_opus":0.046774939507639265,"score_gpt":0.3007108778880702,"score_spread":0.25393593838043094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2914207527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036912467,0.00023623144,0.99532557,0.00005597376,0.000012307945,0.000014149724,0.00007561702,0.00021080827,0.000378119],"genre_scores_gemma":[0.5412796,0.0017925801,0.4494058,0.00020666893,0.00022921391,0.00023554235,0.001322111,0.00025817484,0.0052703316],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988949,0.00025197395,0.000058185935,0.0003186908,0.00033370464,0.00014266185],"domain_scores_gemma":[0.9989598,0.00039087003,0.00018397946,0.00011707078,0.0002841508,0.000064189386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014072494,0.00090679084,0.0014901565,0.0018147401,0.00050799304,0.001675483,0.002483213,0.0014659007,0.0016191562],"category_scores_gemma":[0.0036436354,0.0011959403,0.0015590495,0.0016632765,0.0009356813,0.0017197742,0.0017595717,0.0014958049,0.0007786768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001530603,0.00010950655,0.0016414818,0.00010537131,0.00013696351,0.00009660253,0.00011370181,0.7558336,0.005345451,0.035934642,0.0021451758,0.19838437],"study_design_scores_gemma":[0.000002498155,0.00000870874,0.0002454268,0.000006750901,0.000009086237,0.000013236532,0.0000060422767,0.99294466,0.0003275025,0.00604249,0.0003851904,0.000008345071],"about_ca_topic_score_codex":0.025387473,"about_ca_topic_score_gemma":0.03233842,"teacher_disagreement_score":0.025387473,"about_ca_system_score_codex":0.0012507823,"about_ca_system_score_gemma":0.0016920648,"threshold_uncertainty_score":0.050479412},"labels":[],"label_agreement":null},{"id":"W2915348692","doi":"10.1109/wacvw50321.2020.9096925","title":"Similarity Learning Networks for Animal Individual Re-Identification - Beyond the Capabilities of a Human Observer","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Guelph","funders":"","keywords":"Artificial intelligence; Identification (biology); Convolutional neural network; Similarity (geometry); Computer science; Population; Machine learning; Deep learning; Set (abstract data type); Animal species; Range (aeronautics); Task (project management); Pattern recognition (psychology); Ecology; Biology; Image (mathematics); Evolutionary biology","score_opus":0.10815845601220406,"score_gpt":0.3431400258965086,"score_spread":0.23498156988430455,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2915348692","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.29376578,0.0040875347,0.6857886,0.001540794,0.0002773481,0.00015600742,0.0009571416,0.0049761827,0.00845062],"genre_scores_gemma":[0.8562654,0.0005557096,0.13633765,0.00031965596,0.00009451906,0.00006601351,0.0015913201,0.00017478241,0.004594886],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987224,0.00032724268,0.000055694025,0.00051661325,0.0002653134,0.000112719485],"domain_scores_gemma":[0.99821174,0.0005230529,0.00029641006,0.0005422062,0.00032255403,0.000103997525],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002219807,0.00092818623,0.00080958154,0.0012864824,0.00043429586,0.0010661712,0.0014890622,0.0014575544,0.0019307836],"category_scores_gemma":[0.0065681473,0.00027844,0.00068566675,0.00090694503,0.0008940711,0.0038456628,0.002127044,0.0017708087,0.0010672442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062913063,0.000264991,0.0140125705,0.00022405041,0.00024295336,0.00026457713,0.0003511089,0.2966409,0.016459081,0.014256239,0.009353248,0.6473011],"study_design_scores_gemma":[0.000013001971,0.00015755605,0.0029570286,0.000026464792,0.000033141332,0.0001784173,0.000108266344,0.96435815,0.00910291,0.019480363,0.0035590811,0.000025688794],"about_ca_topic_score_codex":0.004601401,"about_ca_topic_score_gemma":0.0046627894,"teacher_disagreement_score":0.004601401,"about_ca_system_score_codex":0.001078283,"about_ca_system_score_gemma":0.0007641984,"threshold_uncertainty_score":0.011739612},"labels":[],"label_agreement":null},{"id":"W2916102438","doi":"10.1007/s00521-019-04057-4","title":"Pedestrian detection via deep segmentation and context network","year":2019,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Segmentation; Minimum bounding box; Context (archaeology); Pedestrian detection; Artificial intelligence; Feature (linguistics); Pedestrian; Bounding overwatch; Pattern recognition (psychology); Deep learning; Spatial contextual awareness; Machine learning; Computer vision; Image (mathematics)","score_opus":0.01357085219647437,"score_gpt":0.27640131818416963,"score_spread":0.26283046598769527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2916102438","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06909907,0.0015682443,0.92046297,0.00033227418,0.00024077392,0.000094725816,0.00040627774,0.004110627,0.0036849072],"genre_scores_gemma":[0.6317208,0.0009040816,0.35577697,0.0004367519,0.00021987323,0.00010773142,0.0012079261,0.00026566116,0.009360312],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962914,0.00003483168,0.00001035371,0.00016931814,0.00006690926,0.000089509645],"domain_scores_gemma":[0.99977475,0.00004826698,0.000025301533,0.000042779262,0.000074650205,0.00003432542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038072275,0.0012699026,0.0014473385,0.0014691856,0.0006227882,0.0008655458,0.0013737266,0.0012475906,0.002425392],"category_scores_gemma":[0.0007477639,0.00082913996,0.00094902806,0.0013174665,0.000412722,0.0010066997,0.0013874262,0.0013612598,0.0011395046],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065687665,0.00036325195,0.0034823415,0.00012453762,0.00013640655,0.00019546345,0.0000845402,0.060031425,0.040801615,0.0063323756,0.008678388,0.8791127],"study_design_scores_gemma":[0.000010231399,0.00005766455,0.0014052399,0.000016382692,0.000039222232,0.000093791634,0.000015018271,0.9841925,0.00873052,0.0039453385,0.0014809249,0.000013339298],"about_ca_topic_score_codex":0.013897582,"about_ca_topic_score_gemma":0.027266333,"teacher_disagreement_score":0.013897582,"about_ca_system_score_codex":0.00081897865,"about_ca_system_score_gemma":0.001201849,"threshold_uncertainty_score":0.027633369},"labels":[],"label_agreement":null},{"id":"W2917598361","doi":"","title":"Night Vision Technology","year":2018,"lang":"en","type":"article","venue":"Iconic Research And Engineering Journals","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Parsing; Dusk; Pixel; Population","score_opus":0.07003613503894553,"score_gpt":0.4189165569925835,"score_spread":0.348880421953638,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2917598361","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":[0.009274873,0.011339227,0.3368579,0.0022487766,0.0035844338,0.0007873393,0.05164353,0.16570558,0.4185584],"genre_scores_gemma":[0.1029823,0.009954331,0.3579333,0.0046289666,0.0010370301,0.0009436434,0.20118341,0.018411249,0.30292574],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842834,0.00012322934,0.00008054871,0.0004696938,0.00071914593,0.00017903587],"domain_scores_gemma":[0.99805236,0.00011454625,0.0000631261,0.0007654291,0.00082949223,0.00017494106],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011845783,0.0013807662,0.00087328657,0.0023281788,0.0011146256,0.0048980257,0.00274029,0.0016911196,0.115444],"category_scores_gemma":[0.0023742083,0.0007653269,0.0011097842,0.0017697135,0.00044261344,0.0039629475,0.0031542538,0.0022605343,0.12486697],"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":[0.00026628617,0.00010450356,0.0007581556,0.0005026316,0.00007120152,0.00012428463,0.00012651978,0.0006058782,0.01341096,0.008392118,0.59406745,0.38156992],"study_design_scores_gemma":[0.000038300142,0.000061623214,0.0018846329,0.00014194271,0.00002118684,0.00040644413,0.00010488131,0.00710532,0.012364988,0.008861705,0.9689472,0.000061865816],"about_ca_topic_score_codex":0.0061526364,"about_ca_topic_score_gemma":0.008973815,"teacher_disagreement_score":0.115444,"about_ca_system_score_codex":0.0013141361,"about_ca_system_score_gemma":0.0015114257,"threshold_uncertainty_score":0.38619864},"labels":[],"label_agreement":null},{"id":"W2919688329","doi":"10.1109/tits.2019.2899051","title":"Online Multiple Maneuvering Vehicle Tracking System Based on Multi-Model Smooth Variable Structure Filter","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Variable (mathematics); Computer science; Tracking (education); Vehicle dynamics; Computer vision; Control theory (sociology); Artificial intelligence; Engineering; Mathematics; Aerospace engineering; Control (management)","score_opus":0.03953043588648853,"score_gpt":0.27307534437820596,"score_spread":0.23354490849171744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2919688329","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030714916,0.00016979846,0.96707493,0.00008909382,0.00005282552,0.000031706662,0.000026206351,0.00080177403,0.0010389134],"genre_scores_gemma":[0.80211776,0.0002212978,0.19165158,0.00012900506,0.00007473378,0.00012207039,0.00019745316,0.00004821119,0.0054377792],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996481,0.00003904573,0.000018435385,0.0001294555,0.000117448784,0.000047485966],"domain_scores_gemma":[0.9996606,0.000099379744,0.00005381661,0.00004364141,0.000116018935,0.000026432997],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00048267946,0.00057941896,0.0010794813,0.00034756196,0.0004540012,0.00050447055,0.00093838514,0.00095133885,0.0011386095],"category_scores_gemma":[0.0007440279,0.00030432187,0.00057053776,0.0003793408,0.00025206775,0.00086266693,0.0005063921,0.0007912348,0.00041867234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038908236,0.00024094708,0.002703666,0.00021320909,0.0001341565,0.00023888033,0.0003107086,0.47147968,0.071144715,0.005847294,0.0032317413,0.44406593],"study_design_scores_gemma":[0.000014480115,0.000053375425,0.00027409743,0.0000024424805,0.00001020618,0.000027042743,0.000004994301,0.9968478,0.0019544084,0.0003536166,0.00045010998,0.0000074256777],"about_ca_topic_score_codex":0.006318246,"about_ca_topic_score_gemma":0.004855733,"teacher_disagreement_score":0.006318246,"about_ca_system_score_codex":0.0004209268,"about_ca_system_score_gemma":0.0008147633,"threshold_uncertainty_score":0.012562931},"labels":[],"label_agreement":null},{"id":"W2922282711","doi":"10.1109/wacv.2019.00141","title":"Crowd Counting Using Scale-Aware Attention Networks","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Benchmark (surveying); Computer science; Scale (ratio); Artificial intelligence; Pixel; Image (mathematics); Focus (optics); Computer vision; Pattern recognition (psychology); Geography; Cartography","score_opus":0.0456069500258801,"score_gpt":0.3181265693827793,"score_spread":0.27251961935689917,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922282711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062006157,0.0019298336,0.92524135,0.0011358791,0.00028177456,0.00013414478,0.0003119699,0.0017501411,0.0072087925],"genre_scores_gemma":[0.8833669,0.0011798122,0.104833595,0.00060446165,0.00046954933,0.00015557435,0.0005447956,0.00022438713,0.008620947],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99919194,0.00019117935,0.00003011139,0.00030306503,0.00016406734,0.00011966369],"domain_scores_gemma":[0.99864477,0.00066510023,0.00022542929,0.000112220034,0.0002622453,0.000090232235],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014409326,0.0017759476,0.0012457306,0.0022593043,0.00080026087,0.0012634281,0.0021016463,0.0015380189,0.0018099707],"category_scores_gemma":[0.005149214,0.00067917036,0.0009916773,0.0013629925,0.0010646711,0.0028881442,0.002181481,0.0013838039,0.0005111797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030848978,0.00015239308,0.0055414587,0.00017101692,0.00020086212,0.00030893923,0.00040100943,0.70156175,0.005104461,0.01845613,0.00757528,0.26021823],"study_design_scores_gemma":[0.0000056721756,0.000016251295,0.00055394403,0.000015869566,0.000022017168,0.00004622046,0.000025255185,0.9871158,0.0010074971,0.010223678,0.00095840957,0.0000092543505],"about_ca_topic_score_codex":0.014251757,"about_ca_topic_score_gemma":0.012208288,"teacher_disagreement_score":0.014251757,"about_ca_system_score_codex":0.002134373,"about_ca_system_score_gemma":0.0008616889,"threshold_uncertainty_score":0.028337657},"labels":[],"label_agreement":null},{"id":"W2922491534","doi":"10.1088/1757-899x/491/1/012004","title":"A Real-time Moving Target Following Mobile Robot System with Depth Camera","year":2019,"lang":"en","type":"article","venue":"IOP Conference Series Materials Science and Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer vision; Artificial intelligence; Computer science; Mobile robot; Robustness (evolution); Robot; Omnidirectional camera; Tracking system; Omnidirectional antenna","score_opus":0.009103265847396073,"score_gpt":0.22003395574877058,"score_spread":0.2109306899013745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2922491534","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.114685565,0.0010312237,0.87528133,0.0001986141,0.00015975795,0.00019532091,0.00018533891,0.003685948,0.0045769983],"genre_scores_gemma":[0.5305928,0.0005230882,0.46053582,0.0002131275,0.000050313804,0.00012005412,0.00027456193,0.000057468547,0.0076328265],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99983513,0.000019441444,0.000006273378,0.000040532126,0.00007752689,0.000021104192],"domain_scores_gemma":[0.99987245,0.000014787747,0.000016231317,0.00001674189,0.000057592937,0.000022259817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020363792,0.00037399452,0.00043104822,0.0003115171,0.00023839415,0.0003272578,0.00072750007,0.00059125875,0.0014909032],"category_scores_gemma":[0.00025239936,0.00023892775,0.00021969348,0.00025723933,0.00013234437,0.0004808969,0.00035823902,0.000376488,0.00046226173],"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":[0.0005143253,0.00013505621,0.002287432,0.00031338533,0.000060996754,0.00032553126,0.00022858672,0.01179286,0.5688301,0.0018437352,0.0039732913,0.4096946],"study_design_scores_gemma":[0.0002846839,0.002156445,0.013185002,0.000060435897,0.00020560111,0.0031758407,0.0001499947,0.6600646,0.28300175,0.00097054837,0.036540456,0.00020469978],"about_ca_topic_score_codex":0.0026538847,"about_ca_topic_score_gemma":0.0026663307,"teacher_disagreement_score":0.0026538847,"about_ca_system_score_codex":0.00028108645,"about_ca_system_score_gemma":0.00053494243,"threshold_uncertainty_score":0.0052769184},"labels":[],"label_agreement":null},{"id":"W2923349707","doi":"","title":"Superpixel based road user tracker","year":2014,"lang":"en","type":"article","venue":"Lund University Publications (Lund University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Intersection (aeronautics); Computer vision; Tracking (education); Artificial intelligence; Sign (mathematics); Computer science; Geography; Cartography; Psychology; Mathematics","score_opus":0.0150068986697746,"score_gpt":0.20874398427330168,"score_spread":0.1937370856035271,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2923349707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21597669,0.0025081215,0.71419054,0.0002586408,0.0006511292,0.00041747623,0.005402673,0.042585034,0.018009704],"genre_scores_gemma":[0.7297476,0.00042715052,0.24775574,0.00013683195,0.00009625852,0.00010593175,0.008003228,0.00094713725,0.012780064],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986505,0.00016608313,0.00003715232,0.0005527247,0.00041224374,0.00018133923],"domain_scores_gemma":[0.99905413,0.00013886849,0.00006382814,0.00024730028,0.00040910882,0.000086748994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008848445,0.0009347662,0.0015706089,0.0019109998,0.00044350032,0.0014852717,0.0014168924,0.0013628678,0.008744367],"category_scores_gemma":[0.0013283696,0.0006046823,0.00071941025,0.0014312201,0.00024181863,0.0010217899,0.0009384079,0.00083250774,0.007110517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020145606,0.0005006655,0.012386534,0.00044457172,0.0003337538,0.00051671604,0.0001757555,0.06188902,0.09444272,0.0017699526,0.031546995,0.79397875],"study_design_scores_gemma":[0.000054449585,0.00030074862,0.013461053,0.000024088004,0.000072049894,0.0006974008,0.00004844066,0.9358043,0.039110716,0.000805416,0.0095695695,0.00005175133],"about_ca_topic_score_codex":0.0075655705,"about_ca_topic_score_gemma":0.0112585705,"teacher_disagreement_score":0.008744367,"about_ca_system_score_codex":0.00049534044,"about_ca_system_score_gemma":0.00065781595,"threshold_uncertainty_score":0.029252768},"labels":[],"label_agreement":null},{"id":"W2924232824","doi":"10.1016/j.knosys.2019.03.012","title":"Detection based visual tracking with convolutional neural network","year":2019,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Chinese Academy of Sciences","keywords":"Robustness (evolution); BitTorrent tracker; Artificial intelligence; Convolutional neural network; Computer science; Eye tracking; Benchmark (surveying); Video tracking; Pattern recognition (psychology); Computer vision; Object detection; Data association; Object (grammar)","score_opus":0.020381056009936414,"score_gpt":0.27236646041666035,"score_spread":0.25198540440672396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2924232824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019976163,0.0007155552,0.97541445,0.00013099583,0.000107062726,0.000037967657,0.0001543841,0.0015632451,0.0019001298],"genre_scores_gemma":[0.67429984,0.0012270926,0.31198734,0.0002679805,0.000100961275,0.00008562174,0.0007874725,0.00012121962,0.011122487],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995695,0.000031912128,0.0000199574,0.0001929574,0.00011241482,0.00007341893],"domain_scores_gemma":[0.99948066,0.00013949892,0.00007126879,0.00010284025,0.00017706779,0.00002875661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00067198003,0.00081653224,0.0008585721,0.0010261759,0.00040149156,0.0010443357,0.001434497,0.0011049318,0.0013454139],"category_scores_gemma":[0.0015183729,0.0005375919,0.0007256015,0.0012987945,0.00039992595,0.001034116,0.00097349554,0.0009859308,0.00084609195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025600847,0.00017539295,0.0020634863,0.00011107627,0.00015343765,0.000088012894,0.000053595588,0.15789069,0.03164031,0.005118443,0.0035253523,0.7989241],"study_design_scores_gemma":[0.000003773029,0.00002040363,0.00062643434,0.000008889,0.0000217273,0.00003236299,0.0000034383677,0.9909322,0.0062495274,0.0014694545,0.00062468013,0.0000070160563],"about_ca_topic_score_codex":0.021621056,"about_ca_topic_score_gemma":0.017158164,"teacher_disagreement_score":0.021621056,"about_ca_system_score_codex":0.0012098288,"about_ca_system_score_gemma":0.0009994055,"threshold_uncertainty_score":0.042990386},"labels":[],"label_agreement":null},{"id":"W2927045701","doi":"10.1007/s10044-019-00812-4","title":"Multi-task non-negative matrix factorization for visual object tracking","year":2019,"lang":"en","type":"article","venue":"Pattern Analysis and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Video tracking; Computer science; Artificial intelligence; Object (grammar); Tracking (education); Subspace topology; Pattern recognition (psychology); Task (project management); Non-negative matrix factorization; Computer vision; Particle filter; Matrix decomposition; Eye tracking; Matrix (chemical analysis); Algorithm; Kalman filter","score_opus":0.020608213181781136,"score_gpt":0.34333266829081394,"score_spread":0.3227244551090328,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2927045701","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":[0.0037572973,0.00059652905,0.9944722,0.00011129192,0.0000873397,0.000034585963,0.0001236127,0.00047036365,0.00034666318],"genre_scores_gemma":[0.24264933,0.0011236377,0.7445128,0.00041851052,0.0003387004,0.00034538363,0.0016659872,0.0002713804,0.0086742705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989052,0.00028022358,0.00005513711,0.0003743535,0.00023968199,0.00014535116],"domain_scores_gemma":[0.99766195,0.0011875246,0.00024717292,0.00030193475,0.00048815453,0.000113259346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001703929,0.0015166447,0.0016875371,0.00085558434,0.0007696508,0.00086134,0.0015077199,0.0016276039,0.0025034784],"category_scores_gemma":[0.005054075,0.00068010145,0.0012533051,0.0014418578,0.0006446827,0.0013387538,0.0012776386,0.0018576275,0.0016783583],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006656496,0.00038009367,0.0008999352,0.0005017756,0.00022586921,0.00016015428,0.00018833495,0.16329432,0.03681509,0.008491975,0.016326755,0.7720501],"study_design_scores_gemma":[0.00001709997,0.000061241844,0.0003813053,0.000011447949,0.000019371428,0.00005401941,0.000021948192,0.99037445,0.002529693,0.0047430797,0.0017718321,0.000014536671],"about_ca_topic_score_codex":0.009674701,"about_ca_topic_score_gemma":0.012411867,"teacher_disagreement_score":0.009674701,"about_ca_system_score_codex":0.00057661807,"about_ca_system_score_gemma":0.0014260026,"threshold_uncertainty_score":0.019236743},"labels":[],"label_agreement":null},{"id":"W2929588561","doi":"10.1155/2019/9060797","title":"Extracting Vehicle Trajectories Using Unmanned Aerial Vehicles in Congested Traffic Conditions","year":2019,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Computer science; Trajectory; Convolutional neural network; Feature (linguistics); Traffic congestion; Artificial intelligence; Feature extraction; Real-time computing; Tracking (education); Computer vision; Simulation; Engineering; Transport engineering","score_opus":0.021861466031907514,"score_gpt":0.30779691699887085,"score_spread":0.2859354509669633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2929588561","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":[0.88669544,0.00028711863,0.108706094,0.00008190313,0.00005190924,0.00004166345,0.00071254047,0.0012590636,0.0021642842],"genre_scores_gemma":[0.97886413,0.000089629815,0.019826477,0.000011214304,0.000006251238,0.000009941588,0.0006665866,0.000020168738,0.0005056334],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998952,0.000008368226,0.0000039758875,0.000034343346,0.000031888754,0.00002615269],"domain_scores_gemma":[0.99985635,0.000021699467,0.00003894883,0.000017391305,0.00004862655,0.000016943748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010038599,0.00051893725,0.00019496588,0.00090469973,0.00018116245,0.0002549899,0.00026216457,0.00023463462,0.00031322977],"category_scores_gemma":[0.0004823035,0.0001462068,0.00019485419,0.0005014887,0.00013963554,0.00047257214,0.00026228756,0.00023081154,0.00014709138],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032189433,0.00011805235,0.060298607,0.00013974559,0.000116359195,0.0011444017,0.0003267496,0.57306767,0.04013396,0.0018621795,0.0028537465,0.3196166],"study_design_scores_gemma":[0.0000050896574,0.00003871636,0.01757794,0.000008897402,0.000013669128,0.000086609,0.00013365969,0.97179157,0.008801658,0.0005704426,0.00096027914,0.000011527741],"about_ca_topic_score_codex":0.022136347,"about_ca_topic_score_gemma":0.022319479,"teacher_disagreement_score":0.022136347,"about_ca_system_score_codex":0.00036260666,"about_ca_system_score_gemma":0.00042143324,"threshold_uncertainty_score":0.04401505},"labels":[],"label_agreement":null},{"id":"W2938269868","doi":"10.1007/s00371-019-01646-1","title":"A robust visual tracking method via local feature extraction and saliency detection","year":2019,"lang":"en","type":"article","venue":"The Visual Computer","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Robustness (evolution); Computer science; Computer vision; Video tracking; Feature extraction; Tracking (education); Pattern recognition (psychology); Feature (linguistics); Object detection; Computer graphics; Visualization; Exploit; Kernel (algebra); Eye tracking; Object (grammar); Mathematics","score_opus":0.023256146841918908,"score_gpt":0.32498666556581435,"score_spread":0.30173051872389545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2938269868","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":[0.004980034,0.0002633692,0.99277747,0.00003987178,0.00007184535,0.000048609105,0.00004603293,0.0011367956,0.00063589844],"genre_scores_gemma":[0.1254053,0.00035634558,0.8683976,0.00013377496,0.00013610441,0.00011477526,0.00035134662,0.00027025808,0.0048344475],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994771,0.00004086687,0.000021795373,0.00018454192,0.00023284943,0.000042911845],"domain_scores_gemma":[0.99945694,0.000110306246,0.00006687599,0.0000904305,0.00022549261,0.00004989319],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005034174,0.0009238639,0.00132956,0.0018368617,0.0004526331,0.00074884854,0.0013254958,0.00086262426,0.00177829],"category_scores_gemma":[0.0011614879,0.0005593797,0.00090638007,0.0011391557,0.0003221827,0.0009432381,0.00082049525,0.000784989,0.0014416216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001926695,0.000117386604,0.0005357323,0.00011386879,0.000093007126,0.00008582366,0.000045541154,0.0079508275,0.19978084,0.0021809204,0.004055272,0.78484815],"study_design_scores_gemma":[0.00005290094,0.0002891205,0.004038442,0.000021326592,0.00013122734,0.0007058479,0.000023886474,0.8771307,0.105007775,0.0030489154,0.009475129,0.00007474243],"about_ca_topic_score_codex":0.0031579544,"about_ca_topic_score_gemma":0.004683269,"teacher_disagreement_score":0.0031579544,"about_ca_system_score_codex":0.00046783194,"about_ca_system_score_gemma":0.0007272429,"threshold_uncertainty_score":0.006279111},"labels":[],"label_agreement":null},{"id":"W2939569201","doi":"10.1109/icassp.2019.8683238","title":"Recurrent 3D Convolutional Network for Rodent Behavior Recognition","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Pattern recognition (psychology)","score_opus":0.051221282475197014,"score_gpt":0.3118755639035866,"score_spread":0.2606542814283896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2939569201","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":[0.14457142,0.002292088,0.8391406,0.00047277228,0.00014450538,0.00010347781,0.002623512,0.0064193504,0.0042323475],"genre_scores_gemma":[0.80949694,0.0010543198,0.17553997,0.00026955217,0.00005811506,0.00013254967,0.005416349,0.00011927508,0.00791291],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998275,0.00002273052,0.000008843052,0.000066510496,0.000043094242,0.000031247582],"domain_scores_gemma":[0.9998404,0.00003973359,0.00003251136,0.000029699488,0.000045266752,0.000012344158],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032828274,0.00072656025,0.000473355,0.00052372605,0.00014730052,0.00034172955,0.0009667391,0.0006130531,0.0014862304],"category_scores_gemma":[0.0007085817,0.00026371132,0.000551071,0.0005166661,0.0001765309,0.00042741807,0.0004044462,0.0006197049,0.0006281512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040384298,0.00032226616,0.0071715093,0.00015821452,0.0002016163,0.00023330671,0.000059147296,0.3261264,0.04103584,0.0033912193,0.011927812,0.60896885],"study_design_scores_gemma":[0.0000041883827,0.00004386548,0.0012225898,0.00000951812,0.000019632229,0.00004773847,0.000005075513,0.991413,0.0053432644,0.0010083,0.0008736497,0.000009106902],"about_ca_topic_score_codex":0.012124315,"about_ca_topic_score_gemma":0.017236121,"teacher_disagreement_score":0.012124315,"about_ca_system_score_codex":0.0007118671,"about_ca_system_score_gemma":0.000548911,"threshold_uncertainty_score":0.024107456},"labels":[],"label_agreement":null},{"id":"W2941413327","doi":"10.1016/j.patcog.2019.04.025","title":"Human trajectory prediction in crowded scene using social-affinity Long Short-Term Memory","year":2019,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":63,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; University of Alberta","keywords":"Computer science; Trajectory; Artificial intelligence; Object (grammar); Task (project management); Scale (ratio); Term (time); Machine learning; Pattern recognition (psychology); Geography","score_opus":0.0844853055151568,"score_gpt":0.32426558091891633,"score_spread":0.2397802754037595,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2941413327","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":[0.7448117,0.0014408693,0.24894418,0.00021958619,0.00019212744,0.00004561016,0.0007864528,0.0007622067,0.0027972555],"genre_scores_gemma":[0.98822534,0.00017942397,0.00966266,0.000034957444,0.000043125696,0.0000134564625,0.00047259542,0.000012519475,0.0013559398],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984133,0.000011188077,0.0000072500115,0.000061458835,0.000033271146,0.000045520082],"domain_scores_gemma":[0.999676,0.00007714391,0.000051085815,0.00003316415,0.00011154018,0.00005106336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00019004838,0.00049140444,0.0005572333,0.0012638958,0.00043579133,0.00046324407,0.0008191488,0.0006008032,0.0009305563],"category_scores_gemma":[0.0006779989,0.0001877652,0.00031211582,0.0010474614,0.00022025705,0.00071724737,0.0005994873,0.00040154552,0.0005260171],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001446449,0.00066686224,0.10513408,0.00018613493,0.00038198018,0.0009659183,0.00034055472,0.29757407,0.019016542,0.0019341179,0.008896373,0.5634569],"study_design_scores_gemma":[0.000004972033,0.000058231184,0.0073341196,0.000006092873,0.000025336662,0.000069294554,0.0000566952,0.9898151,0.0016048794,0.00073793967,0.00028007937,0.0000072170515],"about_ca_topic_score_codex":0.015847072,"about_ca_topic_score_gemma":0.025037233,"teacher_disagreement_score":0.015847072,"about_ca_system_score_codex":0.00038772996,"about_ca_system_score_gemma":0.00045869994,"threshold_uncertainty_score":0.031509697},"labels":[],"label_agreement":null},{"id":"W2944436573","doi":"10.5194/isprs-archives-xlii-2-w12-173-2019","title":"MOVING OBJECT DETECTION USING SPATIAL CORRELATION IN LAB COLOUR SPACE","year":2019,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"Atlantic Canada Opportunities Agency","keywords":"Thresholding; Background subtraction; Artificial intelligence; Computer vision; Pixel; Computer science; Subtraction; Image subtraction; Object (grammar); Pattern recognition (psychology); Object detection; Image (mathematics); Image processing; Mathematics; Binary image","score_opus":0.01736833897367269,"score_gpt":0.2663958313868532,"score_spread":0.2490274924131805,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944436573","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":[0.109299086,0.00042208613,0.8845923,0.000091716036,0.00005915344,0.000056277986,0.000113946255,0.0018315972,0.003533853],"genre_scores_gemma":[0.55902636,0.00031610116,0.43829244,0.000077365396,0.000026101723,0.00006473362,0.00019108535,0.0001353332,0.0018704805],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993389,0.00013978648,0.000021475791,0.00013808048,0.00029796074,0.00006378079],"domain_scores_gemma":[0.9992742,0.0001901149,0.000103188846,0.000082367376,0.00031719723,0.000032936598],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063687767,0.00045364926,0.00038580035,0.0017715963,0.00026797366,0.00084689044,0.00048763352,0.0004111136,0.0017879034],"category_scores_gemma":[0.001264864,0.00028715233,0.0004582633,0.0018399548,0.00042434444,0.000734282,0.00045156063,0.00046710492,0.00071822724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004521702,0.00017774773,0.007064254,0.00039849593,0.0001605327,0.00029857983,0.00026694857,0.025530716,0.49446264,0.0057408707,0.0024053855,0.46304175],"study_design_scores_gemma":[0.00003332848,0.00037564442,0.01713736,0.000039342744,0.00014059033,0.0012214378,0.000107147,0.5255753,0.44700912,0.0017038431,0.006561353,0.000095586685],"about_ca_topic_score_codex":0.0016996257,"about_ca_topic_score_gemma":0.0019991763,"teacher_disagreement_score":0.0017879034,"about_ca_system_score_codex":0.00047641303,"about_ca_system_score_gemma":0.00068014907,"threshold_uncertainty_score":0.0059811473},"labels":[],"label_agreement":null},{"id":"W2944659840","doi":"10.1109/ivs.2019.8813779","title":"FANTrack: 3D Multi-Object Tracking with Feature Association Network","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Inference; Artificial intelligence; Object (grammar); Association (psychology); Convolutional neural network; Task (project management); Similarity (geometry); Focus (optics); Exploit; Object detection; Video tracking; Code (set theory); Deep learning; Feature (linguistics); Data association; Machine learning; Data mining; Pattern recognition (psychology); Image (mathematics)","score_opus":0.02684065622084357,"score_gpt":0.28785313298961496,"score_spread":0.2610124767687714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2944659840","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":[0.008335849,0.000476058,0.96121037,0.00018212682,0.00017562837,0.00013487923,0.0014926817,0.025327802,0.0026645875],"genre_scores_gemma":[0.119797,0.00034020035,0.86226153,0.00041536527,0.00008894535,0.00033414442,0.0081984075,0.0012155887,0.0073488536],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990829,0.00008354785,0.000027397247,0.0003997232,0.00030897718,0.0000974018],"domain_scores_gemma":[0.9993243,0.0001246989,0.000075305,0.0003030838,0.000109580666,0.000063084255],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010334015,0.001979354,0.0014679037,0.0012384169,0.0006249475,0.0015096709,0.0043150852,0.0019339353,0.0045168647],"category_scores_gemma":[0.0023297144,0.0013564037,0.0015399898,0.0016843636,0.0005886677,0.0018456769,0.003182892,0.0022687237,0.0033767633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003710929,0.00022962844,0.0027121222,0.00021741008,0.00037838932,0.00023672609,0.00012334206,0.24255529,0.013498783,0.0084866965,0.047448967,0.68374157],"study_design_scores_gemma":[0.000026278822,0.000036186353,0.00048808244,0.000013724877,0.000015970973,0.00010714211,0.000007721555,0.98689646,0.0034182516,0.0030999952,0.0058708694,0.000019315443],"about_ca_topic_score_codex":0.018060295,"about_ca_topic_score_gemma":0.024928423,"teacher_disagreement_score":0.018060295,"about_ca_system_score_codex":0.0012896004,"about_ca_system_score_gemma":0.0016100453,"threshold_uncertainty_score":0.035910368},"labels":[],"label_agreement":null},{"id":"W2945171433","doi":"10.1109/crv.2019.00036","title":"aUToTrack: A Lightweight Object Detection and Tracking System for the SAE AutoDrive Challenge","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Benchmark (surveying); Inertial measurement unit; Global Positioning System; Computer science; Artificial intelligence; Computer vision; Position (finance); Tracking (education); Object detection; Ground truth; Lidar; Pedestrian; Object (grammar); Video tracking; Tracking system; Segmentation; Engineering; Geography; Kalman filter; Remote sensing; Transport engineering; Cartography; Telecommunications","score_opus":0.04084370018297758,"score_gpt":0.2931184954145639,"score_spread":0.25227479523158636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945171433","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17574872,0.004949595,0.25905997,0.0012270007,0.0020811155,0.0026458604,0.22237153,0.30835006,0.023566224],"genre_scores_gemma":[0.14646003,0.00055591384,0.2605548,0.00066994387,0.00016972727,0.0010510823,0.57643473,0.0027677556,0.011336062],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99782616,0.0001982182,0.0001310497,0.0009718975,0.0006587944,0.00021389413],"domain_scores_gemma":[0.99841106,0.00017159207,0.000101367135,0.0005982421,0.0005179453,0.0001997434],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016305239,0.0028437695,0.001959081,0.0026382103,0.0011403521,0.0016398989,0.0039777425,0.002112626,0.0068910276],"category_scores_gemma":[0.0034580461,0.0008099075,0.001136105,0.0018691887,0.00045776673,0.0021547445,0.0028566848,0.0018364422,0.011191252],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013419088,0.00092812465,0.0074621337,0.00072118605,0.0004416688,0.0003201326,0.00022711843,0.008612801,0.029396735,0.0014000591,0.56241184,0.38673624],"study_design_scores_gemma":[0.00082213833,0.0014469987,0.043573856,0.00030829437,0.00023631698,0.0013974379,0.0004986685,0.58455986,0.068145745,0.0071839434,0.29146692,0.00035986028],"about_ca_topic_score_codex":0.031683158,"about_ca_topic_score_gemma":0.059998486,"teacher_disagreement_score":0.031683158,"about_ca_system_score_codex":0.0012852125,"about_ca_system_score_gemma":0.0019073989,"threshold_uncertainty_score":0.06299746},"labels":[],"label_agreement":null},{"id":"W2945371405","doi":"10.1007/s11042-019-7685-2","title":"Robust coding in a global subspace model and its collaboration with a local model for visual tracking","year":2019,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Subspace topology; Residual; Artificial intelligence; BitTorrent tracker; Eye tracking; Coding (social sciences); Pattern recognition (psychology); Leverage (statistics); Computer vision; Bayesian inference; Bayesian probability; Machine learning; Algorithm; Mathematics","score_opus":0.05348745259543691,"score_gpt":0.32123895049734896,"score_spread":0.26775149790191205,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2945371405","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036022037,0.00011748669,0.9956145,0.000063766005,0.000017894474,0.0000055484634,0.00001510885,0.00008609629,0.00047745026],"genre_scores_gemma":[0.4569311,0.0011759631,0.53147,0.00023157155,0.00019469416,0.00012676071,0.00040907858,0.00033364724,0.009127169],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994573,0.00018927857,0.000021924192,0.00011990443,0.00016397606,0.0000476162],"domain_scores_gemma":[0.99883217,0.0005732903,0.00010864544,0.00024144654,0.00019986463,0.000044631397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001037345,0.00067611114,0.0007448501,0.0006458887,0.00030334335,0.0008439143,0.00092639273,0.0010323604,0.0011032011],"category_scores_gemma":[0.0033227468,0.00040776414,0.0008949489,0.00092205766,0.00084895565,0.0016559741,0.001600328,0.0014158654,0.00049600995],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001421246,0.0000696466,0.00048332475,0.00010967476,0.00009182618,0.00007398734,0.00017134525,0.6668767,0.028395357,0.09991464,0.0021612572,0.20151013],"study_design_scores_gemma":[0.0000025760878,0.00001871191,0.00006807005,0.0000039024508,0.000007789691,0.000016661426,0.0000055081687,0.989796,0.0018535325,0.0076726726,0.0005458116,0.000008862471],"about_ca_topic_score_codex":0.003985072,"about_ca_topic_score_gemma":0.0030238503,"teacher_disagreement_score":0.003985072,"about_ca_system_score_codex":0.00037977862,"about_ca_system_score_gemma":0.0006316283,"threshold_uncertainty_score":0.007923782},"labels":[],"label_agreement":null},{"id":"W2946562067","doi":"10.1049/iet-its.2018.5409","title":"Vision‐based traffic accident detection using sparse spatio‐temporal features and weighted extreme learning machine","year":2019,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Computer science; Artificial intelligence; Extreme learning machine; Traffic accident; Computer vision; Pattern recognition (psychology); Machine learning; Engineering; Artificial neural network; Transport engineering","score_opus":0.036171573712231835,"score_gpt":0.28057339098532597,"score_spread":0.24440181727309412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946562067","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1394733,0.00018011163,0.85825205,0.00014143247,0.00004067715,0.000053801123,0.00012467416,0.0007769896,0.0009568672],"genre_scores_gemma":[0.8681114,0.00015419332,0.12952662,0.00009897769,0.000068425215,0.00007586603,0.00062117487,0.000036970625,0.0013064415],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99945074,0.00009526007,0.000035041146,0.00015725753,0.00018542545,0.00007634576],"domain_scores_gemma":[0.9993742,0.00016154085,0.00012647461,0.00007600125,0.00021290516,0.000048846356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060182647,0.000496398,0.0008079715,0.0017575786,0.00024934375,0.0005633982,0.00094243727,0.0005939487,0.00045912008],"category_scores_gemma":[0.0020047515,0.0002601991,0.00082767726,0.0009207723,0.00032889203,0.000911197,0.00078600476,0.0007230341,0.00020192315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005011542,0.00044425658,0.01101646,0.00012541653,0.00018076901,0.00027650656,0.00015258393,0.28055614,0.029748436,0.003330265,0.004077072,0.66959095],"study_design_scores_gemma":[0.0000055875416,0.000041733274,0.0016597138,0.0000034290001,0.000011598022,0.00005859104,0.000011762815,0.9942162,0.0029505908,0.00079613813,0.00023674444,0.000007801454],"about_ca_topic_score_codex":0.0023844163,"about_ca_topic_score_gemma":0.0021006214,"teacher_disagreement_score":0.0023844163,"about_ca_system_score_codex":0.00037614023,"about_ca_system_score_gemma":0.00043879892,"threshold_uncertainty_score":0.0047410727},"labels":[],"label_agreement":null},{"id":"W2946675038","doi":"10.1007/978-3-030-27202-9_17","title":"Tracking in Urban Traffic Scenes from Background Subtraction and Object Detection","year":2019,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Background subtraction; Artificial intelligence; Computer science; Computer vision; Object detection; Trajectory; Tracking (education); Object (grammar); Video tracking; Task (project management); Subtraction; Detector; Class (philosophy); Pattern recognition (psychology); Pixel; Mathematics; Engineering; Psychology","score_opus":0.032952239799302396,"score_gpt":0.3023294807574394,"score_spread":0.269377240958137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2946675038","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":[0.34817505,0.0007445804,0.6436324,0.000098322445,0.00012573496,0.00008063386,0.0003210115,0.0028312546,0.0039909803],"genre_scores_gemma":[0.78699476,0.0005872012,0.20607397,0.000074806056,0.000074661024,0.00003656383,0.0011762256,0.00023138867,0.0047504166],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996307,0.00003671954,0.000009632196,0.00011615562,0.000106765074,0.00010003714],"domain_scores_gemma":[0.99975616,0.000063828775,0.000022949629,0.00003386609,0.0000932967,0.00002985077],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006163621,0.0007234294,0.0009754916,0.0017670197,0.0003968261,0.0010012339,0.0007403504,0.00071973976,0.0010451901],"category_scores_gemma":[0.00073555775,0.0005386817,0.00046959336,0.0017577702,0.0002863704,0.0005165061,0.0005568564,0.0004572414,0.0008278251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011420549,0.00035042528,0.011191296,0.00019954811,0.00021060265,0.0002812585,0.00017033373,0.076568685,0.17735706,0.0016067809,0.0024207379,0.7285012],"study_design_scores_gemma":[0.000031212327,0.00017261556,0.021143258,0.000013460021,0.00010567346,0.00034367715,0.00008590421,0.9294062,0.045132097,0.0013553848,0.0021882858,0.000022246557],"about_ca_topic_score_codex":0.0052057123,"about_ca_topic_score_gemma":0.008780205,"teacher_disagreement_score":0.0052057123,"about_ca_system_score_codex":0.00036533218,"about_ca_system_score_gemma":0.0005617464,"threshold_uncertainty_score":0.010350823},"labels":[],"label_agreement":null},{"id":"W2949163612","doi":"10.48550/arxiv.1903.02025","title":"Crowd Counting Using Scale-Aware Attention Networks","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","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":"Natural Sciences and Engineering Research Council of Canada; ShanghaiTech University; Nvidia","keywords":"Benchmark (surveying); Computer science; Scale (ratio); Artificial intelligence; Image (mathematics); Pixel; Focus (optics); Computer vision; Pattern recognition (psychology); Geography; Cartography","score_opus":0.09883004615290243,"score_gpt":0.22359779106026478,"score_spread":0.12476774490736235,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949163612","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06667732,0.0018939438,0.9198839,0.0012488532,0.00029613893,0.00014173512,0.00033878605,0.0017584121,0.007760872],"genre_scores_gemma":[0.88781154,0.0010633693,0.100281805,0.00062043953,0.000463052,0.000160385,0.0005679541,0.00021873142,0.008812726],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991353,0.00020729192,0.000031676696,0.0003293926,0.00017090979,0.00012543882],"domain_scores_gemma":[0.9986237,0.0006781741,0.00022849393,0.0001166156,0.00025909807,0.000094030904],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015111566,0.0018000647,0.0012817688,0.0023005514,0.0008442397,0.001290882,0.0021850315,0.0016253446,0.0019370717],"category_scores_gemma":[0.005382551,0.0006998986,0.0010243405,0.0014087745,0.0011217812,0.0029657646,0.0023338932,0.0014191834,0.0005385833],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031682133,0.00015929546,0.005875077,0.0001698255,0.0002000768,0.00030973993,0.00042828947,0.7084156,0.004658649,0.019777803,0.007974342,0.25171444],"study_design_scores_gemma":[0.0000060028933,0.000016484038,0.00054439023,0.0000161191,0.000021569165,0.000044364344,0.00002637666,0.9862714,0.0008911055,0.011195578,0.0009574163,0.000009295371],"about_ca_topic_score_codex":0.0140370075,"about_ca_topic_score_gemma":0.012270316,"teacher_disagreement_score":0.0140370075,"about_ca_system_score_codex":0.0021817756,"about_ca_system_score_gemma":0.00090449286,"threshold_uncertainty_score":0.02791059},"labels":[],"label_agreement":null},{"id":"W2949324449","doi":"10.1007/978-3-030-17798-0_26","title":"Adaptive Fusion of Sub-band Particle Filters for Robust Tracking of Multiple Objects in Video","year":2019,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Particle filter; Video tracking; Wavelet; Robustness (evolution); Frame (networking); Tracking (education); Filter (signal processing); Video processing","score_opus":0.04842609483695144,"score_gpt":0.2880669494898727,"score_spread":0.2396408546529213,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949324449","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.003590182,0.0005802582,0.9945305,0.000036478265,0.00010561308,0.000014136683,0.000024938203,0.0002398907,0.00087806967],"genre_scores_gemma":[0.15214449,0.0020029922,0.83742094,0.00012725887,0.00018862878,0.00009797708,0.00036550238,0.0001412868,0.007510913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999495,0.0000728965,0.000028837298,0.00012283366,0.00024091742,0.00003946161],"domain_scores_gemma":[0.99958795,0.00014108846,0.000037056736,0.00007169571,0.00014535789,0.000016802089],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007992515,0.00073861505,0.0012259113,0.00090322125,0.00031381234,0.00095884304,0.0008517177,0.0010978716,0.0016457073],"category_scores_gemma":[0.0015928298,0.00048224904,0.0008456353,0.0014723121,0.0003482042,0.0012308048,0.001177478,0.0010971046,0.0011650565],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030981973,0.00012431951,0.00042939247,0.00020702272,0.00014666327,0.000059077935,0.00011843662,0.13841526,0.05512605,0.01329284,0.004251845,0.7875193],"study_design_scores_gemma":[0.000009859374,0.00007143299,0.0005024024,0.000014443552,0.000042061944,0.00006492502,0.000012564115,0.9806979,0.009789181,0.0041167364,0.004663121,0.000015355607],"about_ca_topic_score_codex":0.002194456,"about_ca_topic_score_gemma":0.0023651032,"teacher_disagreement_score":0.002194456,"about_ca_system_score_codex":0.0004928373,"about_ca_system_score_gemma":0.0005053364,"threshold_uncertainty_score":0.0055054426},"labels":[],"label_agreement":null},{"id":"W2949877132","doi":"10.48550/arxiv.1809.02714","title":"DensSiam: End-to-End Densely-Siamese Network with Self-Attention Model for Object Tracking","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Memorial University of Newfoundland","funders":"","keywords":"Computer science; End-to-end principle; Convolutional neural network; Generalization; Artificial intelligence; Set (abstract data type); Architecture; Deep learning; Network architecture; Similarity (geometry); Function (biology); Object (grammar); Layer (electronics); Pattern recognition (psychology); Image (mathematics); Computer network; Mathematics","score_opus":0.08451138352809662,"score_gpt":0.23032950937504315,"score_spread":0.14581812584694653,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2949877132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032233834,0.0005016387,0.9536318,0.00022481488,0.00013176982,0.000114552844,0.00040443375,0.009822461,0.0029346764],"genre_scores_gemma":[0.5195475,0.00038551848,0.4596721,0.00060478883,0.00009304455,0.0002539175,0.0036320775,0.00044680302,0.015364174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997553,0.000029253008,0.000011656401,0.00009638218,0.000063736334,0.000043595373],"domain_scores_gemma":[0.9996463,0.0000856198,0.00002572146,0.00009730116,0.00010988528,0.00003516934],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000805487,0.0012727925,0.00084958074,0.0006779956,0.00044968628,0.00074435555,0.0023312867,0.0013382891,0.0030394373],"category_scores_gemma":[0.0015234612,0.000544861,0.0006247528,0.0007184935,0.00050411414,0.0015027347,0.0013441113,0.0014382743,0.0013454771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026213154,0.00027039004,0.0026705002,0.0000956516,0.00020468942,0.00016857617,0.00010030696,0.4594106,0.014204055,0.008567939,0.016094942,0.49795023],"study_design_scores_gemma":[0.0000072610383,0.000020906376,0.00013626665,0.0000021238288,0.000005845185,0.000017547442,0.0000031357815,0.99605596,0.0012764069,0.0018230712,0.0006473039,0.000004111103],"about_ca_topic_score_codex":0.01358222,"about_ca_topic_score_gemma":0.026734626,"teacher_disagreement_score":0.01358222,"about_ca_system_score_codex":0.0010028234,"about_ca_system_score_gemma":0.0014558558,"threshold_uncertainty_score":0.027006328},"labels":[],"label_agreement":null},{"id":"W2951133296","doi":"10.48550/arxiv.1505.03566","title":"COROLA: A Sequential Solution to Moving Object Detection Using Low-rank Approximation","year":2015,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates; Alberta Innovates - Technology Futures","keywords":"Outlier; Computer science; Rank (graph theory); Representation (politics); Artificial intelligence; Computer vision; Low-rank approximation; Object (grammar); Component (thermodynamics); Object detection; Image (mathematics); Sequence (biology); Sparse approximation; Pattern recognition (psychology); Mathematics","score_opus":0.1364176546118863,"score_gpt":0.245199052017038,"score_spread":0.10878139740515172,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2951133296","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":[0.0040830965,0.00021375906,0.9937546,0.00009502241,0.000031070525,0.00004174136,0.00007072371,0.001374734,0.00033520494],"genre_scores_gemma":[0.12589742,0.00034823923,0.8685002,0.00023432376,0.00016405429,0.00020909432,0.0011195625,0.00034271047,0.0031842557],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984825,0.00028688315,0.00007649674,0.00043721584,0.00053329056,0.0001835527],"domain_scores_gemma":[0.9981213,0.000720577,0.0002433076,0.0003335607,0.00044837836,0.00013289123],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015020723,0.0018110943,0.0023656006,0.0016527545,0.00055508426,0.001557287,0.003248998,0.001700839,0.0019248959],"category_scores_gemma":[0.0056304974,0.00077190425,0.001138283,0.0020768088,0.00078384386,0.001816089,0.0017662764,0.0024515921,0.0016930407],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000631951,0.00046943894,0.0019580002,0.00027519086,0.00021171845,0.0002840986,0.00017745448,0.33471468,0.019133369,0.009817864,0.0150763225,0.6172499],"study_design_scores_gemma":[0.000017461969,0.00003428585,0.000120644065,0.0000036535594,0.000006895375,0.00003226794,0.000010326337,0.9960664,0.0012694056,0.0017034917,0.00072773505,0.0000073402143],"about_ca_topic_score_codex":0.013426222,"about_ca_topic_score_gemma":0.014673448,"teacher_disagreement_score":0.013426222,"about_ca_system_score_codex":0.0008514496,"about_ca_system_score_gemma":0.0021939331,"threshold_uncertainty_score":0.026696146},"labels":[],"label_agreement":null},{"id":"W2952084543","doi":"10.48550/arxiv.1407.6251","title":"FollowMe: Efficient Online Min-Cost Flow Tracking with Bounded Memory and Computation","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computation; Computer science; Bounded function; Online algorithm; Tracking (education); Inference; Minimum-cost flow problem; Algorithm; Sequence (biology); Mathematical optimization; Flow network; Mathematics; Artificial intelligence","score_opus":0.07850547832486682,"score_gpt":0.22977701691183197,"score_spread":0.15127153858696515,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952084543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006916028,0.0003439792,0.98610944,0.0002779822,0.00009933658,0.00008241074,0.00034373996,0.003689922,0.0021372817],"genre_scores_gemma":[0.16506544,0.00032565327,0.82361615,0.00028406244,0.00019786406,0.00031658236,0.002306099,0.0005756155,0.0073125595],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991048,0.00015385218,0.000043655473,0.00029493382,0.0002772629,0.00012543763],"domain_scores_gemma":[0.9985297,0.00079788634,0.00011152635,0.00028758182,0.00020409805,0.00006920557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013931387,0.001802172,0.0016532573,0.0011322502,0.0007426221,0.0016217753,0.0033316524,0.0022148958,0.008057649],"category_scores_gemma":[0.006244022,0.0008353697,0.0007964528,0.001767061,0.0008335269,0.0033998275,0.0020548413,0.0021172664,0.002057018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005237436,0.00028464317,0.0010669213,0.00022045436,0.00007308896,0.000111284746,0.00010849782,0.5516708,0.0039130766,0.022665396,0.024468055,0.39489415],"study_design_scores_gemma":[0.000027899605,0.000022708884,0.000088456996,0.000006417099,0.0000044648436,0.000023034007,0.0000074206027,0.98972917,0.00072567735,0.008115955,0.0012430296,0.0000057236202],"about_ca_topic_score_codex":0.011577855,"about_ca_topic_score_gemma":0.011213499,"teacher_disagreement_score":0.011577855,"about_ca_system_score_codex":0.001265422,"about_ca_system_score_gemma":0.0028835272,"threshold_uncertainty_score":0.026955545},"labels":[],"label_agreement":null},{"id":"W2952342326","doi":"","title":"Winner-Take-All Autoencoders","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"MNIST database; Autoencoder; Computer science; Artificial intelligence; Pattern recognition (psychology); Winner-take-all; Deep learning; Convolutional neural network; Feature (linguistics); Layer (electronics); Face (sociological concept); Feature learning; Unsupervised learning; Invariant (physics); Machine learning; Artificial neural network; Mathematics","score_opus":0.11260375270162493,"score_gpt":0.21849721107727108,"score_spread":0.10589345837564615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952342326","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":[0.01604686,0.00042850943,0.9783237,0.00028782623,0.00010853516,0.00008918294,0.00013942932,0.0017775965,0.0027982593],"genre_scores_gemma":[0.63075876,0.000547026,0.34609118,0.0008979173,0.00022201338,0.00035991715,0.0010279289,0.0004365495,0.019658646],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998953,0.00025639686,0.00006613539,0.00026395818,0.00030845037,0.00015206724],"domain_scores_gemma":[0.99895906,0.00037960778,0.00008779453,0.00028307788,0.00022602495,0.000064478445],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017388553,0.0016123358,0.0019655349,0.00052375544,0.00057455886,0.0010592762,0.0030687312,0.0016390138,0.004050889],"category_scores_gemma":[0.0036893003,0.0008812727,0.0012152889,0.0006507666,0.0011030397,0.0020032772,0.001868568,0.0022631537,0.0017173977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033984595,0.00034636763,0.0014444652,0.00023771716,0.00040868882,0.00020367322,0.00015476873,0.48757732,0.009707664,0.031954348,0.012584398,0.45504078],"study_design_scores_gemma":[0.000017254642,0.00004694554,0.00015894094,0.000007936004,0.000020057796,0.000038585207,0.000012287562,0.9851783,0.0026040124,0.010871679,0.0010333312,0.000010610354],"about_ca_topic_score_codex":0.0030558826,"about_ca_topic_score_gemma":0.0057749604,"teacher_disagreement_score":0.004050889,"about_ca_system_score_codex":0.00071249827,"about_ca_system_score_gemma":0.001342443,"threshold_uncertainty_score":0.013551533},"labels":[],"label_agreement":null},{"id":"W2952371377","doi":"10.23919/springsim.2019.8732855","title":"Scalable Pattern Recognition and Real Time Tracking of Moving Objects","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Artificial intelligence; Dynamic time warping; Computer vision; Naive Bayes classifier; Pattern recognition (psychology); Cluster analysis; Tracking (education); Video tracking; Mean-shift; Scalability; Cognitive neuroscience of visual object recognition; Object (grammar); Support vector machine","score_opus":0.028430690492031178,"score_gpt":0.26066769563909653,"score_spread":0.23223700514706536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952371377","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010540248,0.00044595968,0.9869086,0.000065529006,0.000067571294,0.00004623024,0.000068296635,0.0011012112,0.0007562218],"genre_scores_gemma":[0.24515365,0.00078151695,0.7495402,0.000114377566,0.000097322365,0.00013037388,0.0006045292,0.00010630742,0.0034717612],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99901307,0.00008883506,0.00007400903,0.00038693863,0.00037653555,0.000060574646],"domain_scores_gemma":[0.99933136,0.00015150083,0.000083952604,0.00016529397,0.00023727064,0.000030596875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007997661,0.00053754455,0.00091644685,0.0013753813,0.00034866843,0.0008911751,0.0009946186,0.0006798328,0.0007905252],"category_scores_gemma":[0.0017995259,0.00034981823,0.0006797393,0.0013793436,0.00047640328,0.0013237888,0.0005283937,0.00060203124,0.0005078827],"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":[0.00012917862,0.00009149118,0.0023413948,0.00018055215,0.0001119277,0.000246004,0.00015446242,0.060704984,0.092615865,0.008936621,0.002973112,0.8315145],"study_design_scores_gemma":[0.000021470949,0.00013870363,0.0053102886,0.000027060383,0.000059280257,0.00045854758,0.00007567135,0.9376643,0.035733562,0.0104121035,0.01005903,0.000039980336],"about_ca_topic_score_codex":0.005210359,"about_ca_topic_score_gemma":0.0048064305,"teacher_disagreement_score":0.005210359,"about_ca_system_score_codex":0.0005716752,"about_ca_system_score_gemma":0.0005715649,"threshold_uncertainty_score":0.010360062},"labels":[],"label_agreement":null},{"id":"W2952447184","doi":"10.48550/arxiv.1808.07349","title":"Multi-Branch Siamese Networks with Online Selection for Object Tracking","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Polytechnique Montréal","funders":"","keywords":"BitTorrent tracker; Artificial intelligence; Computer science; Video tracking; Tracking (education); Object (grammar); Representation (politics); Selection (genetic algorithm); Computer vision; Pattern recognition (psychology); Eye tracking","score_opus":0.10890527370294992,"score_gpt":0.24493338349449784,"score_spread":0.13602810979154792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952447184","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":[0.022236457,0.0003069585,0.9744435,0.00013283035,0.000039621173,0.000048279355,0.00005479148,0.0011474453,0.0015900093],"genre_scores_gemma":[0.600469,0.0003783505,0.3877497,0.0004117107,0.0001352068,0.00026442562,0.0006520773,0.00035098352,0.009588577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994336,0.00011879809,0.000027659962,0.0001979818,0.00014459719,0.000077378776],"domain_scores_gemma":[0.99873954,0.0005367519,0.00013429039,0.00017414254,0.0003047013,0.00011061185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016299458,0.001294667,0.0011126212,0.00076879497,0.0006447748,0.0009592372,0.0017504768,0.001324319,0.003391385],"category_scores_gemma":[0.0037789643,0.00054483267,0.000615976,0.0008231496,0.00073952996,0.002124663,0.001476499,0.001729789,0.0012057843],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039015277,0.0001837127,0.0031007587,0.00006713119,0.00010122936,0.00016588943,0.0001356822,0.46389195,0.017898427,0.017011134,0.005814094,0.49123985],"study_design_scores_gemma":[0.0000069360617,0.000028606768,0.00010906438,0.0000024778599,0.000006117283,0.00001884342,0.0000035990952,0.99516535,0.0015269906,0.002749186,0.00037888993,0.000003906952],"about_ca_topic_score_codex":0.0035665163,"about_ca_topic_score_gemma":0.006451757,"teacher_disagreement_score":0.0035665163,"about_ca_system_score_codex":0.0008723372,"about_ca_system_score_gemma":0.0011755107,"threshold_uncertainty_score":0.011345327},"labels":[],"label_agreement":null},{"id":"W2952541350","doi":"10.1109/crv.2018.00049","title":"An Evaluation of Deep CNN Baselines for Scene-Independent Person Re-identification","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Senstar (Canada)","funders":"","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Baseline (sea); Software deployment; Identification (biology); Deep learning; Domain (mathematical analysis); Range (aeronautics); Deep neural networks; Machine learning; Pattern recognition (psychology)","score_opus":0.14423520140968737,"score_gpt":0.40771245562461067,"score_spread":0.2634772542149233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2952541350","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":[0.6714647,0.021416776,0.22399826,0.0015670813,0.0027188908,0.0013564817,0.014011399,0.024910668,0.03855578],"genre_scores_gemma":[0.74553305,0.0037735458,0.19135213,0.0006562695,0.00021896492,0.00039815297,0.04016521,0.00082500576,0.017077694],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968129,0.0007705898,0.00015954928,0.0011324587,0.0007489504,0.00037544948],"domain_scores_gemma":[0.9963707,0.0008150789,0.000256225,0.0012663521,0.0011184791,0.00017322823],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006624518,0.0029428434,0.0014983496,0.001984502,0.0012027393,0.0011418087,0.0034447745,0.001904837,0.0040950337],"category_scores_gemma":[0.009589079,0.0006864981,0.0010895063,0.0017392286,0.0007237407,0.0034786176,0.0023763506,0.0018179623,0.0029959865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0045975405,0.0014177414,0.01781322,0.0016118413,0.0013375115,0.0003687552,0.00018295106,0.17362854,0.015799336,0.0037056706,0.048018266,0.73151857],"study_design_scores_gemma":[0.00027781693,0.0028036057,0.02326691,0.000349455,0.0004705959,0.0011947987,0.00043093617,0.89157027,0.050376095,0.0036567675,0.025464566,0.00013814097],"about_ca_topic_score_codex":0.017147867,"about_ca_topic_score_gemma":0.026848879,"teacher_disagreement_score":0.017147867,"about_ca_system_score_codex":0.001821542,"about_ca_system_score_gemma":0.0011246846,"threshold_uncertainty_score":0.03503424},"labels":[],"label_agreement":null},{"id":"W2954369198","doi":"10.1109/access.2019.2924732","title":"Learning Robust Features for Planar Object Tracking","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities; University of Alberta; National Science Foundation","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Convolutional neural network; Computer vision; Deep learning; Motion blur; Feature extraction; Pattern recognition (psychology); Video tracking; Pixel; Eye tracking; Object (grammar); Image (mathematics)","score_opus":0.052938793798077574,"score_gpt":0.3374745261316309,"score_spread":0.28453573233355334,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954369198","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021996304,0.00044644816,0.9740466,0.00008003565,0.000037924106,0.00003644316,0.0003185325,0.0021886835,0.0008491118],"genre_scores_gemma":[0.59938365,0.0005807577,0.3909578,0.00022244964,0.00012620642,0.00014316685,0.0038362888,0.00034602117,0.0044036508],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937266,0.00005064752,0.000024862022,0.00028418962,0.00018404023,0.00008359846],"domain_scores_gemma":[0.9992951,0.00019488334,0.0001402425,0.0001687817,0.00016830639,0.000032709468],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072815624,0.0010563526,0.0011839985,0.0014428013,0.0003525265,0.00078264036,0.0014508222,0.0011046291,0.0012989875],"category_scores_gemma":[0.0031691212,0.00047357095,0.0007627801,0.0018938431,0.00050967775,0.0011905076,0.0013100323,0.00126467,0.00092022767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001920436,0.00010969,0.0019245053,0.00007050406,0.00009463474,0.00009428692,0.000039983952,0.34139723,0.01707128,0.00451304,0.0053668534,0.62912595],"study_design_scores_gemma":[0.000008027755,0.000027045999,0.0005571629,0.000005861185,0.000009771852,0.00004010201,0.000004871937,0.9920576,0.0038598403,0.002484412,0.00093773333,0.000007582601],"about_ca_topic_score_codex":0.0053514102,"about_ca_topic_score_gemma":0.0066393735,"teacher_disagreement_score":0.0053514102,"about_ca_system_score_codex":0.0007777324,"about_ca_system_score_gemma":0.00079519476,"threshold_uncertainty_score":0.010640562},"labels":[],"label_agreement":null},{"id":"W2954593156","doi":"10.1007/s11760-019-01528-y","title":"Local null space pursuit for real-time moving object detection in aerial surveillance","year":2019,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Subspace topology; Artificial intelligence; Computer science; Null (SQL); Object detection; Principal component analysis; Computer vision; Norm (philosophy); Aerial image; Linear subspace; Pattern recognition (psychology); Object (grammar); Mathematics; Data mining; Image (mathematics)","score_opus":0.01132179500352911,"score_gpt":0.2749157757072685,"score_spread":0.2635939807037394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2954593156","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043138415,0.0003079149,0.95564526,0.000083792875,0.000016801909,0.000012364542,0.000023085977,0.00016803344,0.00060433714],"genre_scores_gemma":[0.7435031,0.000494513,0.25182635,0.00006883167,0.000063271706,0.0000746077,0.00016407193,0.00007142524,0.0037337025],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967957,0.00010276404,0.000013516678,0.00006341756,0.00010947075,0.000031316104],"domain_scores_gemma":[0.99937636,0.0003483365,0.00006577073,0.000052715277,0.000117922056,0.000038942977],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070826407,0.00037917573,0.0005776178,0.000682272,0.00023641178,0.00049618416,0.00060787104,0.00049536856,0.00096353586],"category_scores_gemma":[0.002264652,0.00020113435,0.0003288848,0.000657617,0.0005791898,0.000692313,0.00082838617,0.0005544292,0.00032950027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009265545,0.00019383965,0.0019567518,0.00031058697,0.00011067404,0.00014039682,0.00027156252,0.23268199,0.10010615,0.0187223,0.0022906915,0.6422885],"study_design_scores_gemma":[0.000009800144,0.00007588794,0.00046300553,0.000004408181,0.0000096556905,0.000047604994,0.00002786771,0.992148,0.004717205,0.0021421597,0.00034801153,0.000006518088],"about_ca_topic_score_codex":0.0011321941,"about_ca_topic_score_gemma":0.0009357116,"teacher_disagreement_score":0.0011321941,"about_ca_system_score_codex":0.00027809406,"about_ca_system_score_gemma":0.00044313312,"threshold_uncertainty_score":0.0037457347},"labels":[],"label_agreement":null},{"id":"W2958371845","doi":"10.11575/prism/35761","title":"Real-time Pedestrian Classification System Using Deep Learning on a Raspberry Pi Cluster","year":2019,"lang":"en","type":"dissertation","venue":"PRISM (University of Calgary)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs; University of Calgary","keywords":"Raspberry pi; Cluster (spacecraft); Artificial intelligence; Pedestrian; Deep learning; Computer science; Pedestrian detection; Cartography; Geography; Operating system; Embedded system; Internet of Things; Archaeology","score_opus":0.022189325656967927,"score_gpt":0.2509331122554019,"score_spread":0.22874378659843395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2958371845","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":[0.47588253,0.0003026404,0.48295647,0.0003493137,0.00028326322,0.00035094016,0.0006825661,0.027913468,0.011278839],"genre_scores_gemma":[0.85015565,0.00014532196,0.13914093,0.00016777162,0.000030347677,0.0001488979,0.0008648658,0.00014468169,0.009201526],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978954,0.000016560309,0.000007997861,0.00007393212,0.00006872611,0.0000432663],"domain_scores_gemma":[0.9997813,0.00002084314,0.000019232608,0.00003146293,0.000116363284,0.000030895128],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030042639,0.0005132151,0.0004737658,0.00054782664,0.0002960601,0.00039177595,0.00091182534,0.0003374416,0.0032462135],"category_scores_gemma":[0.00035621307,0.00024061142,0.00024435975,0.00038669363,0.00012444315,0.0005658045,0.00043686127,0.0003825099,0.0013038018],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017606622,0.0006609303,0.010344572,0.00019701498,0.00012526127,0.0006130926,0.0001729905,0.059744433,0.2276182,0.002207761,0.029171126,0.66738397],"study_design_scores_gemma":[0.000048144775,0.00031122734,0.007254211,0.000017388458,0.000046326746,0.0001372052,0.000056943325,0.91088265,0.0755653,0.0005529315,0.0050911163,0.00003653197],"about_ca_topic_score_codex":0.0093273,"about_ca_topic_score_gemma":0.009907156,"teacher_disagreement_score":0.0093273,"about_ca_system_score_codex":0.00094133837,"about_ca_system_score_gemma":0.0007233812,"threshold_uncertainty_score":0.018545985},"labels":[],"label_agreement":null},{"id":"W2959663291","doi":"10.18280/ts.360110","title":"A Robust mRMR Based Pedestrian Detection Approach Using Shape Descriptor","year":2019,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian detection; Pedestrian; Artificial intelligence; Pattern recognition (psychology); Computer science; Computer vision; Geography","score_opus":0.07655309384377086,"score_gpt":0.2562309002244278,"score_spread":0.17967780638065695,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2959663291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03933637,0.00083309767,0.9551682,0.00013552533,0.0001529201,0.000126932,0.00019360358,0.00229246,0.001760872],"genre_scores_gemma":[0.29147756,0.0010700715,0.697639,0.00023482993,0.00015428601,0.00010978855,0.00084261445,0.00015500617,0.00831683],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996196,0.00004819519,0.00001574467,0.000121395395,0.00015112138,0.000043982393],"domain_scores_gemma":[0.99972135,0.00002605604,0.000043696993,0.000058573165,0.0001268458,0.000023483368],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047530123,0.000588723,0.0011364466,0.0017758624,0.00027059318,0.0005590988,0.0007689059,0.0006354643,0.0015677917],"category_scores_gemma":[0.0006106095,0.0002541661,0.0007483442,0.0010876645,0.00027682693,0.00062979787,0.000585689,0.0004726548,0.0020619035],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00042309333,0.00014859425,0.0019549555,0.00021025032,0.00009205,0.0002878061,0.000058158163,0.0062057185,0.21963544,0.0018520802,0.0039742226,0.7651576],"study_design_scores_gemma":[0.000072974966,0.0016494443,0.017136559,0.00006559462,0.0002574218,0.004837941,0.00019040043,0.6721642,0.27774462,0.0028203547,0.022845045,0.00021534147],"about_ca_topic_score_codex":0.00104371,"about_ca_topic_score_gemma":0.0015426478,"teacher_disagreement_score":0.0017758624,"about_ca_system_score_codex":0.00024046695,"about_ca_system_score_gemma":0.00041213658,"threshold_uncertainty_score":0.005244732},"labels":[],"label_agreement":null},{"id":"W2961669282","doi":"10.1007/s11042-019-07895-5","title":"Attention-based multi-modal fusion for improved real estate appraisal: a case study in Los Angeles","year":2019,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":26,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Mitacs","keywords":"Real estate; Computer science; Presentation (obstetrics); Modal; Tourism; Artificial neural network; Artificial intelligence; Data mining; Operations research; Data science; Geography; Finance; Business","score_opus":0.044902434224743985,"score_gpt":0.3584265051045842,"score_spread":0.3135240708798402,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2961669282","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":[0.97429514,0.00032166287,0.014565954,0.000753773,0.00003475639,0.00013288438,0.00016910174,0.0002253818,0.009501367],"genre_scores_gemma":[0.9874215,0.000108490254,0.00831868,0.00004088236,0.000011998307,0.000029644061,0.000082339975,0.000017830023,0.003968619],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949694,0.00020777335,0.000020902771,0.000075450866,0.00011304804,0.00008588979],"domain_scores_gemma":[0.9992866,0.0002642136,0.0000658272,0.000045838446,0.00028171774,0.000055742654],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009968978,0.0004239941,0.0004022783,0.0007647776,0.0011246423,0.0013813318,0.00065404555,0.00076475315,0.0028000495],"category_scores_gemma":[0.002363071,0.00015059755,0.00029691294,0.0009676988,0.0003980497,0.0008388762,0.0006997135,0.0005006748,0.0004291702],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022620617,0.003138394,0.14194381,0.00072787155,0.00035431294,0.023682324,0.01974514,0.09060527,0.03323042,0.007131567,0.032061603,0.6451172],"study_design_scores_gemma":[0.00017883511,0.0014576699,0.20976363,0.00017798423,0.00032475143,0.0029011657,0.048242044,0.6791417,0.019401737,0.005358649,0.032817747,0.0002341105],"about_ca_topic_score_codex":0.057606958,"about_ca_topic_score_gemma":0.09729949,"teacher_disagreement_score":0.057606958,"about_ca_system_score_codex":0.0016173474,"about_ca_system_score_gemma":0.0010580381,"threshold_uncertainty_score":0.11454332},"labels":[],"label_agreement":null},{"id":"W2962844880","doi":"10.1109/cvpr.2019.00739","title":"Moving Object Detection Under Discontinuous Change in Illumination Using Tensor Low-Rank and Invariant Sparse Decomposition","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Multilinear map; Artificial intelligence; Computer science; Regularization (linguistics); Rank (graph theory); Pattern recognition (psychology); Tensor (intrinsic definition); Computer vision; Matrix decomposition; Invariant (physics); Decomposition; Sparse approximation; Mathematics","score_opus":0.03366286165331285,"score_gpt":0.3008423779083436,"score_spread":0.2671795162550308,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2962844880","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.035677627,0.00023511426,0.96225524,0.00017096792,0.00003994776,0.00003285202,0.000115575494,0.0006476804,0.0008249314],"genre_scores_gemma":[0.40130603,0.00059811206,0.59391826,0.00019461274,0.00014362906,0.00006610822,0.0010673234,0.00015275914,0.0025531193],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992931,0.0001325814,0.000037570386,0.00018641353,0.0002584058,0.000091972994],"domain_scores_gemma":[0.99879897,0.0003293345,0.00023490393,0.00021885474,0.00033170008,0.00008624903],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009165158,0.00073593116,0.00088646566,0.0015651113,0.00032852413,0.0010402917,0.00080821145,0.0007964141,0.0007214985],"category_scores_gemma":[0.0031558583,0.00030308377,0.00083817984,0.0012158296,0.00065584603,0.0012309701,0.0007803192,0.0013169841,0.00050597714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041242008,0.00020764678,0.005356916,0.00026493592,0.00018746001,0.00041516233,0.00032059973,0.18657789,0.13451698,0.0130529525,0.006958866,0.65172815],"study_design_scores_gemma":[0.0000081432045,0.000047921156,0.0016093054,0.000010262638,0.000019702169,0.00014846944,0.000035632394,0.9820826,0.011696105,0.003116988,0.0012065113,0.00001832036],"about_ca_topic_score_codex":0.004109986,"about_ca_topic_score_gemma":0.0047784615,"teacher_disagreement_score":0.004109986,"about_ca_system_score_codex":0.00041926844,"about_ca_system_score_gemma":0.00070178445,"threshold_uncertainty_score":0.008172095},"labels":[],"label_agreement":null},{"id":"W2963276905","doi":"10.1109/itsc.2018.8569479","title":"Practical Issues of Action-Conditioned Next Image Prediction","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; Convolutional neural network; Encoding (memory); Feed forward; Frame (networking); Artificial neural network; Action (physics); Pattern recognition (psychology); Computer vision; Machine learning; Algorithm; Engineering; Physics","score_opus":0.09271974874719818,"score_gpt":0.40937042371048776,"score_spread":0.3166506749632896,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963276905","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07376325,0.0017515266,0.90612304,0.0054137236,0.00025555116,0.00009654628,0.00040124476,0.003184401,0.009010594],"genre_scores_gemma":[0.8323,0.00045946456,0.16043957,0.0006619609,0.00010914134,0.00012483975,0.00043681904,0.00023790922,0.005230293],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99866116,0.0004465579,0.00006808899,0.0003839176,0.00031129402,0.00012899954],"domain_scores_gemma":[0.9936052,0.004173238,0.00023039433,0.0010517931,0.00078309234,0.00015634195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030910259,0.0009728762,0.001100462,0.0003902365,0.0007841958,0.0012584662,0.0032550322,0.002270809,0.005089853],"category_scores_gemma":[0.01757657,0.00069840706,0.000468972,0.0005988577,0.0015041707,0.0041124793,0.002096657,0.0029920393,0.0012989771],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037822282,0.00011027294,0.0025382787,0.00016243472,0.00005751212,0.00013797308,0.00014820446,0.7539121,0.005114905,0.02615306,0.0076566422,0.20363042],"study_design_scores_gemma":[0.00001323576,0.00002274727,0.00020121518,0.000009269944,0.000005245576,0.00002937969,0.000019763378,0.9825319,0.0019415836,0.014521349,0.0006966834,0.000007620646],"about_ca_topic_score_codex":0.017094426,"about_ca_topic_score_gemma":0.01478158,"teacher_disagreement_score":0.017094426,"about_ca_system_score_codex":0.0017145282,"about_ca_system_score_gemma":0.0025628803,"threshold_uncertainty_score":0.033989847},"labels":[],"label_agreement":null},{"id":"W2963328866","doi":"10.1007/s11042-019-07851-3","title":"Robust visual tracking via a hybrid correlation filter","year":2019,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Computer science; Correlation; Eye tracking; Computer vision; Artificial intelligence; Tracking (education); Filter (signal processing)","score_opus":0.03867292149606424,"score_gpt":0.28383281087225043,"score_spread":0.24515988937618619,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963328866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004135771,0.00014169443,0.9948716,0.0000421047,0.00004185145,0.000014312171,0.000019345685,0.00025360778,0.00047976978],"genre_scores_gemma":[0.17252353,0.00059093896,0.82131326,0.00021808805,0.00014670155,0.00014284898,0.00022240388,0.00016101738,0.004681134],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857664,0.0002353101,0.00005849561,0.0004103562,0.0006072574,0.00011202569],"domain_scores_gemma":[0.99852365,0.00050983735,0.00016960107,0.00026446872,0.0004571824,0.00007525327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013546726,0.00096944015,0.001251337,0.0012526123,0.0005539989,0.0013871876,0.0012370375,0.0017068124,0.0018695784],"category_scores_gemma":[0.0033302847,0.0007885612,0.0010854158,0.0020447362,0.00055014476,0.0015513323,0.001844933,0.0010736536,0.0014246099],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007164912,0.00024743285,0.002147401,0.00023112974,0.00033739622,0.00021414222,0.00013374517,0.13221563,0.15838297,0.019342586,0.0041264086,0.68190473],"study_design_scores_gemma":[0.000025917738,0.000096271564,0.0010011623,0.000013245074,0.0000604411,0.00018220414,0.0000075833805,0.97845477,0.016224902,0.0017234085,0.0021771155,0.000032907636],"about_ca_topic_score_codex":0.0040320163,"about_ca_topic_score_gemma":0.004665745,"teacher_disagreement_score":0.0040320163,"about_ca_system_score_codex":0.0006721456,"about_ca_system_score_gemma":0.0016468712,"threshold_uncertainty_score":0.008017123},"labels":[],"label_agreement":null},{"id":"W2963895973","doi":"10.1109/icip.2015.7351162","title":"Reproducible evaluation of Pan-Tilt-Zoom tracking","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université Laval; Polytechnique Montréal","funders":"","keywords":"Zoom; Computer vision; Computer science; Artificial intelligence; Tracking (education); Tilt (camera); Tracking system; Field (mathematics); Kalman filter; Engineering; Mathematics","score_opus":0.2690667962726861,"score_gpt":0.4005891811658235,"score_spread":0.1315223848931374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963895973","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5258492,0.002732062,0.44389543,0.00031085222,0.00079552707,0.0016759468,0.00298756,0.01299987,0.00875351],"genre_scores_gemma":[0.86173433,0.0003559642,0.12983494,0.00015164388,0.000060605533,0.00052884594,0.004425792,0.0007268944,0.002181036],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99004924,0.0026726904,0.0008214761,0.0020802598,0.003864946,0.00051145116],"domain_scores_gemma":[0.97997916,0.0072777322,0.0018127891,0.0042738765,0.0058792178,0.00077732303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009179245,0.001479194,0.0010627584,0.0016052152,0.000734799,0.0016354627,0.0017185826,0.0015413815,0.0018761756],"category_scores_gemma":[0.038033407,0.00035311072,0.00061239727,0.0011740347,0.0008958409,0.0013946659,0.0020651822,0.0008171717,0.00072725676],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004185708,0.0016827657,0.03170209,0.001973836,0.0008867801,0.0005825793,0.0007335145,0.21808617,0.20293145,0.003670228,0.013817665,0.51974726],"study_design_scores_gemma":[0.00036567182,0.0044867108,0.059122197,0.0002013142,0.000266221,0.0010296676,0.00035727842,0.7442982,0.17474577,0.0026226004,0.012289114,0.00021525685],"about_ca_topic_score_codex":0.0041295397,"about_ca_topic_score_gemma":0.0034774046,"teacher_disagreement_score":0.009179245,"about_ca_system_score_codex":0.0012704064,"about_ca_system_score_gemma":0.001118126,"threshold_uncertainty_score":0.048545063},"labels":[],"label_agreement":null},{"id":"W2963968420","doi":"10.1109/iros.2018.8594050","title":"Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":53,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Shadow (psychology); Computer science; Artificial intelligence; Benchmark (surveying); Convolutional neural network; Computer vision; Image (mathematics); Deep learning; Object detection; Class (philosophy); Pattern recognition (psychology)","score_opus":0.041247683245049356,"score_gpt":0.28506854672533727,"score_spread":0.24382086348028792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2963968420","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051741723,0.0005488389,0.94220537,0.00012854673,0.00006699598,0.000058221496,0.0001788595,0.003111431,0.0019600769],"genre_scores_gemma":[0.5959117,0.0008277055,0.39638025,0.00019033812,0.00008923657,0.00007127599,0.0008472128,0.00019467948,0.0054875887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99977285,0.000017331344,0.000008006534,0.00007643418,0.00008674478,0.00003862695],"domain_scores_gemma":[0.99975044,0.000058674388,0.000029553574,0.000058832957,0.00008198725,0.000020457268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027877922,0.0008288046,0.0006917333,0.00082457135,0.00021356373,0.0004389493,0.0009771301,0.0004789259,0.0018862329],"category_scores_gemma":[0.00076764455,0.00041123078,0.00048744408,0.0005458858,0.000294092,0.0009597807,0.0008438801,0.0007484133,0.000698085],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002960792,0.000118482894,0.0020263628,0.00012907546,0.00010217985,0.00015157057,0.00007748203,0.07742386,0.07755609,0.0015341421,0.0036687811,0.8369158],"study_design_scores_gemma":[0.000005983247,0.000034543096,0.0012550798,0.000007610832,0.000016649652,0.00009882281,0.000011322697,0.98032284,0.016485501,0.0008305545,0.00092341105,0.000007650478],"about_ca_topic_score_codex":0.006739133,"about_ca_topic_score_gemma":0.011336931,"teacher_disagreement_score":0.006739133,"about_ca_system_score_codex":0.0006065747,"about_ca_system_score_gemma":0.0005913639,"threshold_uncertainty_score":0.013399839},"labels":[],"label_agreement":null},{"id":"W2964035707","doi":"10.1609/aaai.v32i1.12325","title":"MixedPeds: Pedestrian Detection in Unannotated Videos Using Synthetically Generated Human-Agents for Training","year":2018,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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":"Army Research Office; Pan African Materials Institute; Canadian Institute for Advanced Research","keywords":"Computer science; Detector; Pedestrian detection; Artificial intelligence; Training set; Set (abstract data type); Pattern recognition (psychology); Pedestrian; Computer vision","score_opus":0.15091415266110905,"score_gpt":0.3847674818189787,"score_spread":0.23385332915786963,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964035707","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28462687,0.0008333274,0.6742883,0.00026884468,0.0005817541,0.00058269966,0.004837052,0.026321976,0.0076592457],"genre_scores_gemma":[0.5574379,0.00026173567,0.4256907,0.00029194562,0.000062621715,0.00031629135,0.012143162,0.000589777,0.003205905],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990293,0.00017445773,0.000034926303,0.00044832425,0.00017015722,0.00014287562],"domain_scores_gemma":[0.9989361,0.00031892196,0.00009944571,0.00032247952,0.00023094626,0.00009213966],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001246727,0.0019304649,0.0009277336,0.0012410844,0.00038734946,0.0009655733,0.0023859923,0.0013902156,0.002285907],"category_scores_gemma":[0.0037090231,0.0008439075,0.00087110425,0.00057371444,0.00060218683,0.0010593932,0.0010411142,0.0011106213,0.0015037068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010941477,0.0010454099,0.01593149,0.0007207979,0.00051132916,0.0009074601,0.0002836984,0.26997167,0.07014591,0.0030925681,0.029260186,0.6070353],"study_design_scores_gemma":[0.00003385524,0.0002099124,0.002915037,0.00003665498,0.000028089207,0.0003098744,0.000053397667,0.95751786,0.03331865,0.0008914247,0.004658447,0.000026827483],"about_ca_topic_score_codex":0.008297972,"about_ca_topic_score_gemma":0.015422314,"teacher_disagreement_score":0.008297972,"about_ca_system_score_codex":0.0012048808,"about_ca_system_score_gemma":0.00079736335,"threshold_uncertainty_score":0.01649934},"labels":[],"label_agreement":null},{"id":"W2964310808","doi":"10.1139/juvs-2018-0033","title":"Implementation and optimization of the cascade classifier algorithm for UAV detection and tracking","year":2019,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Cascading classifiers; Computer vision; Drone; Computer science; Cascade; Classifier (UML); Robotics; Robot; Engineering","score_opus":0.021030927266434952,"score_gpt":0.2935798968032977,"score_spread":0.27254896953686275,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2964310808","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":[0.008914559,0.000082375336,0.9879887,0.000049160844,0.00005322489,0.00010143845,0.000033349526,0.0010066801,0.0017705299],"genre_scores_gemma":[0.2315675,0.00015104383,0.7630522,0.00007227864,0.00004774894,0.00027812747,0.0002500675,0.000131293,0.0044496893],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993606,0.000045496203,0.000031610984,0.00015781753,0.00031881084,0.000085675536],"domain_scores_gemma":[0.99951744,0.000086894324,0.000031122847,0.00004606862,0.00029070984,0.000027755379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070258207,0.0008142153,0.0007361475,0.0008238496,0.0006799965,0.0007277736,0.00127147,0.0010703758,0.0038400695],"category_scores_gemma":[0.0015965482,0.00045852724,0.00049258437,0.0005758519,0.00025067432,0.00070240465,0.0005002937,0.0010997977,0.0017046316],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020602123,0.00019323842,0.0015866543,0.000080635604,0.000058607508,0.00012990224,0.0000714368,0.21557072,0.054241836,0.0043463716,0.0061245873,0.7173899],"study_design_scores_gemma":[0.000007806951,0.000048112714,0.000336951,0.0000028753575,0.0000052731352,0.000041301722,0.0000071530353,0.989893,0.008158439,0.0003321296,0.0011596304,0.0000072369944],"about_ca_topic_score_codex":0.01442448,"about_ca_topic_score_gemma":0.016427515,"teacher_disagreement_score":0.01442448,"about_ca_system_score_codex":0.00086965493,"about_ca_system_score_gemma":0.0020499034,"threshold_uncertainty_score":0.02868104},"labels":[],"label_agreement":null},{"id":"W2965242851","doi":"10.48550/arxiv.1805.11123","title":"Global Sum Pooling: A Generalization Trick for Object Counting with Small Datasets of Large Images","year":2018,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Overfitting; Pooling; Computer science; Artificial intelligence; Convolutional neural network; Generalization; Inference; Pattern recognition (psychology); Focus (optics); Object (grammar); Deep learning; Machine learning; Artificial neural network; Mathematics","score_opus":0.06881288877122635,"score_gpt":0.2382420283306845,"score_spread":0.16942913955945815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2965242851","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.013624474,0.00044902315,0.9800244,0.0003189421,0.00008155552,0.00010486086,0.00034816953,0.004069868,0.0009787651],"genre_scores_gemma":[0.3716284,0.0009022562,0.6141091,0.001163813,0.00035832296,0.0006545921,0.0030637311,0.0011721929,0.006947603],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981217,0.00036783327,0.000122499,0.0008880055,0.00032409962,0.00017573396],"domain_scores_gemma":[0.99688065,0.0010706269,0.0002739217,0.0013839978,0.0002924353,0.00009832971],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005828431,0.0036407716,0.0022679802,0.0014083514,0.0007622082,0.0017015834,0.0051298314,0.0020512189,0.0036083711],"category_scores_gemma":[0.0112326015,0.0012903572,0.0022885916,0.0022999647,0.0016208861,0.005297536,0.003616938,0.00398416,0.001969967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004312187,0.00023712225,0.004380765,0.000463738,0.00072036707,0.0004269011,0.00027005805,0.22480938,0.025507875,0.014374918,0.014480565,0.7138971],"study_design_scores_gemma":[0.000026742204,0.00012638155,0.0014767159,0.000039082122,0.00009336999,0.00018460024,0.000045394765,0.96125853,0.01133382,0.02169023,0.0036974608,0.000027603883],"about_ca_topic_score_codex":0.0066404617,"about_ca_topic_score_gemma":0.0074394275,"teacher_disagreement_score":0.0066404617,"about_ca_system_score_codex":0.001325521,"about_ca_system_score_gemma":0.0014229924,"threshold_uncertainty_score":0.030824006},"labels":[],"label_agreement":null},{"id":"W2966683173","doi":"10.1109/access.2019.2953276","title":"Online Multi-Object Tracking With GMPHD Filter and Occlusion Group Management","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea; Iran Telecommunication Research Center; National Research Foundation of Korea; Ministry of Science, ICT and Future Planning; National Research Foundation","keywords":"Computer science; Tracking (education); Video tracking; Computer vision; Object (grammar); Group (periodic table); Artificial intelligence","score_opus":0.0457259568362274,"score_gpt":0.32506655300314474,"score_spread":0.27934059616691737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2966683173","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0056023947,0.00028170622,0.991581,0.00006183763,0.000055745368,0.00004253882,0.00006997692,0.0018606358,0.00044405402],"genre_scores_gemma":[0.2052414,0.0003853876,0.7883422,0.0002393396,0.000117228614,0.00019462702,0.0010530059,0.00027937294,0.0041474523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99771094,0.00017962955,0.00010809905,0.0007939896,0.0009818787,0.00022547855],"domain_scores_gemma":[0.99882466,0.00027366524,0.00014565804,0.00036551442,0.00029240092,0.00009804213],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001689627,0.001301943,0.0023755198,0.0017583233,0.0010125277,0.0015896593,0.003415474,0.0015150353,0.002307944],"category_scores_gemma":[0.0030798786,0.0008197208,0.0014197234,0.0021933895,0.0006757807,0.0023166002,0.0026702578,0.0016052825,0.0016354962],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043479376,0.00019698923,0.00494866,0.00017435361,0.00018056866,0.00018177518,0.0002618581,0.07029655,0.018567175,0.004578567,0.005204483,0.89497423],"study_design_scores_gemma":[0.00004002411,0.000078175006,0.0014737466,0.000011551925,0.0000484783,0.00022691386,0.00003495409,0.98049337,0.010133719,0.0028048207,0.00462435,0.000029916215],"about_ca_topic_score_codex":0.009314936,"about_ca_topic_score_gemma":0.00846073,"teacher_disagreement_score":0.009314936,"about_ca_system_score_codex":0.0010845239,"about_ca_system_score_gemma":0.0019113461,"threshold_uncertainty_score":0.018521428},"labels":[],"label_agreement":null},{"id":"W2967130730","doi":"10.1007/978-3-030-27202-9_32","title":"Safely Caching HOG Pyramid Feature Levels, to Speed up Facial Landmark Detection","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alcohol Countermeasure Systems (Canada); Toronto Metropolitan University","funders":"","keywords":"Computer science; Landmark; Artificial intelligence; Histogram; Pyramid (geometry); Feature (linguistics); Computer vision; Pattern recognition (psychology); Scale (ratio); Object detection; Naive Bayes classifier; Histogram of oriented gradients; Object (grammar); Image (mathematics); Support vector machine; Mathematics","score_opus":0.029565912019654104,"score_gpt":0.282781595580986,"score_spread":0.2532156835613319,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967130730","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":[0.13797711,0.0025851172,0.8210243,0.0003675219,0.00095655955,0.00026740285,0.0025859172,0.025702735,0.008533324],"genre_scores_gemma":[0.4010128,0.0010080395,0.57171565,0.0004008991,0.00013625817,0.000120953366,0.003476524,0.0014088384,0.020720132],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996816,0.000017020364,0.0000144930555,0.00007153659,0.00015046132,0.000064785716],"domain_scores_gemma":[0.99958736,0.000075538475,0.000023361661,0.00013635492,0.00014608435,0.00003126404],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027951988,0.0010423557,0.00090972276,0.0010635706,0.0004409792,0.0013820608,0.0012080456,0.00053854764,0.009125137],"category_scores_gemma":[0.001652399,0.0006236926,0.0005198707,0.0014813853,0.0002776204,0.0016791472,0.0013160375,0.00070268085,0.0036986682],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009025332,0.00022876167,0.002419448,0.0001264493,0.000106980464,0.00022714643,0.00006475482,0.016938576,0.100190856,0.0025363965,0.0347052,0.8415529],"study_design_scores_gemma":[0.00012610696,0.00031040775,0.003939764,0.000027833421,0.0001349119,0.0004324748,0.00012819807,0.8826493,0.08949739,0.0069212103,0.015790956,0.000041362775],"about_ca_topic_score_codex":0.01719558,"about_ca_topic_score_gemma":0.04558695,"teacher_disagreement_score":0.01719558,"about_ca_system_score_codex":0.0005657759,"about_ca_system_score_gemma":0.0014550713,"threshold_uncertainty_score":0.034191012},"labels":[],"label_agreement":null},{"id":"W2967555058","doi":"10.1007/978-3-030-27202-9_6","title":"Drift Detection Using SVM in Structured Object Tracking","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Support vector machine; Segmentation; Leverage (statistics); Video tracking; Tracking (education); Particle filter; Bounding overwatch; Minimum bounding box; Structured support vector machine; Pattern recognition (psychology); Structured prediction; Object detection; Object (grammar); Kalman filter; Image (mathematics)","score_opus":0.030889400351063103,"score_gpt":0.2879081219291493,"score_spread":0.2570187215780862,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967555058","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":[0.015676353,0.0011363614,0.98196304,0.00005825677,0.00010187784,0.000017354441,0.000041215084,0.0005134892,0.00049201783],"genre_scores_gemma":[0.43124327,0.001650077,0.5586941,0.00011150371,0.00020868621,0.00005480833,0.0004795571,0.00020084204,0.0073572034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962103,0.00007080468,0.000021847756,0.00013200635,0.00011218971,0.000042230164],"domain_scores_gemma":[0.9994497,0.00025183635,0.0000459303,0.00006947409,0.00015602629,0.000026987771],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010044393,0.0005278664,0.0012068052,0.0006624401,0.00033135127,0.0007524238,0.0008328628,0.0009286038,0.001020987],"category_scores_gemma":[0.0016155266,0.0004094853,0.00056113285,0.0010568606,0.00029478903,0.00091403566,0.000608545,0.0007885585,0.0008155742],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000231953,0.00007421926,0.0015988906,0.0001334621,0.000073104275,0.000073454255,0.00005622843,0.087974116,0.026296213,0.004052406,0.0031977904,0.87623817],"study_design_scores_gemma":[0.0000031456489,0.00003138381,0.0005080661,0.0000059930135,0.000012743864,0.00004065618,0.0000060765547,0.9935375,0.0036292297,0.0013857663,0.00083514163,0.0000042314277],"about_ca_topic_score_codex":0.0018505228,"about_ca_topic_score_gemma":0.0018761685,"teacher_disagreement_score":0.0018505228,"about_ca_system_score_codex":0.00037568403,"about_ca_system_score_gemma":0.0004093639,"threshold_uncertainty_score":0.005312085},"labels":[],"label_agreement":null},{"id":"W2967700132","doi":"10.1007/978-3-030-27272-2_7","title":"Information Fusion via Multimodal Hashing with Discriminant Canonical Correlation Maximization","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Linear discriminant analysis; Discriminant; Artificial intelligence; Canonical correlation; Maximization; Hash function; Pattern recognition (psychology); Fusion; Correlation; Sensor fusion; Mathematics; Mathematical optimization","score_opus":0.013354136991249814,"score_gpt":0.24565540100757835,"score_spread":0.23230126401632853,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2967700132","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":[0.0054240637,0.00031678058,0.99250424,0.000088406734,0.000054196997,0.000034321358,0.00006815638,0.00043004184,0.0010798807],"genre_scores_gemma":[0.26880142,0.00057680847,0.7240947,0.0002118484,0.00018661532,0.00016289859,0.0006344657,0.0001947218,0.0051365583],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987286,0.00038873576,0.00007064789,0.00025640722,0.00043421608,0.000121493955],"domain_scores_gemma":[0.9989064,0.00033468788,0.00007664965,0.0003726058,0.00025391488,0.000055764984],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018976775,0.0008395339,0.0017441887,0.0011138816,0.0006078103,0.001226417,0.0012938307,0.0010709822,0.0031884355],"category_scores_gemma":[0.0041380296,0.0005072046,0.0011025891,0.0021269429,0.0008609376,0.0027852764,0.004119985,0.0010700863,0.0018890216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004264079,0.00011523806,0.0006813699,0.00016819761,0.00014041581,0.000097528085,0.0001494446,0.092757195,0.026012639,0.044373497,0.008242745,0.82683533],"study_design_scores_gemma":[0.000018385168,0.0001378271,0.00042947268,0.000019413756,0.00004424135,0.00017946771,0.000050828574,0.9503531,0.015520271,0.029756302,0.00343721,0.000053478514],"about_ca_topic_score_codex":0.0009643969,"about_ca_topic_score_gemma":0.0011299131,"teacher_disagreement_score":0.0031884355,"about_ca_system_score_codex":0.00049394503,"about_ca_system_score_gemma":0.0007887738,"threshold_uncertainty_score":0.01066637},"labels":[],"label_agreement":null},{"id":"W2968339806","doi":"10.1007/978-3-030-27202-9_4","title":"Motion-Augmented Inference and Joint Kernels in Structured Learning for Object Tracking","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Particle filter; Computer science; Artificial intelligence; Inference; Kernel (algebra); Feature vector; Entropy (arrow of time); Video tracking; Regularization (linguistics); Pattern recognition (psychology); Computer vision; Object (grammar); Exploit; Mathematics; Kalman filter","score_opus":0.0369903024693669,"score_gpt":0.29557008550599895,"score_spread":0.25857978303663204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968339806","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":[0.0020278955,0.0001964025,0.99732167,0.000043123542,0.00001616618,0.0000058689557,0.000020464282,0.0002094144,0.00015903612],"genre_scores_gemma":[0.20576982,0.00082308444,0.78550184,0.00014831728,0.00014046484,0.00009760989,0.00050475675,0.0004127001,0.0066014137],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912137,0.0003281564,0.00005612553,0.0002579756,0.00015966763,0.00007663772],"domain_scores_gemma":[0.9966186,0.002230265,0.00016657048,0.0005943548,0.00031213296,0.00007805929],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020158582,0.0007825952,0.0017515928,0.0006264998,0.00043588356,0.0011206617,0.0023776612,0.0017685798,0.002332558],"category_scores_gemma":[0.007974049,0.0010840188,0.0011079294,0.0012642286,0.0013333077,0.0032775218,0.0022688296,0.0030796723,0.0009571398],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002879989,0.0001324335,0.0005972842,0.0001884751,0.0001288879,0.00006177895,0.00015081573,0.5217753,0.006170329,0.08888033,0.004295327,0.37733105],"study_design_scores_gemma":[0.000004636382,0.000012298385,0.0000708316,0.000004148546,0.0000061618753,0.000008850248,0.0000036845993,0.97458696,0.0005882512,0.02434184,0.0003659599,0.0000062559207],"about_ca_topic_score_codex":0.0072827074,"about_ca_topic_score_gemma":0.0078771645,"teacher_disagreement_score":0.0072827074,"about_ca_system_score_codex":0.0009394053,"about_ca_system_score_gemma":0.0010091556,"threshold_uncertainty_score":0.01448065},"labels":[],"label_agreement":null},{"id":"W2968524820","doi":"10.1109/icra.2019.8794278","title":"Classifying Pedestrian Actions In Advance Using Predicted Video Of Urban Driving Scenes","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","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; Simon Fraser University","keywords":"Pedestrian; Computer science; Encoder; Artificial intelligence; Ground truth; Sequence (biology); Computer vision; Key (lock); Pedestrian crossing; Pattern recognition (psychology); Computer security; Transport engineering; Engineering","score_opus":0.049433308443651805,"score_gpt":0.3272837222633687,"score_spread":0.2778504138197169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2968524820","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":[0.8376748,0.00048211653,0.14561312,0.00025708097,0.00017674243,0.00019724038,0.0048950724,0.0066627096,0.0040410394],"genre_scores_gemma":[0.94719243,0.00014293585,0.046866104,0.000040490457,0.000020351086,0.000026278156,0.004152881,0.00005202267,0.0015065244],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999863,0.000016533373,0.000004942969,0.000058202186,0.000032230088,0.000025093135],"domain_scores_gemma":[0.9996735,0.00011550365,0.000025410562,0.00003438672,0.00012241153,0.000028838762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036923555,0.000779029,0.00027219614,0.00058204064,0.00018069785,0.00036890848,0.0004356854,0.0005035371,0.001729137],"category_scores_gemma":[0.0012969546,0.00019097698,0.00024766248,0.00032389595,0.00013560688,0.00032187966,0.00018682174,0.000362865,0.0006985402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00227399,0.0007245509,0.06682297,0.00021869967,0.00019380126,0.0008318286,0.00020738499,0.32727975,0.0405596,0.0011126692,0.013647702,0.5461271],"study_design_scores_gemma":[0.000011269607,0.00012546242,0.010109273,0.000014099791,0.000018285804,0.000114294664,0.000042482854,0.9795872,0.009079964,0.00031644173,0.00057163084,0.000009636823],"about_ca_topic_score_codex":0.014013691,"about_ca_topic_score_gemma":0.019152237,"teacher_disagreement_score":0.014013691,"about_ca_system_score_codex":0.00046500535,"about_ca_system_score_gemma":0.0004170197,"threshold_uncertainty_score":0.027864218},"labels":[],"label_agreement":null},{"id":"W2969381273","doi":"10.1109/dcoss.2019.00103","title":"Emerging Urban Challenge: RPAS/UAVs in Cities","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Drone; Agile software development; Computer science; Computer security; Software deployment; Transport engineering; Engineering","score_opus":0.02062648517353766,"score_gpt":0.2839797493477921,"score_spread":0.2633532641742544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969381273","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3588066,0.027147684,0.41296804,0.03239465,0.0019208494,0.0002463642,0.0005820203,0.0026063318,0.16332747],"genre_scores_gemma":[0.8694781,0.009280041,0.08847007,0.0017560966,0.00046657634,0.00007601802,0.00032497282,0.00017043321,0.029977692],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995716,0.0001248453,0.000011779003,0.000063773805,0.0001407977,0.00008715931],"domain_scores_gemma":[0.99946517,0.00015240777,0.00004866527,0.00009220526,0.00015339567,0.000088314584],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069161464,0.00041477414,0.00026481363,0.0004434549,0.00084318646,0.0019688855,0.0008646181,0.0014306866,0.0040098666],"category_scores_gemma":[0.0007968336,0.00023968433,0.0002551594,0.00071377796,0.0010455482,0.0023000182,0.0018457815,0.0010753445,0.0011056162],"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":[0.00039259833,0.00021012,0.015163227,0.00094569655,0.000089915855,0.0031193749,0.0035040365,0.049807716,0.038287684,0.16561839,0.05794453,0.6649167],"study_design_scores_gemma":[0.00005899346,0.0006208639,0.019916771,0.0005620736,0.00010277679,0.0055825827,0.023082012,0.20172437,0.03665107,0.08750352,0.6239837,0.00021126188],"about_ca_topic_score_codex":0.0034594035,"about_ca_topic_score_gemma":0.0075160814,"teacher_disagreement_score":0.0040098666,"about_ca_system_score_codex":0.00052134274,"about_ca_system_score_gemma":0.0004046323,"threshold_uncertainty_score":0.013414383},"labels":[],"label_agreement":null},{"id":"W2969429527","doi":"10.1109/jsen.2019.2936916","title":"Cross-Modality Person Re-Identification Based on Dual-Path Multi-Branch Network","year":2019,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Natural Science Foundation of Heilongjiang Province; Northwestern Polytechnical University; National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Modality (human–computer interaction); Computer vision; RGB color model; Identification (biology); Convolutional neural network; Pattern recognition (psychology)","score_opus":0.04810826150359843,"score_gpt":0.33134889021383146,"score_spread":0.28324062871023303,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969429527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16081242,0.0010301663,0.8241156,0.00051754346,0.00028336738,0.00015768326,0.000565359,0.0048282677,0.0076895966],"genre_scores_gemma":[0.8300935,0.00036786706,0.1532215,0.0004788278,0.000118278025,0.00010132643,0.0019236511,0.00015366205,0.013541387],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99944407,0.00007951181,0.000016693273,0.00022556563,0.00011134492,0.00012276873],"domain_scores_gemma":[0.999559,0.00007793723,0.00005188289,0.00009122184,0.00017178345,0.00004821879],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007097813,0.0012755388,0.001104796,0.00082930113,0.0005280992,0.0006288649,0.0017225845,0.0012460349,0.0024109706],"category_scores_gemma":[0.0014742153,0.0003911915,0.00063300686,0.0006205282,0.00039885496,0.0016596303,0.0016155151,0.0014071346,0.0015507524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008997348,0.00054455124,0.007161053,0.00011870188,0.00021386359,0.00048279727,0.00025423898,0.11909862,0.026759826,0.0025886074,0.012870071,0.829008],"study_design_scores_gemma":[0.0000092809305,0.00009875934,0.0014833574,0.000013211509,0.00003542428,0.00016717143,0.00004669472,0.9864368,0.008165331,0.0021918523,0.0013371609,0.000014996779],"about_ca_topic_score_codex":0.004948531,"about_ca_topic_score_gemma":0.0074980403,"teacher_disagreement_score":0.004948531,"about_ca_system_score_codex":0.000606154,"about_ca_system_score_gemma":0.00051695574,"threshold_uncertainty_score":0.009839475},"labels":[],"label_agreement":null},{"id":"W2969730556","doi":"10.1007/978-3-030-33720-9_4","title":"DomainSiam: Domain-Aware Siamese Network for Visual Object Tracking","year":2019,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; Memorial University of Newfoundland","funders":"","keywords":"Overfitting; Computer science; BitTorrent tracker; Artificial intelligence; Task (project management); Feature (linguistics); Set (abstract data type); Domain (mathematical analysis); Machine learning; Computer vision; Class (philosophy); Deep learning; Object (grammar); Pattern recognition (psychology); Eye tracking; Artificial neural network","score_opus":0.025962792774452775,"score_gpt":0.32532938292318603,"score_spread":0.29936659014873324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2969730556","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0036773558,0.00015155977,0.98738164,0.00008323947,0.000061395556,0.000057086316,0.00037516947,0.0072258594,0.0009867634],"genre_scores_gemma":[0.12532558,0.0002838578,0.863213,0.00019752506,0.00007317026,0.00025283097,0.0027098756,0.0011429297,0.0068012434],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954283,0.00009223106,0.000021374974,0.00015702264,0.00013581026,0.000050759438],"domain_scores_gemma":[0.99925965,0.00020718726,0.000041828454,0.00024320836,0.00018139242,0.000066708526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010716363,0.0010539608,0.00092228915,0.0014111121,0.00056481146,0.0011955104,0.0021369215,0.0012392877,0.0060037524],"category_scores_gemma":[0.0032348623,0.00062253664,0.00076875504,0.0013783645,0.00051985437,0.0017756722,0.0025357057,0.0018017164,0.0027781005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00056619715,0.00031908133,0.0015175324,0.00019197396,0.00020894175,0.00015144232,0.00011418664,0.24113989,0.019751059,0.019575216,0.039783843,0.6766807],"study_design_scores_gemma":[0.000018907587,0.000019257961,0.00016073091,0.0000045682073,0.0000070378996,0.00003494517,0.000009287795,0.9868859,0.0031531483,0.006629098,0.0030685847,0.000008473369],"about_ca_topic_score_codex":0.007131402,"about_ca_topic_score_gemma":0.015326073,"teacher_disagreement_score":0.007131402,"about_ca_system_score_codex":0.0007079607,"about_ca_system_score_gemma":0.0013020991,"threshold_uncertainty_score":0.02008462},"labels":[],"label_agreement":null},{"id":"W2972012950","doi":"10.48550/arxiv.1811.07130","title":"Batch DropBlock Network for Person Re-identification and Beyond","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Feature (linguistics); Computer science; Margin (machine learning); Artificial intelligence; Salient; Feature learning; Pattern recognition (psychology); Identification (biology); Metric (unit); Representation (politics); Machine learning; Engineering","score_opus":0.09844570635410742,"score_gpt":0.22933165505671188,"score_spread":0.13088594870260445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972012950","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10705216,0.0021631904,0.8587577,0.00061021326,0.00037468193,0.00027954736,0.001618153,0.016442642,0.012701676],"genre_scores_gemma":[0.66186225,0.00082858826,0.28831002,0.0008933955,0.00026868915,0.00027918982,0.009069613,0.00072575937,0.03776256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994324,0.00007763154,0.000016991926,0.00024487573,0.00012115409,0.000106875195],"domain_scores_gemma":[0.9994929,0.00009444812,0.000045628414,0.00019405647,0.00012913137,0.000043775595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006825224,0.0017307148,0.0012673289,0.0008441294,0.00054165424,0.0007080245,0.0022615595,0.001289561,0.006094783],"category_scores_gemma":[0.0016235852,0.0005040848,0.00073339744,0.0005609668,0.00053319737,0.0021032528,0.0015501309,0.0015028679,0.0038174267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012687818,0.00040161458,0.0038959673,0.00017282076,0.00015776575,0.00038492435,0.00018317335,0.08206885,0.029942675,0.0070236647,0.034686387,0.8398134],"study_design_scores_gemma":[0.000025164958,0.00017801106,0.0013968032,0.00002307822,0.000049548325,0.00018930342,0.000040453357,0.9703406,0.014915534,0.005710997,0.0071056993,0.000024818324],"about_ca_topic_score_codex":0.008546335,"about_ca_topic_score_gemma":0.012376089,"teacher_disagreement_score":0.008546335,"about_ca_system_score_codex":0.000813107,"about_ca_system_score_gemma":0.00080166257,"threshold_uncertainty_score":0.02038908},"labels":[],"label_agreement":null},{"id":"W2972833696","doi":"10.3390/sym11091155","title":"Low-Rank Multi-Channel Features for Robust Visual Object Tracking","year":2019,"lang":"en","type":"article","venue":"Symmetry","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Artificial intelligence; Robustness (evolution); Pattern recognition (psychology); Computer science; Support vector machine; Kernel (algebra); Histogram; Computational complexity theory; Video tracking; Computer vision; Eye tracking; Local binary patterns; Circulant matrix; Histogram of oriented gradients; Mathematics; Algorithm; Object (grammar); Image (mathematics)","score_opus":0.03141101484369791,"score_gpt":0.31345672352449777,"score_spread":0.28204570868079987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2972833696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01227492,0.00029500856,0.9859138,0.000057734782,0.000041180127,0.000026971353,0.00011682829,0.00074690004,0.00052670366],"genre_scores_gemma":[0.55450875,0.00060885644,0.4399243,0.00013342449,0.0001106208,0.00012162383,0.0012770221,0.00021455919,0.0031008117],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992693,0.00012057579,0.00004284168,0.00015040915,0.00033717585,0.000079557976],"domain_scores_gemma":[0.9988171,0.0003007291,0.0001829451,0.00026764162,0.0003841874,0.000047467354],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009401485,0.00056179246,0.0008123275,0.0011611151,0.00028224496,0.0008392232,0.0007023775,0.000589774,0.0013714058],"category_scores_gemma":[0.003265057,0.00022697063,0.0005750152,0.0014509796,0.00033843162,0.0010413929,0.0005980862,0.0008961893,0.00092530774],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022407171,0.0001401943,0.002406173,0.00015046363,0.00008293723,0.000093895535,0.000060836068,0.096932754,0.058426693,0.010415833,0.0068165534,0.82424957],"study_design_scores_gemma":[0.000010194215,0.000068474714,0.001914298,0.000009751427,0.000018140705,0.00009491308,0.000014493047,0.97404104,0.018312804,0.0028091785,0.0026833655,0.000023351577],"about_ca_topic_score_codex":0.0030809857,"about_ca_topic_score_gemma":0.003345316,"teacher_disagreement_score":0.0030809857,"about_ca_system_score_codex":0.00055606395,"about_ca_system_score_gemma":0.0009802259,"threshold_uncertainty_score":0.006126106},"labels":[],"label_agreement":null},{"id":"W2973408110","doi":"10.1139/cjce-2019-0087","title":"Enhancing unsupervised video-based vehicle tracking and modeling for traffic data collection","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Headway; Computer science; Data collection; Minimum bounding box; Ground truth; Data mining; Artificial intelligence; Segmentation; Traffic flow (computer networking); Sensor fusion; Computer vision; Real-time computing; Simulation; Image (mathematics)","score_opus":0.03536193240355473,"score_gpt":0.24937219276196987,"score_spread":0.21401026035841514,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973408110","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04088307,0.000036998263,0.9581953,0.000023713554,0.000006901143,0.000030352148,0.000089527995,0.00037914523,0.00035500777],"genre_scores_gemma":[0.63850015,0.00015002699,0.3589617,0.000042161882,0.000029607163,0.00017571557,0.0007722444,0.00010199738,0.0012663291],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995017,0.00012355587,0.000022728618,0.00014679074,0.0001540368,0.00005107291],"domain_scores_gemma":[0.9993106,0.0002924057,0.000096367796,0.000082527156,0.00019550757,0.000022542617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066747604,0.00053389557,0.00058645225,0.00075912854,0.00029867803,0.00062257843,0.00096345093,0.0005707755,0.00029072768],"category_scores_gemma":[0.002204899,0.0003272459,0.0006156247,0.00069953746,0.00034901945,0.0008162192,0.0005309613,0.0006492696,0.00020582227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0000891994,0.00012758643,0.0048920326,0.000043296513,0.000043674438,0.000042230677,0.00012309059,0.8805535,0.020934962,0.0027984607,0.00043430048,0.08991772],"study_design_scores_gemma":[0.0000013367315,0.0000073744955,0.0006646351,0.0000012800612,0.0000021776111,0.0000070086576,0.000004691065,0.99738747,0.0015405327,0.00023371674,0.000146428,0.000003383036],"about_ca_topic_score_codex":0.031035407,"about_ca_topic_score_gemma":0.032391727,"teacher_disagreement_score":0.031035407,"about_ca_system_score_codex":0.0007427605,"about_ca_system_score_gemma":0.0010570146,"threshold_uncertainty_score":0.061709523},"labels":[],"label_agreement":null},{"id":"W2973617321","doi":"10.1109/access.2019.2941978","title":"IoT-Guard: Event-Driven Fog-Based Video Surveillance System for Real-Time Security Management","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":81,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Computer science; Testbed; Guard (computer science); Scalability; Computer security; Edge computing; Internet of Things; Real-time computing; Computer network; Database","score_opus":0.017825312407642752,"score_gpt":0.3110033456493996,"score_spread":0.29317803324175684,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973617321","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16575696,0.00084274955,0.78874016,0.0004547438,0.0004292794,0.00058251544,0.0007302577,0.021271396,0.021191861],"genre_scores_gemma":[0.8913173,0.0003291908,0.10117549,0.00033398982,0.000054879372,0.00016521417,0.0008217904,0.0001335705,0.0056685912],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998356,0.000015879916,0.000011848923,0.00004551149,0.00005854881,0.000032593318],"domain_scores_gemma":[0.9998753,0.000013463948,0.000017542823,0.000021072437,0.0000397321,0.000032948337],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024118449,0.0003652568,0.00035264262,0.0003777697,0.00035777877,0.00048397545,0.0010749861,0.0003976583,0.0016727359],"category_scores_gemma":[0.00026821063,0.00012301792,0.00025812304,0.00020099881,0.00022148206,0.0007212881,0.00055815664,0.0003100466,0.00038965011],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002477641,0.0012482837,0.017202046,0.0007638637,0.0002675276,0.0022255718,0.0009212531,0.050238457,0.35586905,0.022341967,0.057705116,0.48873925],"study_design_scores_gemma":[0.0002306462,0.0006648869,0.010269451,0.00006862534,0.0001854042,0.0010101106,0.00029372144,0.81581485,0.10963209,0.005392543,0.05631722,0.00012045409],"about_ca_topic_score_codex":0.0029969506,"about_ca_topic_score_gemma":0.0032088521,"teacher_disagreement_score":0.0029969506,"about_ca_system_score_codex":0.00038924007,"about_ca_system_score_gemma":0.00059818086,"threshold_uncertainty_score":0.005959034},"labels":[],"label_agreement":null},{"id":"W2973851319","doi":"10.3390/rs11182155","title":"Orientation- and Scale-Invariant Multi-Vehicle Detection and Tracking from Unmanned Aerial Videos","year":2019,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Computer vision; Artificial intelligence; Vehicle tracking system; Kalman filter; Orientation (vector space); Video tracking; Object detection; Tracking (education); Video processing; Segmentation","score_opus":0.019728188631295742,"score_gpt":0.2705043076829735,"score_spread":0.2507761190516778,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2973851319","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":[0.20443134,0.00060397317,0.7878262,0.00012453331,0.00010656846,0.00013929362,0.0008395995,0.0025288702,0.0033995889],"genre_scores_gemma":[0.76354045,0.0004022775,0.23134564,0.00009699963,0.00004757261,0.00009617945,0.0020298113,0.00007322475,0.0023678066],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973565,0.000021001408,0.0000084591575,0.00009345418,0.000090039845,0.000051349314],"domain_scores_gemma":[0.9998136,0.000033514283,0.000033997934,0.000032344607,0.00006887152,0.00001773613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000224257,0.0006102712,0.0003451296,0.00092593965,0.00017735374,0.0003554637,0.00055476115,0.00043839603,0.00057707465],"category_scores_gemma":[0.00071654026,0.00021133435,0.0003095129,0.0004791965,0.00017536292,0.00047717793,0.0004605732,0.00043428157,0.00042720407],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018687494,0.00016073749,0.004493934,0.00012794216,0.00006927158,0.00028243847,0.0000838458,0.116799,0.1483645,0.0016182029,0.0037815338,0.72403175],"study_design_scores_gemma":[0.000008406941,0.000061805404,0.0067233574,0.000011755014,0.000013388644,0.00014043287,0.00003420606,0.9579533,0.032669432,0.0007026262,0.0016685344,0.000012779299],"about_ca_topic_score_codex":0.0064808843,"about_ca_topic_score_gemma":0.008869062,"teacher_disagreement_score":0.0064808843,"about_ca_system_score_codex":0.0002757007,"about_ca_system_score_gemma":0.00041265626,"threshold_uncertainty_score":0.012886286},"labels":[],"label_agreement":null},{"id":"W2976931991","doi":"10.48550/arxiv.1805.11123","title":"Global Sum Pooling: A Generalization Trick for Object Counting with\\n Small Datasets of Large Images","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Overfitting; Pooling; Computer science; Generalization; Artificial intelligence; Convolutional neural network; Inference; Pattern recognition (psychology); Focus (optics); Object (grammar); Image (mathematics); Property (philosophy); Machine learning; Artificial neural network; Mathematics","score_opus":0.085670418535971,"score_gpt":0.248886750074821,"score_spread":0.16321633153885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2976931991","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":[0.013371086,0.00056571217,0.97872007,0.0003721443,0.00009750622,0.00012102289,0.0005313216,0.0049610115,0.001260205],"genre_scores_gemma":[0.31198734,0.0011498239,0.6703837,0.0012733764,0.00041436197,0.0006176917,0.004109428,0.0010224289,0.009041814],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983662,0.00027280496,0.00009906986,0.0007876807,0.0003008124,0.0001734512],"domain_scores_gemma":[0.9978613,0.0006580089,0.00020043484,0.0009864261,0.00020356443,0.000090260386],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004895484,0.0040350677,0.0024115557,0.0014357051,0.00077496073,0.0019087855,0.0058310116,0.0022617811,0.003776581],"category_scores_gemma":[0.008088093,0.0012618382,0.0026665295,0.0024714957,0.0017109156,0.0056047826,0.0036290663,0.0042230026,0.0022288817],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003949777,0.00022153104,0.0036134853,0.00041766066,0.0007483282,0.00037305985,0.0002224685,0.21678384,0.021141654,0.016068019,0.014795316,0.7252196],"study_design_scores_gemma":[0.000022936081,0.00011270269,0.0012736778,0.000039052815,0.000100304016,0.00015857563,0.000043051485,0.960797,0.0109979035,0.021867989,0.0045565767,0.000030247345],"about_ca_topic_score_codex":0.009102057,"about_ca_topic_score_gemma":0.011529678,"teacher_disagreement_score":0.009102057,"about_ca_system_score_codex":0.0015198751,"about_ca_system_score_gemma":0.0015457718,"threshold_uncertainty_score":0.025890052},"labels":[],"label_agreement":null},{"id":"W2979856626","doi":"10.30880/ijie.2019.11.04.001","title":"Contour Based Tracking for Driveway Entrance Counting System","year":2019,"lang":"en","type":"article","venue":"International Journal of Integrated Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Ministry of Education, India; Universiti Kebangsaan Malaysia","keywords":"Computer vision; Guard (computer science); Artificial intelligence; Computer science; Tracking (education); Frame (networking); Process (computing); Object (grammar); Heuristic; Vehicle tracking system; Task (project management); Engineering; Segmentation","score_opus":0.009550403974004867,"score_gpt":0.24887065556926025,"score_spread":0.23932025159525538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2979856626","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11565408,0.00039518642,0.85589546,0.00012948467,0.00015285976,0.00020212348,0.0006079399,0.011879406,0.015083495],"genre_scores_gemma":[0.7147338,0.00040417264,0.26881102,0.00012000178,0.000046150453,0.00014190121,0.0010997023,0.00014904258,0.014494218],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997725,0.0000133435115,0.000012775402,0.000060221315,0.00012055152,0.000020656313],"domain_scores_gemma":[0.999747,0.000031542124,0.000029492938,0.000031628664,0.00014595156,0.00001441338],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017529722,0.00027110058,0.00038613152,0.000781905,0.00025190486,0.00064926525,0.000669808,0.00047233616,0.0029497212],"category_scores_gemma":[0.0006328165,0.00019330753,0.00018332253,0.00047537807,0.000115699244,0.0005131591,0.0003243573,0.00027417482,0.0012797797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005031303,0.00010898684,0.0069855493,0.00017411695,0.000031224776,0.00031784442,0.0002323111,0.0141727375,0.154899,0.00323588,0.01010176,0.80923754],"study_design_scores_gemma":[0.00006819057,0.0004670885,0.0217415,0.00006461322,0.00010090473,0.0010665691,0.00012871262,0.7186903,0.22322349,0.001571227,0.032774363,0.00010314966],"about_ca_topic_score_codex":0.0027729084,"about_ca_topic_score_gemma":0.0029866914,"teacher_disagreement_score":0.0029497212,"about_ca_system_score_codex":0.00038972255,"about_ca_system_score_gemma":0.0004622461,"threshold_uncertainty_score":0.009867787},"labels":[],"label_agreement":null},{"id":"W2980171313","doi":"10.1049/iet-ipr.2019.0334","title":"Training approach using the shallow model and hard triplet mining for person re‐identification","year":2019,"lang":"en","type":"article","venue":"IET Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"Institute for Information and Communications Technology Promotion","keywords":"Identification (biology); Computer science; Training (meteorology); Artificial intelligence; Machine learning; Data mining; Physics","score_opus":0.15902094953147755,"score_gpt":0.34440641180561593,"score_spread":0.18538546227413838,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2980171313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06728152,0.00059221004,0.92244774,0.00030822854,0.00016721536,0.00016273823,0.0005059356,0.0054740533,0.003060324],"genre_scores_gemma":[0.630652,0.00038078468,0.35531375,0.0006792262,0.000121069505,0.0001694589,0.0043492927,0.00027911298,0.0080552725],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998711,0.00020961775,0.00007664994,0.0005675957,0.00026098796,0.00017411797],"domain_scores_gemma":[0.9989949,0.00021402563,0.000088678375,0.00039970764,0.00022565894,0.000077044075],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012569304,0.0017433513,0.0017592215,0.0015613367,0.00082607695,0.0008283921,0.0026376985,0.0018630179,0.0034654995],"category_scores_gemma":[0.0026403277,0.0007055229,0.0018524932,0.0015567014,0.00047358926,0.0029087658,0.0021526283,0.0026555993,0.0031837362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039422818,0.0007358984,0.0072790678,0.0001276798,0.0002844121,0.00031192557,0.0001964892,0.09127694,0.017227443,0.0033358163,0.012999813,0.8658303],"study_design_scores_gemma":[0.000017822225,0.00013130881,0.0010674909,0.0000088279185,0.000047649468,0.00017084104,0.000054251843,0.9898902,0.0038611093,0.0033606081,0.0013705678,0.000019329485],"about_ca_topic_score_codex":0.008362988,"about_ca_topic_score_gemma":0.010971632,"teacher_disagreement_score":0.008362988,"about_ca_system_score_codex":0.00061265915,"about_ca_system_score_gemma":0.0012561483,"threshold_uncertainty_score":0.016628623},"labels":[],"label_agreement":null},{"id":"W2981657066","doi":"10.1109/tii.2019.2949347","title":"Edge Coordinated Query Configuration for Low-Latency and Accurate Video Analytics","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":96,"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 Science Foundation of Hubei Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computer science; Video tracking; Analytics; Cloud computing; Latency (audio); Video quality; Edge computing; Low latency (capital markets); Video processing; Enhanced Data Rates for GSM Evolution; Real-time computing; Video post-processing; Uncompressed video; Artificial intelligence; Computer network; Database","score_opus":0.05528549721993517,"score_gpt":0.2933357789928377,"score_spread":0.23805028177290252,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2981657066","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09808808,0.0004103266,0.89609915,0.00018530966,0.000059035883,0.00016545974,0.00010512829,0.0019528284,0.0029347243],"genre_scores_gemma":[0.92289346,0.00010429266,0.07573753,0.00008880221,0.000027154007,0.00006651295,0.00014357416,0.000083751336,0.0008547818],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989312,0.00019604656,0.00005346189,0.00032588272,0.00026690363,0.00022654886],"domain_scores_gemma":[0.9988759,0.00034748059,0.00015196983,0.00024265518,0.00025538658,0.00012657886],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00088918867,0.0009274645,0.0007335826,0.00046912045,0.00080966915,0.0010737604,0.0018762596,0.0005567887,0.0012592011],"category_scores_gemma":[0.0029055038,0.00026045152,0.00021125798,0.0006183199,0.00048093655,0.0016513546,0.0011667791,0.0007636641,0.00028628032],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014043759,0.00049989513,0.00748813,0.00013911969,0.00007001131,0.0005358325,0.0003555044,0.70068365,0.051939756,0.012690263,0.008582072,0.21561137],"study_design_scores_gemma":[0.000019508678,0.00008346424,0.0005402766,0.0000025647428,0.000008233454,0.000063532345,0.00006122299,0.9908564,0.005803958,0.0018303512,0.00072051585,0.000010071834],"about_ca_topic_score_codex":0.004582365,"about_ca_topic_score_gemma":0.004242151,"teacher_disagreement_score":0.004582365,"about_ca_system_score_codex":0.00077909284,"about_ca_system_score_gemma":0.0010869063,"threshold_uncertainty_score":0.009111404},"labels":[],"label_agreement":null},{"id":"W2982296769","doi":"10.48550/arxiv.1910.12945","title":"Literature Review: Human Segmentation with Static Camera","year":2019,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Segmentation; Computer vision; Artificial intelligence; Computer science","score_opus":0.06251967614211922,"score_gpt":0.2338388280668715,"score_spread":0.17131915192475228,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2982296769","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":[0.00059874146,0.9900085,0.003581159,0.0010776956,0.0007206074,0.0000323434,0.00022156583,0.00008796989,0.0036714212],"genre_scores_gemma":[0.0050036176,0.9864184,0.0042194705,0.0010363827,0.001120599,0.000047145335,0.00072156644,0.000044493678,0.0013882207],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9987343,0.0002795615,0.00018111976,0.00041453185,0.0003135174,0.00007710262],"domain_scores_gemma":[0.990858,0.0060407226,0.0005473579,0.00026186666,0.0021172022,0.0001748672],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016221474,0.0015561826,0.0020933123,0.007617961,0.0007774979,0.0022683481,0.0021338877,0.0024865933,0.008784015],"category_scores_gemma":[0.010465214,0.00088107714,0.0013938228,0.011131257,0.0009062661,0.0038004697,0.00089037226,0.0012539763,0.004019625],"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":[0.00014364334,0.000076142955,0.00089274894,0.06818931,0.00029867177,0.00030689797,0.00024724772,0.0018789044,0.0010145967,0.0076246583,0.09694659,0.82238054],"study_design_scores_gemma":[0.000035046076,0.00019736661,0.0051293117,0.06089314,0.0013888814,0.002404014,0.00083845377,0.0027563616,0.0018107992,0.012638193,0.9117733,0.00013511254],"about_ca_topic_score_codex":0.0069496576,"about_ca_topic_score_gemma":0.0075830165,"teacher_disagreement_score":0.008784015,"about_ca_system_score_codex":0.001258758,"about_ca_system_score_gemma":0.0035699967,"threshold_uncertainty_score":0.029385507},"labels":[],"label_agreement":null},{"id":"W2984040540","doi":"10.1109/iccv.2019.00379","title":"Batch DropBlock Network for Person Re-Identification and Beyond","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":255,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Feature (linguistics); Computer science; Artificial intelligence; Margin (machine learning); Feature learning; Salient; Pattern recognition (psychology); Metric (unit); Representation (politics); Identification (biology); Feature extraction; Machine learning; Engineering","score_opus":0.025270742531560343,"score_gpt":0.2827099289710358,"score_spread":0.25743918643947544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2984040540","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.110930726,0.002075466,0.85531753,0.00053266756,0.0003745494,0.00029017113,0.0015192002,0.017102996,0.011856619],"genre_scores_gemma":[0.6722108,0.00078128284,0.27873525,0.000846947,0.00022452907,0.0002644088,0.008227525,0.00066948176,0.038039718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995316,0.00005851125,0.000014364447,0.00019411859,0.000106423,0.000094971605],"domain_scores_gemma":[0.9995708,0.000077403354,0.000037906073,0.00015569097,0.00011943253,0.00003886184],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006200254,0.0015903936,0.0011784065,0.0007664162,0.00049139693,0.0006289123,0.0020365606,0.0011047549,0.006139992],"category_scores_gemma":[0.0013210934,0.00046674995,0.0006552793,0.0004777876,0.00048991805,0.0018998219,0.0014176659,0.0013328291,0.003553561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00130643,0.00036350597,0.0035246809,0.00016126403,0.00014378414,0.00037383006,0.00016395829,0.072818495,0.03543981,0.0058089695,0.03234997,0.84754544],"study_design_scores_gemma":[0.00002710147,0.00020398987,0.0014488705,0.000022158469,0.000052477935,0.00019465313,0.000040777973,0.9659244,0.019605689,0.004818474,0.0076348335,0.000026660498],"about_ca_topic_score_codex":0.008754165,"about_ca_topic_score_gemma":0.013035334,"teacher_disagreement_score":0.008754165,"about_ca_system_score_codex":0.0007651417,"about_ca_system_score_gemma":0.0007695855,"threshold_uncertainty_score":0.020540297},"labels":[],"label_agreement":null},{"id":"W2987468779","doi":"10.1016/j.neucom.2019.10.081","title":"DCGSA: A global self-attention network with dilated convolution for crowd density map generating","year":2019,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Upsampling; Computer science; Pooling; Artificial intelligence; Convolution (computer science); Convolutional neural network; Pyramid (geometry); Context (archaeology); Pattern recognition (psychology); Density estimation; Perspective (graphical); Pixel; Computer vision; Artificial neural network; Image (mathematics); Mathematics; Geography","score_opus":0.00965658640923097,"score_gpt":0.24589116915658024,"score_spread":0.23623458274734926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987468779","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014835051,0.00037072934,0.97952867,0.00020639903,0.0001713662,0.0000917238,0.00022066101,0.0029218455,0.0016535566],"genre_scores_gemma":[0.40319192,0.00043224514,0.5815838,0.0006558116,0.00023606537,0.0003079451,0.0012534881,0.00051126507,0.01182745],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997012,0.000048675094,0.000013826383,0.00010990777,0.00007428693,0.000052084077],"domain_scores_gemma":[0.9995284,0.00014755315,0.000030752348,0.00007471171,0.00015514367,0.000063521766],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007761717,0.0011472021,0.0011424909,0.0012388357,0.0007483517,0.0007316881,0.0025868237,0.0017058079,0.004158668],"category_scores_gemma":[0.00147943,0.0006947923,0.0009845345,0.0009898283,0.0006022419,0.0012337422,0.0021395416,0.0012655244,0.001210638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030820037,0.00026846436,0.001494036,0.00010783633,0.0001673534,0.00016975829,0.00014182903,0.34342462,0.014043689,0.006992313,0.012602577,0.62027925],"study_design_scores_gemma":[0.0000045991455,0.000015122935,0.00009282918,0.0000026854093,0.0000062379377,0.000017225731,0.0000049480072,0.99676704,0.0013897367,0.0011454476,0.00054990867,0.0000042591973],"about_ca_topic_score_codex":0.016175706,"about_ca_topic_score_gemma":0.019593148,"teacher_disagreement_score":0.016175706,"about_ca_system_score_codex":0.0011047965,"about_ca_system_score_gemma":0.0012431015,"threshold_uncertainty_score":0.032163143},"labels":[],"label_agreement":null},{"id":"W2987878306","doi":"10.1109/tcsvt.2019.2951778","title":"Dynamic Deep Pixel Distribution Learning for Background Subtraction","year":2019,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Pixel; Computer science; Random permutation; Background subtraction; Deep learning; Pattern recognition (psychology); Noise (video); Benchmark (surveying); Machine learning; Computer vision; Mathematics; Image (mathematics)","score_opus":0.022443118621899367,"score_gpt":0.2817593106939884,"score_spread":0.25931619207208906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2987878306","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0055128564,0.0002249879,0.9925007,0.00008293752,0.000017933417,0.00001394984,0.00005313491,0.0010067902,0.00058670534],"genre_scores_gemma":[0.43335712,0.0009540768,0.558482,0.0004283183,0.00008459543,0.00008745658,0.0009795168,0.0004916565,0.005135241],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953496,0.00007348827,0.000017821445,0.00015186098,0.00015080208,0.000070978684],"domain_scores_gemma":[0.9996045,0.00013579936,0.000048118454,0.0000705153,0.000109497094,0.000031482214],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090542325,0.0011048717,0.0009944552,0.0011498086,0.0003112383,0.00086313765,0.0019131429,0.00086125795,0.001791205],"category_scores_gemma":[0.001559659,0.00056592334,0.000792005,0.0010710311,0.0006029977,0.0018406283,0.0012377452,0.0017976803,0.00081053836],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028035886,0.00014064113,0.0014580142,0.00012230432,0.00013004536,0.00010656318,0.00007246513,0.31622747,0.029767036,0.015816728,0.0034112022,0.6324671],"study_design_scores_gemma":[0.0000051194843,0.000016283548,0.00020547236,0.0000051396687,0.000009787312,0.00004054361,0.000005132997,0.98513436,0.008413532,0.005254465,0.0009037173,0.000006465484],"about_ca_topic_score_codex":0.0036978773,"about_ca_topic_score_gemma":0.0049405536,"teacher_disagreement_score":0.0036978773,"about_ca_system_score_codex":0.0011834308,"about_ca_system_score_gemma":0.00097617315,"threshold_uncertainty_score":0.008586466},"labels":[],"label_agreement":null},{"id":"W2988556478","doi":"10.1109/atsip49331.2020.9231549","title":"AI-based Pilgrim Detection using Convolutional Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Pilgrimage; Convolutional neural network; Pilgrim; Computer science; Extractor; Artificial intelligence; Convolution (computer science); Artificial neural network; Feature (linguistics); Deep learning; Pattern recognition (psychology); Geography; Engineering; Archaeology","score_opus":0.07850833550363649,"score_gpt":0.328214178162918,"score_spread":0.24970584265928147,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2988556478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42723694,0.0027977994,0.5334114,0.0008340321,0.00039595916,0.00022794797,0.0022682175,0.015740415,0.017087333],"genre_scores_gemma":[0.883504,0.0005381504,0.10067317,0.00029304583,0.00008031952,0.00008441635,0.0028397543,0.0001206452,0.011866438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981135,0.000015765829,0.000007474644,0.00006206394,0.000040470903,0.000062778345],"domain_scores_gemma":[0.99980015,0.000048708054,0.00003052645,0.000033575223,0.00006904632,0.000018014127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020634495,0.0010370525,0.0004465811,0.0010894705,0.00034151363,0.0005216338,0.0012543807,0.0007657544,0.0017750256],"category_scores_gemma":[0.00061846606,0.00028460287,0.00048621648,0.0007223464,0.00024313896,0.0005814695,0.000535054,0.00057680794,0.0009742271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058175006,0.00040610167,0.010960767,0.00017685242,0.00022299221,0.00036043007,0.00009320384,0.16180804,0.046254877,0.0014696489,0.014965772,0.76269954],"study_design_scores_gemma":[0.000006190281,0.00004369209,0.003312372,0.000012500646,0.000022643042,0.00006732961,0.000020188521,0.9865942,0.008201068,0.0005882248,0.0011208238,0.000010734801],"about_ca_topic_score_codex":0.017416304,"about_ca_topic_score_gemma":0.022225687,"teacher_disagreement_score":0.017416304,"about_ca_system_score_codex":0.00073022256,"about_ca_system_score_gemma":0.0005131931,"threshold_uncertainty_score":0.03462982},"labels":[],"label_agreement":null},{"id":"W2991085797","doi":"10.1609/aaai.v34i04.5777","title":"Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking","year":2020,"lang":"en","type":"preprint","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Samsung","keywords":"Computer science; Scalability; Artificial intelligence; Object (grammar); Architecture; Computer vision; Invariant (physics); Video tracking; Tracking (education); Process (computing); Computation; Track (disk drive); Cognitive neuroscience of visual object recognition; Geography","score_opus":0.20920677760534342,"score_gpt":0.3441807439909045,"score_spread":0.13497396638556106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991085797","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":[0.03088694,0.00020503839,0.9646135,0.000092482405,0.000034579843,0.000025458117,0.00007109778,0.0023687386,0.0017022305],"genre_scores_gemma":[0.6553282,0.00031619155,0.33857173,0.00023806351,0.00006329188,0.00008277332,0.0005688772,0.00021238787,0.0046184333],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997019,0.00003380878,0.000014262353,0.00011624712,0.00007756544,0.00005606171],"domain_scores_gemma":[0.999501,0.00014584705,0.00006330992,0.00017583456,0.00007845694,0.00003564265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006048667,0.0005379059,0.00062360195,0.0005941118,0.0003470479,0.0005745071,0.0017675456,0.00066165614,0.0016087712],"category_scores_gemma":[0.0014322856,0.00035482278,0.0005982601,0.0008819742,0.0005268001,0.0011625194,0.0011655127,0.0008314363,0.0006201634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021475703,0.00019271085,0.0016259437,0.0000878914,0.00011236872,0.0001223885,0.00012483835,0.3986128,0.094043456,0.018000733,0.004804739,0.48205736],"study_design_scores_gemma":[0.0000051327993,0.00002344171,0.00031166343,0.0000020780117,0.000008668537,0.00002769219,0.0000053248923,0.98903507,0.005683966,0.0042418563,0.0006496151,0.0000054750985],"about_ca_topic_score_codex":0.008430123,"about_ca_topic_score_gemma":0.011391904,"teacher_disagreement_score":0.008430123,"about_ca_system_score_codex":0.0007698294,"about_ca_system_score_gemma":0.0009363359,"threshold_uncertainty_score":0.016762137},"labels":[],"label_agreement":null},{"id":"W2991539852","doi":"10.1007/978-3-030-50347-5_13","title":"Color Inference from Semantic Labeling for Person Search in Videos","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Inference; Segmentation; Perception; Focus (optics); Set (abstract data type); Context (archaeology); Semantic search; Information retrieval; Pattern recognition (psychology); Psychology; Semantic Web","score_opus":0.08330024808562296,"score_gpt":0.3547376528803481,"score_spread":0.2714374047947251,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991539852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05240162,0.0008912102,0.9344531,0.00026984987,0.00011793749,0.00010784565,0.0015884162,0.0072900895,0.0028799418],"genre_scores_gemma":[0.61884445,0.00061495695,0.3666936,0.0003684181,0.00023509354,0.000117531585,0.0061568585,0.001018228,0.0059508802],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989502,0.0001572224,0.00003252073,0.0004562176,0.00017806239,0.00022590334],"domain_scores_gemma":[0.99890435,0.00042267452,0.0000685522,0.0002800163,0.00022981095,0.000094501316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010106618,0.0012855785,0.0024618462,0.0028907107,0.000951517,0.0016963684,0.002319571,0.002159623,0.004869959],"category_scores_gemma":[0.0034050357,0.00080286287,0.0017199657,0.0026234551,0.0008113721,0.002653794,0.0015393127,0.0017257361,0.002223042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002239114,0.00077844755,0.007694539,0.0003814657,0.00034879197,0.0002486953,0.00021056873,0.10101989,0.04869169,0.017615408,0.023574106,0.7971973],"study_design_scores_gemma":[0.000033389184,0.000058731097,0.0012172495,0.000020916408,0.00006125395,0.000070018345,0.000050038518,0.96945626,0.008629382,0.018917935,0.0014666086,0.000018269036],"about_ca_topic_score_codex":0.026011871,"about_ca_topic_score_gemma":0.03183216,"teacher_disagreement_score":0.026011871,"about_ca_system_score_codex":0.0013315057,"about_ca_system_score_gemma":0.0014825925,"threshold_uncertainty_score":0.051720977},"labels":[],"label_agreement":null},{"id":"W2991796308","doi":"10.18280/ria.330407","title":"Compact Hardware of Running Gaussian Average Algorithm for Moving Object Detection Realized on FPGA and ASIC","year":2019,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Field-programmable gate array; Application-specific integrated circuit; Computer science; Gaussian; Computer hardware; FPGA prototype; Embedded system; Object (grammar); Artificial intelligence; Physics","score_opus":0.03851916740797801,"score_gpt":0.3026671273518354,"score_spread":0.26414795994385737,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2991796308","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086120814,0.0016751466,0.89040613,0.00018899611,0.0002167037,0.00015632239,0.00016896371,0.00782832,0.013238604],"genre_scores_gemma":[0.6029768,0.00058985845,0.38848576,0.00018452457,0.00007425671,0.00012810309,0.00031373545,0.00011609419,0.0071309907],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972194,0.00003557218,0.000014646526,0.000067916306,0.00011439654,0.000045535668],"domain_scores_gemma":[0.9998293,0.000036850968,0.000023358249,0.000029155013,0.00006870552,0.000012718129],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021327926,0.00062414806,0.00038617404,0.00067803403,0.0002466029,0.0006032858,0.0013841703,0.00029431022,0.0035101788],"category_scores_gemma":[0.0003094604,0.0001976479,0.00022707388,0.0005757926,0.00015820102,0.0006435556,0.00020779372,0.00031280544,0.00070438953],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008032882,0.00021537543,0.0027418265,0.00057171524,0.00016736801,0.00047638253,0.00015775216,0.023038732,0.33735543,0.015512985,0.010589879,0.6083693],"study_design_scores_gemma":[0.00032507197,0.0025107856,0.00722766,0.000112506816,0.00035882517,0.0029945546,0.00012452003,0.43501192,0.47587946,0.00306575,0.07227702,0.000111881105],"about_ca_topic_score_codex":0.001730919,"about_ca_topic_score_gemma":0.002082484,"teacher_disagreement_score":0.0035101788,"about_ca_system_score_codex":0.00044701513,"about_ca_system_score_gemma":0.0005439322,"threshold_uncertainty_score":0.011742711},"labels":[],"label_agreement":null},{"id":"W2995552133","doi":"10.1016/j.jvcir.2019.102733","title":"A novel change-detection scheduler for a network of depth sensors","year":2019,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Real-time computing; Change detection; Wireless sensor network; Reliability (semiconductor); Frame (networking); Artificial intelligence; Noise (video); Outlier; Visual sensor network; Computer vision; Power (physics); Key distribution in wireless sensor networks","score_opus":0.07363112622527142,"score_gpt":0.39869360313157814,"score_spread":0.32506247690630674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995552133","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044300124,0.0008889665,0.94246835,0.00028035307,0.0004942312,0.0003751138,0.0003214018,0.009137041,0.0017344292],"genre_scores_gemma":[0.5255717,0.00035319396,0.4674323,0.00031295398,0.00029745602,0.000358757,0.00058426365,0.00030637687,0.0047830185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990067,0.00009907436,0.00008999607,0.00035279186,0.00030035246,0.00015113821],"domain_scores_gemma":[0.9980046,0.0006011187,0.00014321296,0.0003553135,0.0006107009,0.00028502877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012965482,0.0010481522,0.0017971908,0.0011966657,0.0014508117,0.00188633,0.0038964213,0.00081153447,0.005537089],"category_scores_gemma":[0.0035696353,0.0006662237,0.0004171072,0.0013207796,0.00047784296,0.0023000292,0.0017967639,0.0010954984,0.0011675952],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004520397,0.0014325344,0.008594896,0.00041964566,0.00037285668,0.00044307904,0.00035450887,0.16020435,0.10334407,0.017416574,0.026337948,0.6765592],"study_design_scores_gemma":[0.00016513705,0.0001867718,0.00065264595,0.000006761198,0.00005723799,0.000081245526,0.000035548725,0.9814571,0.0112424735,0.0025161938,0.003566697,0.00003218139],"about_ca_topic_score_codex":0.01160784,"about_ca_topic_score_gemma":0.014621612,"teacher_disagreement_score":0.01160784,"about_ca_system_score_codex":0.0016205527,"about_ca_system_score_gemma":0.00419581,"threshold_uncertainty_score":0.023080528},"labels":[],"label_agreement":null},{"id":"W2995997760","doi":"10.5121/csit.2019.91712","title":"A New Hybrid Descriptor Based on Spatiogram and Region Covariance Descriptor","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Covariance; Computer science; Pattern recognition (psychology); Artificial intelligence; Covariance intersection; Algorithm; Covariance matrix; Mathematics; Covariance function; Statistics","score_opus":0.021002007329364664,"score_gpt":0.24092296760178167,"score_spread":0.219920960272417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2995997760","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":[0.027927805,0.0018028958,0.96437997,0.00017131273,0.00035802278,0.0001382069,0.0013246073,0.0012329095,0.0026643167],"genre_scores_gemma":[0.481965,0.0026743822,0.49057594,0.00057850726,0.00060258387,0.000499048,0.009421887,0.00040296983,0.013279712],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990376,0.00010344539,0.00006651674,0.00020028814,0.0004989412,0.00009319497],"domain_scores_gemma":[0.9993393,0.00010253956,0.000090318936,0.00011840653,0.00030660458,0.00004275048],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056429993,0.00055749656,0.0013740918,0.0030425193,0.00024197706,0.0011218549,0.001174698,0.0006747266,0.0022937874],"category_scores_gemma":[0.0011521906,0.00019564111,0.0009952798,0.0037486034,0.00046438898,0.0023995636,0.0008903879,0.00062487874,0.0012120457],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005621617,0.00021524586,0.005964785,0.0003680651,0.00022003464,0.00028942543,0.000078853926,0.024378937,0.073277935,0.02321165,0.022289958,0.84914297],"study_design_scores_gemma":[0.000093779534,0.0005903537,0.0150162,0.00007135391,0.00022936615,0.0023429897,0.00017999773,0.8692576,0.047142033,0.010553816,0.054321975,0.0002005463],"about_ca_topic_score_codex":0.0035781292,"about_ca_topic_score_gemma":0.002937244,"teacher_disagreement_score":0.0035781292,"about_ca_system_score_codex":0.00065734255,"about_ca_system_score_gemma":0.00084709947,"threshold_uncertainty_score":0.007673502},"labels":[],"label_agreement":null},{"id":"W2997593239","doi":"10.1609/aaai.v34i04.5777","title":"Exploiting Spatial Invariance for Scalable Unsupervised Object Tracking","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"Samsung","keywords":"Computer science; Artificial intelligence; Scalability; Object (grammar); Computer vision; Video tracking; Architecture; Tracking (education); Invariant (physics); Cognitive neuroscience of visual object recognition; Process (computing); Computation; Track (disk drive); Object detection; Task (project management); Pattern recognition (psychology)","score_opus":0.08860429258946417,"score_gpt":0.29513565658334945,"score_spread":0.20653136399388528,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997593239","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031013642,0.00020395328,0.9646024,0.00008377909,0.0000325828,0.000025433656,0.00006949201,0.0022568877,0.0017118957],"genre_scores_gemma":[0.6694199,0.00029944172,0.32459277,0.00021846015,0.000059022866,0.00007491985,0.0005500133,0.00019743382,0.0045880713],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971825,0.0000314655,0.000013424462,0.00010918607,0.0000750168,0.000052596097],"domain_scores_gemma":[0.99956614,0.00012335977,0.000057023495,0.00014977783,0.000071216724,0.00003242817],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056884094,0.0005340997,0.00060121703,0.000583517,0.00032788332,0.00054108765,0.0016749941,0.0006041943,0.0015706022],"category_scores_gemma":[0.0013039266,0.00033273493,0.0005699318,0.00083441695,0.00050095125,0.0010870569,0.0010822538,0.0007847643,0.00060094893],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021076894,0.00018928383,0.0016736474,0.00008315881,0.00011538522,0.0001198762,0.00012109278,0.39245927,0.09168755,0.016434021,0.004574531,0.4923315],"study_design_scores_gemma":[0.0000047555195,0.00002450514,0.00033084335,0.0000020740702,0.000009138049,0.000028483662,0.0000053711215,0.9896947,0.0056125387,0.00361741,0.00066465646,0.000005474739],"about_ca_topic_score_codex":0.008364799,"about_ca_topic_score_gemma":0.0124890935,"teacher_disagreement_score":0.008364799,"about_ca_system_score_codex":0.0007352518,"about_ca_system_score_gemma":0.00090101437,"threshold_uncertainty_score":0.0166322},"labels":[],"label_agreement":null},{"id":"W2997749801","doi":"10.1016/j.image.2019.115764","title":"Convolutional neural networks for multispectral pedestrian detection","year":2020,"lang":"en","type":"article","venue":"Signal Processing Image Communication","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Multispectral image; Pedestrian detection; Computer science; Convolutional neural network; Artificial intelligence; Benchmark (surveying); Fuse (electrical); Pedestrian; Computer vision; Pattern recognition (psychology); Cartography; Geography; Engineering","score_opus":0.0503846043613038,"score_gpt":0.3131087830973598,"score_spread":0.26272417873605597,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2997749801","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07330942,0.0032883731,0.9147042,0.00048598775,0.00024192524,0.000048194805,0.00071038207,0.002450137,0.0047613448],"genre_scores_gemma":[0.7480905,0.0019362617,0.22169513,0.00026696696,0.00017579932,0.00005995514,0.001492272,0.00019160211,0.026091577],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997825,0.000031470947,0.00000906123,0.00006886085,0.000057163044,0.00005094034],"domain_scores_gemma":[0.999637,0.000115313436,0.000042534142,0.00006054246,0.00012539738,0.000019162026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047396385,0.00071333634,0.0005356097,0.00082411576,0.0002980096,0.000582166,0.00083435775,0.00089180586,0.002649045],"category_scores_gemma":[0.0011396908,0.0004502939,0.00047832486,0.00093274354,0.00026279723,0.0006778396,0.0006603722,0.0007897452,0.0010384485],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003166289,0.00016403025,0.0020651543,0.00014781707,0.00014265433,0.00011271227,0.000041444757,0.23738165,0.028187657,0.0079912655,0.009100625,0.7143484],"study_design_scores_gemma":[0.0000022364882,0.000011859545,0.0006246932,0.000006700535,0.000014333625,0.00001979648,0.000003948421,0.99300295,0.0036750284,0.0017823996,0.0008519152,0.0000040604145],"about_ca_topic_score_codex":0.015581161,"about_ca_topic_score_gemma":0.019947713,"teacher_disagreement_score":0.015581161,"about_ca_system_score_codex":0.00076848443,"about_ca_system_score_gemma":0.00058833393,"threshold_uncertainty_score":0.030980945},"labels":[],"label_agreement":null},{"id":"W2999382916","doi":"10.1155/2020/9194028","title":"A Vision-Based Video Crash Detection Framework for Mixed Traffic Flow Environment Considering Low-Visibility Condition","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":69,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Visibility; Crash; Computer science; Minimum bounding box; Artificial intelligence; Traffic flow (computer networking); Computer vision; Object detection; Rollover (web design); Constant false alarm rate; Set (abstract data type); Simulation; Image (mathematics); Pattern recognition (psychology); Computer security","score_opus":0.016479587155620232,"score_gpt":0.28611888960379694,"score_spread":0.2696393024481767,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W2999382916","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030994887,0.0004748041,0.965901,0.0000914548,0.0000544248,0.00009822275,0.000104570136,0.0013609166,0.00091972016],"genre_scores_gemma":[0.73590755,0.0006912633,0.25983217,0.00014482082,0.000087073626,0.00017063868,0.00050992006,0.00006970289,0.002586764],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975413,0.000022128439,0.000009566352,0.00009523993,0.000063076965,0.0000558905],"domain_scores_gemma":[0.9997849,0.000026623205,0.000026123409,0.000017976921,0.00011962102,0.000024806564],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035867168,0.0008709855,0.00071336725,0.0010205416,0.00032214736,0.0006701372,0.0012270877,0.0006629545,0.00082279753],"category_scores_gemma":[0.00063302857,0.00030011692,0.00072471524,0.00036017227,0.0002551677,0.0008487251,0.0005466239,0.00076697144,0.0003497589],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035729128,0.00036458863,0.010256287,0.0003099213,0.00020008381,0.00057143276,0.00023259022,0.40068027,0.06505002,0.0066474364,0.00467568,0.5106544],"study_design_scores_gemma":[0.0000060225275,0.00008258858,0.0013016714,0.000008119652,0.000030192004,0.00008653178,0.000027305925,0.99289083,0.0041800044,0.00057537254,0.00079689716,0.000014535575],"about_ca_topic_score_codex":0.020439783,"about_ca_topic_score_gemma":0.014928205,"teacher_disagreement_score":0.020439783,"about_ca_system_score_codex":0.00051640393,"about_ca_system_score_gemma":0.0012919435,"threshold_uncertainty_score":0.040641606},"labels":[],"label_agreement":null},{"id":"W3000529389","doi":"10.1109/wacv45572.2020.9093606","title":"Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Adaptability; Artificial intelligence; Cluster analysis; Machine learning; Identification (biology); Robustness (evolution); Scalability; Annotation; Unsupervised learning; Benchmark (surveying); Adaptation (eye); Domain (mathematical analysis); Pattern recognition (psychology)","score_opus":0.04602381814675512,"score_gpt":0.2594556655881834,"score_spread":0.21343184744142826,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3000529389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.015501747,0.00019798618,0.98191226,0.00007114165,0.000059618033,0.000043067488,0.00009955693,0.0011637926,0.00095071574],"genre_scores_gemma":[0.32834587,0.0004224704,0.6624463,0.00032106077,0.000111100526,0.00015418163,0.0015536281,0.00046674407,0.006178642],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99847573,0.00033781774,0.000049595747,0.000651677,0.00030800822,0.00017706446],"domain_scores_gemma":[0.9988644,0.00021681383,0.00011192511,0.0004573005,0.00027702126,0.0000725669],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001031963,0.00103293,0.001278864,0.0012826609,0.0006328965,0.0009204412,0.0020438246,0.0011735259,0.0011091866],"category_scores_gemma":[0.002965361,0.0004239387,0.0012915966,0.0016199773,0.00071034976,0.0013387314,0.0017855045,0.0015135623,0.0020076528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004391499,0.0003478034,0.003435459,0.00017896702,0.0002100119,0.000371515,0.00063029653,0.24651353,0.0525726,0.0067011267,0.01177055,0.676829],"study_design_scores_gemma":[0.000011358153,0.00004674788,0.0012900704,0.000011255327,0.00002066605,0.00025662244,0.00013088266,0.9762349,0.014130651,0.004261719,0.0035715313,0.000033578173],"about_ca_topic_score_codex":0.0049030087,"about_ca_topic_score_gemma":0.0070098937,"teacher_disagreement_score":0.0049030087,"about_ca_system_score_codex":0.0005814188,"about_ca_system_score_gemma":0.00070948614,"threshold_uncertainty_score":0.009748936},"labels":[],"label_agreement":null},{"id":"W3003581603","doi":"10.1109/iccvw.2019.00281","title":"Fast Visual Object Tracking using Ellipse Fitting for Rotated Bounding Boxes","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Ellipse; Minimum bounding box; Artificial intelligence; Computer vision; Computer science; Bounding overwatch; Tracking (education); Rotation (mathematics); Frame rate; Object (grammar); Video tracking; Frame (networking); Segmentation; Eye tracking; Image (mathematics); Mathematics; Geometry","score_opus":0.04074596289829786,"score_gpt":0.3454373993911114,"score_spread":0.30469143649281355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003581603","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":[0.0055488506,0.00028806704,0.9875053,0.000038987644,0.000045134337,0.000038137925,0.00009634629,0.005763402,0.000675776],"genre_scores_gemma":[0.098780885,0.0003374005,0.8963566,0.000102847676,0.000041618427,0.00010339171,0.0009095595,0.0011616913,0.0022060894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976477,0.0003058402,0.00012119059,0.0009370941,0.0008169408,0.00017127849],"domain_scores_gemma":[0.99764377,0.0007171678,0.00026701533,0.00063686934,0.0006422767,0.00009279871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001873218,0.0017256817,0.0018292129,0.0021624218,0.0007336627,0.0018685643,0.0020077464,0.0015706762,0.0033963977],"category_scores_gemma":[0.006260488,0.0011022977,0.0013438194,0.0017230478,0.00065794704,0.0022448837,0.002280234,0.0015405876,0.003527003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033055188,0.000104043276,0.002586897,0.00018884738,0.00018270021,0.0001596098,0.00025550462,0.11141635,0.0769072,0.006209032,0.007966384,0.7936928],"study_design_scores_gemma":[0.000017449021,0.000040960786,0.0011041313,0.000027508086,0.000021192276,0.0002653301,0.00003113438,0.955625,0.03241973,0.0027167664,0.007687688,0.00004313595],"about_ca_topic_score_codex":0.006397571,"about_ca_topic_score_gemma":0.0063135237,"teacher_disagreement_score":0.006397571,"about_ca_system_score_codex":0.0008669718,"about_ca_system_score_gemma":0.001273172,"threshold_uncertainty_score":0.0127206445},"labels":[],"label_agreement":null},{"id":"W3003950743","doi":"10.48550/arxiv.2002.00264","title":"Few-Shot Scene Adaptive Crowd Counting Using Meta-Learning","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nvidia","keywords":"Computer science; Artificial intelligence; Shot (pellet); Computer vision; Adaptation (eye); One shot; Code (set theory); Context (archaeology)","score_opus":0.37130965750172656,"score_gpt":0.25858745180836795,"score_spread":0.11272220569335861,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3003950743","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":[0.022051692,0.0004953123,0.9731863,0.00030114237,0.00015217059,0.00011917574,0.00018957796,0.0017580789,0.0017465015],"genre_scores_gemma":[0.58482456,0.00055815355,0.40447047,0.00079662696,0.00042518813,0.0003284188,0.0014520952,0.0006399485,0.00650452],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986125,0.00029949786,0.000054606942,0.0006266096,0.00022598942,0.00018079551],"domain_scores_gemma":[0.99767023,0.001004809,0.00030898908,0.00040927294,0.0003601925,0.00024639265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022561227,0.0022090233,0.002841699,0.001917176,0.00093308126,0.0017374097,0.005198038,0.0025273135,0.0025224881],"category_scores_gemma":[0.006579233,0.0012373144,0.0016455263,0.0014969996,0.0015313771,0.0029741533,0.0028954302,0.0023285886,0.0012536579],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034927917,0.00032461173,0.0026445016,0.0002470332,0.00025792525,0.00023231888,0.00026817783,0.7620972,0.0063237515,0.0066358256,0.0055244653,0.2150949],"study_design_scores_gemma":[0.0000072119838,0.00002790811,0.00015962696,0.00001293682,0.00001262283,0.000038224378,0.00001904268,0.9939711,0.00090597157,0.0044096382,0.00042431452,0.000011530687],"about_ca_topic_score_codex":0.006097767,"about_ca_topic_score_gemma":0.0066589573,"teacher_disagreement_score":0.006097767,"about_ca_system_score_codex":0.0014242695,"about_ca_system_score_gemma":0.0013830938,"threshold_uncertainty_score":0.012124538},"labels":[],"label_agreement":null},{"id":"W3004367836","doi":"10.1142/s0218001420500330","title":"Towards Wide Range Tracking of Head Scanning Movement in Driving","year":2020,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Eye Institute; National Institutes of Health","keywords":"Orb (optics); Artificial intelligence; Computer vision; Computer science; Tracking (education); Head (geology); Simultaneous localization and mapping; Deep learning; Feature (linguistics); Metric (unit); Mobile robot; Engineering; Image (mathematics); Robot","score_opus":0.16212861196578218,"score_gpt":0.3625894182911271,"score_spread":0.20046080632534494,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004367836","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.292665,0.0005905043,0.70036715,0.00016616553,0.000106267165,0.00006705587,0.00030141507,0.002699038,0.0030373507],"genre_scores_gemma":[0.8962432,0.00019953432,0.101419054,0.000074756324,0.00003066554,0.000034909375,0.00038151568,0.000092868395,0.0015235615],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99972874,0.000042328422,0.000008736938,0.00008235456,0.00008803805,0.000049843973],"domain_scores_gemma":[0.99963236,0.00006440534,0.000048157854,0.000067169494,0.00015703356,0.000030895568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045416201,0.00061351253,0.00035981878,0.0008147256,0.00016259681,0.00042587463,0.0005343021,0.00033249616,0.00068910146],"category_scores_gemma":[0.0014963498,0.00028963102,0.00026369927,0.0005113766,0.0002668829,0.0005292686,0.00079318666,0.00054605264,0.000517288],"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":[0.00045890576,0.0001574307,0.013763796,0.0001452817,0.00010098932,0.00015994144,0.00040439205,0.123455115,0.13597243,0.0013906725,0.0037531587,0.7202379],"study_design_scores_gemma":[0.000014747111,0.00018494614,0.012947635,0.000021564741,0.000021858847,0.00022611588,0.00014199976,0.95333236,0.029051935,0.0015786684,0.0024490252,0.000029140889],"about_ca_topic_score_codex":0.005079683,"about_ca_topic_score_gemma":0.0064464007,"teacher_disagreement_score":0.005079683,"about_ca_system_score_codex":0.00020228888,"about_ca_system_score_gemma":0.00049814326,"threshold_uncertainty_score":0.0101002455},"labels":[],"label_agreement":null},{"id":"W3004460637","doi":"10.1016/j.knosys.2020.105594","title":"CNN tracking based on data augmentation","year":2020,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"State Key Laboratory of Robotics and System; National Natural Science Foundation of China; Deutsche Forschungsgemeinschaft","keywords":"Computer science; Robustness (evolution); Artificial intelligence; BitTorrent tracker; Convolutional neural network; Video tracking; Hash function; Benchmark (surveying); Pattern recognition (psychology); Filter (signal processing); Computer vision; Eye tracking; Exploit; Tracking (education); ENCODE; Object (grammar)","score_opus":0.17186710604954095,"score_gpt":0.3530485955476173,"score_spread":0.18118148949807636,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3004460637","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.030068763,0.00073114777,0.95932627,0.00023784657,0.00030643796,0.00008748528,0.00047182414,0.0041363616,0.004633776],"genre_scores_gemma":[0.5736316,0.0009824606,0.4094762,0.0004568609,0.00018810984,0.00015439207,0.0024818755,0.00029125437,0.012337239],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994789,0.000042945,0.000021761101,0.00022779388,0.00014370246,0.000084863896],"domain_scores_gemma":[0.9992494,0.00015839448,0.00006601888,0.00022398401,0.00026294016,0.0000393359],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007759687,0.0011233403,0.00110117,0.0010048547,0.00045990676,0.0011491785,0.0014947562,0.0011083779,0.0022490164],"category_scores_gemma":[0.0018188258,0.0006088704,0.00088318175,0.0013610726,0.00047848007,0.0012643895,0.001403939,0.0012853972,0.0014598478],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032705473,0.00017069805,0.0022802234,0.0001210001,0.00014593144,0.00013974204,0.000073623225,0.11303279,0.042600885,0.003981545,0.008063336,0.8290632],"study_design_scores_gemma":[0.0000050787735,0.00002926861,0.00078107335,0.000012444521,0.000028592185,0.000050924195,0.000006181577,0.9843739,0.011295237,0.0015453568,0.0018626204,0.000009313364],"about_ca_topic_score_codex":0.014896733,"about_ca_topic_score_gemma":0.015773764,"teacher_disagreement_score":0.014896733,"about_ca_system_score_codex":0.000864843,"about_ca_system_score_gemma":0.001174396,"threshold_uncertainty_score":0.029620051},"labels":[],"label_agreement":null},{"id":"W3005651405","doi":"10.1109/crv50864.2020.00038","title":"SpotNet: Self-Attention Multi-Task Network for Object Detection","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Genetec (Canada); Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Segmentation; Computer science; Bounding overwatch; Artificial intelligence; Background subtraction; Task (project management); Object detection; Feature (linguistics); Object (grammar); Computer vision; Pattern recognition (psychology); Machine learning; Pixel","score_opus":0.04839782180162159,"score_gpt":0.31422849677369014,"score_spread":0.2658306749720685,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005651405","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":[0.05995756,0.0018365366,0.88578844,0.00095322885,0.00042221328,0.00029941293,0.003629178,0.040877543,0.0062359674],"genre_scores_gemma":[0.53881377,0.0006868528,0.42195067,0.0012187128,0.00038173757,0.0008070925,0.016863553,0.0015729903,0.017704684],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994746,0.000106979845,0.000018864956,0.00021684245,0.000099633755,0.00008316563],"domain_scores_gemma":[0.9991053,0.00030231333,0.000082395956,0.00023281941,0.00021049616,0.00006668613],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010975673,0.002272671,0.0009866975,0.0011787276,0.00054977834,0.00091633335,0.003146839,0.0021071907,0.0047381744],"category_scores_gemma":[0.0027823932,0.00080004625,0.0008069463,0.0011042546,0.0006224399,0.0026713135,0.0015059371,0.0018632042,0.0023075633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011639534,0.0008019625,0.002725951,0.0004533935,0.0004327999,0.00034843356,0.00015529209,0.38569346,0.018044304,0.0070410995,0.08754239,0.49559703],"study_design_scores_gemma":[0.000028073664,0.000053137046,0.0003460105,0.0000073534275,0.000012536499,0.000031203388,0.000009358927,0.99038106,0.0029649637,0.0043781754,0.0017789239,0.000009209341],"about_ca_topic_score_codex":0.011492581,"about_ca_topic_score_gemma":0.018044308,"teacher_disagreement_score":0.011492581,"about_ca_system_score_codex":0.001330211,"about_ca_system_score_gemma":0.0009801708,"threshold_uncertainty_score":0.022851348},"labels":[],"label_agreement":null},{"id":"W3005758259","doi":"10.1109/ccwc51732.2021.9376083","title":"Analysis of Distracted Driver Behaviour Using Self-Organizing Maps","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Distraction; Computer science; Artificial intelligence; Human–computer interaction; Cognitive psychology; Psychology","score_opus":0.02974038283754765,"score_gpt":0.30065357673628706,"score_spread":0.2709131938987394,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3005758259","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":[0.754992,0.00038821663,0.23918977,0.00016376555,0.00008043364,0.00016787583,0.0011885179,0.0014507613,0.002378629],"genre_scores_gemma":[0.95912963,0.00010159325,0.039089978,0.000016191763,0.000014915356,0.00006636135,0.00084837514,0.000039119415,0.00069387426],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995455,0.00009853691,0.00002760474,0.00011120298,0.00014756763,0.00006957776],"domain_scores_gemma":[0.9989396,0.0004882984,0.00012231804,0.00009075812,0.00029764685,0.0000614583],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00060116086,0.000506948,0.00032935358,0.0025032642,0.0003370505,0.0007876397,0.00047414872,0.00036426404,0.0006395799],"category_scores_gemma":[0.0024264199,0.00017856374,0.0005364597,0.00134281,0.00023553005,0.0006261515,0.00040741538,0.00032536013,0.00025229232],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012454486,0.0011256773,0.13840236,0.00053213275,0.0006131964,0.0006923769,0.0029247631,0.29103625,0.04132304,0.0033520665,0.0049230056,0.5138297],"study_design_scores_gemma":[0.0000130400385,0.00015724753,0.07121233,0.000021681717,0.000045218065,0.0001776837,0.0009711226,0.91293466,0.00976673,0.0028674945,0.0017789869,0.00005376865],"about_ca_topic_score_codex":0.00694178,"about_ca_topic_score_gemma":0.006992778,"teacher_disagreement_score":0.00694178,"about_ca_system_score_codex":0.00036821543,"about_ca_system_score_gemma":0.00045700325,"threshold_uncertainty_score":0.013802707},"labels":[],"label_agreement":null},{"id":"W3009235338","doi":"10.3390/s20051412","title":"Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Track (disk drive); Track geometry; Association (psychology); Computer science; Maximization; Probabilistic logic; Expectation–maximization algorithm; Tracking (education); Gaussian; Constraint (computer-aided design); Computer vision; Artificial intelligence; Algorithm; Geometry; Mathematics; Mathematical optimization; Maximum likelihood; Statistics","score_opus":0.03538670272402877,"score_gpt":0.29538072164245577,"score_spread":0.259994018918427,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3009235338","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021565307,0.000256483,0.9770453,0.00006722534,0.0000437135,0.000024197843,0.000047865098,0.0003896097,0.0005602616],"genre_scores_gemma":[0.65214723,0.00044764578,0.34409612,0.00012111815,0.00010348341,0.000093960865,0.0006121525,0.00015372351,0.0022245606],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988681,0.00021467198,0.000051276576,0.00039384465,0.00034101564,0.00013101629],"domain_scores_gemma":[0.99781775,0.000691974,0.0004562192,0.00050621526,0.00040059705,0.00012725953],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014662552,0.0007942724,0.0012466434,0.0012883702,0.0008820939,0.0011291907,0.0020538336,0.001149236,0.00092009833],"category_scores_gemma":[0.0059520793,0.0005699334,0.00076544733,0.0023340043,0.0007013171,0.002333912,0.0022580212,0.0011175994,0.0007717101],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003593445,0.0001106354,0.008418946,0.00011179294,0.00015113616,0.00017490303,0.00029343125,0.39301357,0.01087476,0.015099979,0.002834575,0.568557],"study_design_scores_gemma":[0.000017006407,0.00007783038,0.0012955053,0.0000088090355,0.000032660195,0.00015982159,0.00006327294,0.9822662,0.004379607,0.009255968,0.0024222669,0.00002100121],"about_ca_topic_score_codex":0.0039426587,"about_ca_topic_score_gemma":0.0051592467,"teacher_disagreement_score":0.0039426587,"about_ca_system_score_codex":0.00062184694,"about_ca_system_score_gemma":0.001510141,"threshold_uncertainty_score":0.007839441},"labels":[],"label_agreement":null},{"id":"W3011644762","doi":"10.48550/arxiv.2003.04468","title":"Tracking Road Users using Constraint Programming","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","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":"Constraint programming; Constraint (computer-aided design); Computer science; Programming language; Mathematical optimization; Mathematics; Stochastic programming","score_opus":0.20412897302062763,"score_gpt":0.2532485601582698,"score_spread":0.04911958713764217,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011644762","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01099364,0.00046644366,0.98272043,0.00042448431,0.00006364808,0.00011059647,0.0010695946,0.0011372624,0.0030138507],"genre_scores_gemma":[0.25227624,0.00064172514,0.7337863,0.0005544958,0.0001488235,0.0005698669,0.004498729,0.0006148177,0.006909082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979213,0.0006414942,0.00008502297,0.00072345615,0.00044328198,0.00018549625],"domain_scores_gemma":[0.99603003,0.0027303714,0.0002958914,0.00023485285,0.0005522804,0.00015658335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018068271,0.0019257719,0.0016937669,0.0015036623,0.0008458014,0.0026811424,0.003022965,0.0019082087,0.0054493966],"category_scores_gemma":[0.0057002017,0.0012531077,0.0016506967,0.0033919879,0.0009874678,0.0022150846,0.0016073632,0.0022211052,0.0011650653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013071559,0.00010009035,0.0020433122,0.00025432848,0.00013466948,0.00016270038,0.000062919135,0.8741226,0.0011741337,0.010859527,0.008399703,0.10255532],"study_design_scores_gemma":[0.000007826311,0.000008640758,0.00012854469,0.000008736088,0.000007376967,0.00001869122,0.000011938937,0.99481153,0.00032089604,0.003376799,0.0012932641,0.00000568287],"about_ca_topic_score_codex":0.037087906,"about_ca_topic_score_gemma":0.045619525,"teacher_disagreement_score":0.037087906,"about_ca_system_score_codex":0.0014746828,"about_ca_system_score_gemma":0.0028924264,"threshold_uncertainty_score":0.07374406},"labels":[],"label_agreement":null},{"id":"W3011981040","doi":"10.48550/arxiv.2003.08021","title":"Applying r-spatiogram in object tracking for occlusion handling","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer vision; Video tracking; Tracking (education); Computer science; Object (grammar); Feature (linguistics); Active appearance model; BitTorrent tracker; Sequence (biology); Pixel; Pattern recognition (psychology); Feature vector; Image (mathematics); Eye tracking","score_opus":0.14972964801202554,"score_gpt":0.25058517862314506,"score_spread":0.10085553061111951,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3011981040","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018097656,0.00046620585,0.9800459,0.00006617435,0.00002520756,0.000036455418,0.000070677605,0.0008123361,0.00037933874],"genre_scores_gemma":[0.3662485,0.0010120369,0.6287463,0.00019169028,0.00011170952,0.00013702831,0.00069917965,0.0002995135,0.0025541782],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99874276,0.00033177985,0.000081558224,0.00048409312,0.00026003429,0.00009975583],"domain_scores_gemma":[0.9982494,0.0005049321,0.00031975794,0.00058905437,0.00027206226,0.000064852145],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017054514,0.0009150342,0.0013494112,0.0023685778,0.00048005418,0.0011370481,0.0010624217,0.0013645237,0.00097637606],"category_scores_gemma":[0.004191238,0.0004697033,0.0009675421,0.0026813808,0.000849346,0.0017948807,0.0013307331,0.00069788046,0.0009032001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007831845,0.00022364275,0.0059595914,0.0002498918,0.00018445012,0.00048143245,0.00040502532,0.16248475,0.066847704,0.015258393,0.0039716973,0.74315023],"study_design_scores_gemma":[0.000016874847,0.00021897287,0.002911315,0.000026530117,0.00005192443,0.00047991777,0.000060490074,0.96550393,0.018927084,0.0053678146,0.0064062653,0.000028919208],"about_ca_topic_score_codex":0.0025913122,"about_ca_topic_score_gemma":0.0028528152,"teacher_disagreement_score":0.0025913122,"about_ca_system_score_codex":0.0007510154,"about_ca_system_score_gemma":0.00076146,"threshold_uncertainty_score":0.009019375},"labels":[],"label_agreement":null},{"id":"W3012053907","doi":"10.23919/fusion43075.2019.9011161","title":"Context-Enhanced Vehicle Tracking Method Under the Connected Environment (Poster)","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Context (archaeology); Clutter; Dynamic Bayesian network; Tracking (education); Vehicle tracking system; Key (lock); Vehicle dynamics; Artificial intelligence; Bayesian network; Video tracking; Bayesian probability; Object (grammar); Computer vision; Context model; Real-time computing; Kalman filter; Engineering; Radar; Computer security; Automotive engineering","score_opus":0.02714427764906146,"score_gpt":0.2870343884894719,"score_spread":0.2598901108404104,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3012053907","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":[0.023944462,0.00047579303,0.9727956,0.00007965951,0.000100517726,0.000048189482,0.00007108101,0.00039971847,0.0020850482],"genre_scores_gemma":[0.63999236,0.0007855881,0.35324812,0.00015788425,0.0001507932,0.00010443776,0.00032055797,0.0000942361,0.005145973],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969697,0.00003981118,0.000013758349,0.00011702874,0.000090030946,0.000042536452],"domain_scores_gemma":[0.99982363,0.0000339421,0.000018292656,0.000020061985,0.00008546953,0.000018683784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002933818,0.0005205659,0.00053300505,0.00067488354,0.0004322131,0.00054399105,0.0007984153,0.0006988998,0.0015406001],"category_scores_gemma":[0.0009459592,0.00028365257,0.0006080336,0.00055406283,0.00021189911,0.00077644206,0.0007509116,0.0005381251,0.00039940455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033171027,0.000100197874,0.004671178,0.00017008641,0.00013002566,0.000429455,0.0002689517,0.3240947,0.032243546,0.012733311,0.0034008734,0.621426],"study_design_scores_gemma":[0.000013928249,0.000043399712,0.0007350024,0.000010236136,0.000027022485,0.0001318673,0.00002742861,0.9922157,0.0035110163,0.0016530713,0.001616196,0.000015114934],"about_ca_topic_score_codex":0.008168919,"about_ca_topic_score_gemma":0.0073014023,"teacher_disagreement_score":0.008168919,"about_ca_system_score_codex":0.00032085602,"about_ca_system_score_gemma":0.00072616397,"threshold_uncertainty_score":0.016242743},"labels":[],"label_agreement":null},{"id":"W3013748178","doi":"10.1007/978-3-030-50347-5_4","title":"Supervised and Unsupervised Detections for Multiple Object Tracking in Traffic Scenes: A Comparative Study","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Unsupervised learning; Tracking (education); Object detection; Object (grammar); Detector; Pattern recognition (psychology); Identification (biology); Ranging; Supervised learning; Computer vision; Machine learning; Artificial neural network","score_opus":0.0853150703390353,"score_gpt":0.35122614615434655,"score_spread":0.26591107581531126,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3013748178","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":[0.6876861,0.0064337985,0.2916695,0.00031620084,0.00019980413,0.00020800349,0.00062305015,0.002058205,0.010805316],"genre_scores_gemma":[0.9423477,0.0017087932,0.051646914,0.00007134076,0.0000897938,0.00004542897,0.0010770526,0.00033336825,0.0026796795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9952335,0.0019082256,0.00018984335,0.00091784477,0.0014139607,0.00033671406],"domain_scores_gemma":[0.9587613,0.03110279,0.0018873923,0.002539092,0.0052513685,0.00045809822],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008932449,0.00095076393,0.0012592102,0.0023998385,0.00078060816,0.0028344146,0.0016730608,0.0017250485,0.0013679373],"category_scores_gemma":[0.029013295,0.0005286978,0.0008942543,0.0016477817,0.0009550274,0.0036614675,0.0010334388,0.0007462864,0.000712883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.004915859,0.001082429,0.07298875,0.0014767464,0.0012659929,0.00032146933,0.001411123,0.07084689,0.0207396,0.003272945,0.0036684969,0.8180097],"study_design_scores_gemma":[0.00014484505,0.0017107414,0.10627684,0.00024761818,0.0016829182,0.0010741211,0.001848577,0.85047936,0.024453446,0.0058051376,0.0061142566,0.00016206785],"about_ca_topic_score_codex":0.0077097486,"about_ca_topic_score_gemma":0.010446969,"teacher_disagreement_score":0.008932449,"about_ca_system_score_codex":0.0009827473,"about_ca_system_score_gemma":0.0012003267,"threshold_uncertainty_score":0.0472399},"labels":[],"label_agreement":null},{"id":"W3015476328","doi":"10.1109/icassp40776.2020.9053919","title":"Facial Emotion Recognition Using Light Field Images with Deep Attention-Based Bidirectional LSTM","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Artificial intelligence; Convolutional neural network; Light field; Deep learning; Viewpoints; Computer vision; Context (archaeology); Focus (optics); Facial recognition system; Field (mathematics); Pattern recognition (psychology); Feature extraction; Feature (linguistics)","score_opus":0.044886172549957686,"score_gpt":0.2787972901296638,"score_spread":0.23391111757970612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015476328","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20527734,0.0014327575,0.7785019,0.0007162892,0.00032855483,0.00014204347,0.0009584959,0.005165372,0.0074772453],"genre_scores_gemma":[0.8818882,0.00052569946,0.10916505,0.00037429447,0.00008443484,0.00008131783,0.0011798175,0.00010980561,0.006591391],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99984586,0.000019805639,0.00000601613,0.000050504575,0.000039629245,0.000038266076],"domain_scores_gemma":[0.99990165,0.000021303307,0.000015568183,0.0000128374395,0.00003804344,0.000010557373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024244146,0.00079848716,0.00044267907,0.00042610528,0.00016428129,0.0004285149,0.0007009526,0.00047319417,0.0019978078],"category_scores_gemma":[0.0004419747,0.00020970186,0.0005952972,0.0002821056,0.00018960891,0.00065967935,0.00054635276,0.00080501585,0.00077246566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006029308,0.00033979994,0.0020166985,0.00012117131,0.00014832817,0.00015788774,0.00012427379,0.07459757,0.12868254,0.0021406158,0.010185268,0.7808829],"study_design_scores_gemma":[0.000012156029,0.00008157379,0.0014924983,0.0000108037775,0.00003777962,0.000047920243,0.000030509353,0.9784153,0.016922789,0.0020454025,0.0008901208,0.0000130678345],"about_ca_topic_score_codex":0.005809744,"about_ca_topic_score_gemma":0.008041643,"teacher_disagreement_score":0.005809744,"about_ca_system_score_codex":0.00061438,"about_ca_system_score_gemma":0.00031864838,"threshold_uncertainty_score":0.011551857},"labels":[],"label_agreement":null},{"id":"W3015861221","doi":"10.1109/icassp40776.2020.9053333","title":"Dynamic Channel Pruning For Correlation Filter Based Object Tracking","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Discriminative model; Pruning; Channel (broadcasting); Computer science; BitTorrent tracker; Frame (networking); Filter (signal processing); Artificial intelligence; Tracking (education); Reliability (semiconductor); Algorithm; Object (grammar); Pattern recognition (psychology); Computer vision; Eye tracking","score_opus":0.05315202864088913,"score_gpt":0.300273561657647,"score_spread":0.24712153301675788,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015861221","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.006500349,0.00025345793,0.99148,0.00006149712,0.000032043794,0.000020430216,0.00006646851,0.0010669068,0.00051896885],"genre_scores_gemma":[0.37604526,0.00071209634,0.6166518,0.00028964074,0.00019540713,0.00023271612,0.0012236835,0.0005303777,0.0041189836],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988637,0.00024438908,0.000051349365,0.00026317753,0.00041404268,0.00016337074],"domain_scores_gemma":[0.9979241,0.0010927232,0.00017712264,0.00026098336,0.00044927074,0.00009571494],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018379514,0.0011040163,0.0014217177,0.0010530158,0.0006474541,0.001168286,0.0013444736,0.0010118348,0.0016683097],"category_scores_gemma":[0.0055542826,0.00062006054,0.00080910453,0.0013516324,0.0007412191,0.0015428102,0.0013157737,0.0016741635,0.00092365796],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000461456,0.00017717687,0.002026185,0.00012885591,0.00011667601,0.00015733541,0.00014289844,0.4452495,0.018913215,0.008895815,0.009661252,0.51406956],"study_design_scores_gemma":[0.00001217548,0.000020208565,0.00024110546,0.0000058023934,0.000010891114,0.00003953872,0.0000067468573,0.99339527,0.0028868506,0.0024356141,0.0009364615,0.000009411892],"about_ca_topic_score_codex":0.009392315,"about_ca_topic_score_gemma":0.012065255,"teacher_disagreement_score":0.009392315,"about_ca_system_score_codex":0.00080454117,"about_ca_system_score_gemma":0.0026363302,"threshold_uncertainty_score":0.018675327},"labels":[],"label_agreement":null},{"id":"W3015973434","doi":"10.1109/icassp40776.2020.9054179","title":"Drift Detection and Correction Post-Tracking","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer vision; Tracking (education); Minimum bounding box; BitTorrent tracker; Computer science; Segmentation; Object detection; Object (grammar); Pixel; Frame (networking); Image segmentation; Video tracking; Pattern recognition (psychology); Image (mathematics); Eye tracking","score_opus":0.024011239321723124,"score_gpt":0.26144007876003217,"score_spread":0.23742883943830906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3015973434","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":[0.033759665,0.00077980675,0.95857394,0.00010386541,0.00041621077,0.00013465868,0.00016489603,0.004224413,0.0018426006],"genre_scores_gemma":[0.55814725,0.0009388046,0.42788297,0.00027679937,0.00023904075,0.00017151055,0.001025687,0.0010975067,0.010220424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978509,0.00014699803,0.00013507127,0.00057801063,0.001032541,0.00025650262],"domain_scores_gemma":[0.9967817,0.00054389477,0.0003724759,0.00075820903,0.0013967728,0.0001468485],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023277767,0.0016054697,0.0020125264,0.002040334,0.00090457255,0.0013684462,0.0019630296,0.0011845907,0.0022170783],"category_scores_gemma":[0.005943264,0.00060742424,0.00094575103,0.0013831562,0.0005314218,0.0012941943,0.0015190055,0.0014460048,0.002498689],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006164729,0.00012407082,0.01161908,0.00030252046,0.00015341376,0.0005759189,0.00052294234,0.040028103,0.10931476,0.0037183198,0.008187886,0.8248365],"study_design_scores_gemma":[0.00005189666,0.00049603893,0.024427794,0.000092002585,0.00016199336,0.001356452,0.00017250467,0.7378987,0.19770648,0.004775055,0.032760076,0.00010095722],"about_ca_topic_score_codex":0.0050173136,"about_ca_topic_score_gemma":0.004847252,"teacher_disagreement_score":0.0050173136,"about_ca_system_score_codex":0.0008364458,"about_ca_system_score_gemma":0.0019738448,"threshold_uncertainty_score":0.0123105645},"labels":[],"label_agreement":null},{"id":"W3016096262","doi":"10.1109/icassp40776.2020.9054238","title":"Stochastic Multi-Scale Aggregation Network for Crowd Counting","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Memorial University of Newfoundland","funders":"","keywords":"Subnetwork; Computer science; Scale (ratio); Encoder; ENCODE; Fuse (electrical); Generator (circuit theory); Artificial intelligence; Data mining; Theoretical computer science; Power (physics); Engineering","score_opus":0.059181628633086195,"score_gpt":0.306802533810155,"score_spread":0.2476209051770688,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3016096262","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04353826,0.00030028162,0.95212924,0.00026501025,0.00008482022,0.000060863193,0.00020210506,0.001345262,0.0020740952],"genre_scores_gemma":[0.84274244,0.00030219535,0.1515938,0.00028739456,0.0001164747,0.00014588593,0.00077040924,0.000119523356,0.003921851],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996125,0.00008960227,0.000015644397,0.00012448862,0.00009789504,0.00005984952],"domain_scores_gemma":[0.9994873,0.00019617662,0.00007176401,0.000054056472,0.00014159911,0.000049172293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008694673,0.0010956414,0.0008701178,0.001097206,0.0006112095,0.00061285734,0.0012597302,0.0007695793,0.0013082911],"category_scores_gemma":[0.0023777718,0.0004507386,0.0005889424,0.0008846443,0.00061769,0.0015495723,0.001593856,0.0010052808,0.00036774133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017494491,0.0000872068,0.0026124616,0.000056830922,0.00007080295,0.00016260987,0.0001416273,0.7923493,0.0048511075,0.0075905225,0.0043613655,0.18754126],"study_design_scores_gemma":[0.0000018370308,0.000008684254,0.0001970995,0.0000026772748,0.000004315806,0.0000121017165,0.0000067873857,0.9963877,0.00054396427,0.002584912,0.00024675438,0.0000032716587],"about_ca_topic_score_codex":0.0074401596,"about_ca_topic_score_gemma":0.009403822,"teacher_disagreement_score":0.0074401596,"about_ca_system_score_codex":0.00116642,"about_ca_system_score_gemma":0.00070614007,"threshold_uncertainty_score":0.014793694},"labels":[],"label_agreement":null},{"id":"W3017180934","doi":"10.3390/sym12040647","title":"Person Re-Identification by Discriminative Local Features of Overlapping Stripes","year":2020,"lang":"en","type":"article","venue":"Symmetry","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Discriminative model; Artificial intelligence; Computer science; Concatenation (mathematics); Pattern recognition (psychology); Disjoint sets; Robustness (evolution); Histogram; Histogram of oriented gradients; Matching (statistics); Gaussian; Computer vision; Mathematics; Image (mathematics)","score_opus":0.03430611179340605,"score_gpt":0.28870742329726756,"score_spread":0.2544013115038615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017180934","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.27097794,0.00067935255,0.7219802,0.00013209901,0.00009363695,0.00013217109,0.00048087275,0.0028481204,0.0026755536],"genre_scores_gemma":[0.8744023,0.00034087946,0.11931045,0.00009351061,0.000060238563,0.000049416536,0.0014477503,0.00009489481,0.004200528],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948716,0.000052385432,0.000021646116,0.00016142866,0.00019482269,0.00008251121],"domain_scores_gemma":[0.999624,0.00004151879,0.00007448308,0.00012791934,0.00010420606,0.000027893198],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045252178,0.00059546577,0.00087733736,0.0013502801,0.00018165022,0.00038696855,0.0008625634,0.00036268306,0.0009957289],"category_scores_gemma":[0.0010127539,0.00021997016,0.0007221826,0.0007906815,0.00025825965,0.00075795106,0.0007654283,0.0004995836,0.0010102335],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053538877,0.00023257856,0.009925974,0.000095397176,0.00012432518,0.00027330956,0.000097439995,0.034978177,0.06672937,0.0013100229,0.0051454552,0.8805525],"study_design_scores_gemma":[0.000019947565,0.0002292333,0.01669168,0.000019012294,0.00006390006,0.0008336774,0.00008578568,0.9341346,0.042380292,0.0014515443,0.004060325,0.000029949355],"about_ca_topic_score_codex":0.0027826498,"about_ca_topic_score_gemma":0.0033817273,"teacher_disagreement_score":0.0027826498,"about_ca_system_score_codex":0.00023913059,"about_ca_system_score_gemma":0.00034217437,"threshold_uncertainty_score":0.0055329204},"labels":[],"label_agreement":null},{"id":"W3017949744","doi":"10.1016/j.asoc.2020.106302","title":"Robust fusion for RGB-D tracking using CNN features","year":2020,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Beijing Municipal Natural Science Foundation; State Key Laboratory of Robotics and System; National Natural Science Foundation of China; Deutsche Forschungsgemeinschaft","keywords":"Artificial intelligence; RGB color model; Computer science; Computer vision; Convolutional neural network; Benchmark (surveying); Tracking (education); Feature (linguistics); Eye tracking; Pattern recognition (psychology)","score_opus":0.10457757031399195,"score_gpt":0.30456150404001403,"score_spread":0.19998393372602208,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3017949744","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032212064,0.000651963,0.96136606,0.00014272927,0.00022462542,0.000057064193,0.00035527974,0.0014757745,0.0035145036],"genre_scores_gemma":[0.6269868,0.000825378,0.36112764,0.0002737727,0.00012939311,0.00012000827,0.0015552598,0.00019436104,0.0087874355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999406,0.00004620168,0.000030356981,0.00018264772,0.00023945046,0.000095302144],"domain_scores_gemma":[0.9995981,0.000046748315,0.00004862043,0.00009965026,0.00018395433,0.000022886948],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071549043,0.00097553956,0.00084964145,0.0011976035,0.00040434807,0.0011160595,0.00089638913,0.0009214104,0.0020670197],"category_scores_gemma":[0.0015324032,0.0004969005,0.0008704943,0.0015958635,0.0002827923,0.0012461921,0.001667722,0.0007521191,0.0014600286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043230507,0.00016413661,0.0031542585,0.00012814683,0.00018103616,0.00009366736,0.00006998949,0.080341615,0.10864166,0.0036888977,0.005457648,0.7976467],"study_design_scores_gemma":[0.000011376125,0.00008636563,0.00427492,0.000029855815,0.00006382825,0.00011611021,0.000022604867,0.94031614,0.047812276,0.0025961928,0.0046423706,0.00002801182],"about_ca_topic_score_codex":0.0061932127,"about_ca_topic_score_gemma":0.007923955,"teacher_disagreement_score":0.0061932127,"about_ca_system_score_codex":0.0007876205,"about_ca_system_score_gemma":0.00090976944,"threshold_uncertainty_score":0.01231432},"labels":[],"label_agreement":null},{"id":"W3018791373","doi":"10.5121/csit.2019.91714","title":"Visual Tracking Applying Depth Spatiogram and Multi-feature Data","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Feature (linguistics); Feature tracking; Tracking (education); Pattern recognition (psychology)","score_opus":0.07351024974167035,"score_gpt":0.3612193799295674,"score_spread":0.287709130187897,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3018791373","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025985144,0.0005673185,0.96844673,0.0000964005,0.00008436732,0.000088889996,0.0006163061,0.002485723,0.0016292311],"genre_scores_gemma":[0.46927902,0.0010463945,0.5239123,0.00017775444,0.000090196794,0.000120140816,0.0022036924,0.00015469079,0.003015867],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946207,0.00004246558,0.000023650087,0.00018817348,0.00022650805,0.000057258734],"domain_scores_gemma":[0.99958485,0.000053192747,0.00007937329,0.00011728599,0.00013660995,0.00002874842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004219691,0.000828076,0.00077467423,0.002474607,0.00030917465,0.0011517989,0.00095626287,0.0007083531,0.0014093663],"category_scores_gemma":[0.0012733012,0.000437874,0.00069939747,0.002104361,0.00023917276,0.001325006,0.0012123182,0.0007148283,0.000668294],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027561517,0.00016947834,0.006334946,0.00025210762,0.00016115571,0.00012947287,0.00012962979,0.055795528,0.09330119,0.002980517,0.0039969683,0.8364734],"study_design_scores_gemma":[0.000029737042,0.00017894454,0.01102416,0.000041065538,0.00006906583,0.00033946004,0.000063421234,0.9208599,0.053639725,0.0040889764,0.009615985,0.00004960971],"about_ca_topic_score_codex":0.007515356,"about_ca_topic_score_gemma":0.009763866,"teacher_disagreement_score":0.007515356,"about_ca_system_score_codex":0.0008351886,"about_ca_system_score_gemma":0.00074062595,"threshold_uncertainty_score":0.014943242},"labels":[],"label_agreement":null},{"id":"W3020207422","doi":"10.1109/csci49370.2019.00079","title":"Recognition of Drone Formation Intentions Using Supervised Machine Learning","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Drone; Computer science; Softmax function; Artificial intelligence; Machine learning; Identification (biology); Support vector machine; Plan (archaeology); Computer security; Deep learning; Geography","score_opus":0.06305058343588359,"score_gpt":0.2937886414169703,"score_spread":0.2307380579810867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3020207422","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":[0.57356536,0.0003612227,0.42155188,0.00022590494,0.000082805156,0.00011642671,0.00032437095,0.0017414986,0.0020305363],"genre_scores_gemma":[0.9451645,0.000059103,0.05285774,0.00003771598,0.000024656929,0.0000456346,0.00067177817,0.000022372293,0.0011164405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960726,0.00011237212,0.000025139982,0.00012966717,0.00007082262,0.000054764965],"domain_scores_gemma":[0.99817955,0.00084863853,0.00023970989,0.00027273656,0.00039200007,0.00006752551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059450744,0.0005283245,0.00049239845,0.00043885747,0.00021887683,0.0003726052,0.0006292354,0.00048791736,0.0007618687],"category_scores_gemma":[0.002359032,0.00022003813,0.00038539196,0.0003016367,0.00025301884,0.00067169603,0.00033467653,0.00072688714,0.00038851716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00044607613,0.0012457196,0.024318915,0.000114140654,0.0001738536,0.000091173984,0.00013646264,0.36795533,0.023808368,0.0008662665,0.0026147908,0.5782289],"study_design_scores_gemma":[0.0000057606203,0.000061012757,0.0031671815,0.0000034766524,0.000005552407,0.000012790139,0.000016850767,0.9926905,0.0035009428,0.00038624648,0.00014415583,0.0000055186733],"about_ca_topic_score_codex":0.0044952673,"about_ca_topic_score_gemma":0.006804785,"teacher_disagreement_score":0.0044952673,"about_ca_system_score_codex":0.00033752478,"about_ca_system_score_gemma":0.00041281167,"threshold_uncertainty_score":0.008938193},"labels":[],"label_agreement":null},{"id":"W3022161521","doi":"10.1109/tnnls.2020.2984810","title":"Co-Learning Non-Negative Correlated and Uncorrelated Features for Multi-View Data","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Uncorrelated; Feature (linguistics); Subspace topology; Computer science; Artificial intelligence; Convergence (economics); Pattern recognition (psychology); Semantics (computer science); Process (computing); Machine learning; Data mining; Mathematics; Statistics","score_opus":0.0628084053462207,"score_gpt":0.31344911922342406,"score_spread":0.2506407138772033,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3022161521","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":[0.013185546,0.00034555784,0.98578995,0.00009176381,0.00002774471,0.000037705577,0.00005167186,0.00019451667,0.00027549273],"genre_scores_gemma":[0.5407532,0.000599709,0.4544349,0.0003277252,0.00020723965,0.00028313175,0.0009598749,0.0001748608,0.0022593034],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99712855,0.0007578323,0.00018186135,0.0010659025,0.0006397645,0.00022611697],"domain_scores_gemma":[0.99456406,0.002945062,0.0005970016,0.0008667765,0.0008286716,0.00019852511],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0039221304,0.0015685756,0.0022988936,0.0017189819,0.00075780833,0.001370482,0.002417887,0.0017783754,0.0010303584],"category_scores_gemma":[0.009445877,0.0007821542,0.0018460776,0.0024572064,0.0015479111,0.0028155874,0.0022797242,0.002544489,0.00046707172],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004396951,0.00040870783,0.008063807,0.00035468454,0.0004789301,0.00037497806,0.00034573965,0.47560695,0.014231857,0.013712787,0.0041547418,0.4818271],"study_design_scores_gemma":[0.000009630274,0.000056876597,0.0006228865,0.000008855517,0.00002378393,0.000075482814,0.000027119497,0.9919996,0.0020079392,0.0046875007,0.00046389597,0.000016293086],"about_ca_topic_score_codex":0.003371033,"about_ca_topic_score_gemma":0.004138564,"teacher_disagreement_score":0.0039221304,"about_ca_system_score_codex":0.0009556599,"about_ca_system_score_gemma":0.0013165007,"threshold_uncertainty_score":0.020742476},"labels":[],"label_agreement":null},{"id":"W3027389553","doi":"10.18280/ts.370204","title":"Detection of Skin Cancer Image by Feature Selection Methods Using New Buzzard Optimization (BUZO) Algorithm","year":2020,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Buzzard; Feature selection; Computer science; Feature (linguistics); Algorithm; Selection (genetic algorithm); Pattern recognition (psychology); Artificial intelligence; Image (mathematics); Geography","score_opus":0.030993728107833594,"score_gpt":0.32628309025446206,"score_spread":0.2952893621466285,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3027389553","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":[0.036946427,0.00058292743,0.9601337,0.00016187895,0.000051012634,0.000118410826,0.0000648714,0.0009469004,0.0009938693],"genre_scores_gemma":[0.25433862,0.0005150715,0.7398285,0.00012914112,0.000046470057,0.00035728014,0.00042368847,0.00018284004,0.004178295],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994112,0.00009391427,0.000044457,0.0001376953,0.00024820634,0.00006457903],"domain_scores_gemma":[0.99967444,0.000096564174,0.00004300718,0.000018268936,0.00015243473,0.000015338748],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007755884,0.000862413,0.0011156083,0.0017009419,0.0005128813,0.00086276396,0.00082502485,0.00083088625,0.001866505],"category_scores_gemma":[0.0013227277,0.0004238494,0.0009396766,0.0010900301,0.0003846839,0.0007165277,0.00045969902,0.0005288198,0.00043656217],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007281902,0.00019434086,0.004558706,0.00025091815,0.00019419858,0.00018532343,0.00017187968,0.09368778,0.09021974,0.0035358837,0.005393955,0.8008791],"study_design_scores_gemma":[0.000051890616,0.00014424662,0.004664556,0.000017343278,0.000053589185,0.0001958464,0.000035491164,0.9728899,0.018248776,0.0008539026,0.0028131637,0.000031253705],"about_ca_topic_score_codex":0.0058828164,"about_ca_topic_score_gemma":0.0050772447,"teacher_disagreement_score":0.0058828164,"about_ca_system_score_codex":0.0006667576,"about_ca_system_score_gemma":0.00064386113,"threshold_uncertainty_score":0.011697173},"labels":[],"label_agreement":null},{"id":"W3033418451","doi":"10.1109/crv50864.2020.00028","title":"Domain Adaptation in Crowd Counting","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Manitoba; Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Domain (mathematical analysis); Domain adaptation; Benchmark (surveying); Artificial intelligence; Computer vision; Adaptation (eye); Image (mathematics); Viewpoints; Machine learning; Pattern recognition (psychology); Mathematics","score_opus":0.053413070370529794,"score_gpt":0.28100342797760336,"score_spread":0.22759035760707358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3033418451","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":[0.027907044,0.0011970901,0.96659964,0.00048239788,0.00022861795,0.00017515212,0.00026030204,0.0010387365,0.0021109865],"genre_scores_gemma":[0.54641783,0.0016203515,0.44175807,0.000845919,0.0007116589,0.0004483897,0.0017601603,0.00038515215,0.006052488],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9964353,0.0013306165,0.00016561613,0.0012470599,0.0005372025,0.00028414288],"domain_scores_gemma":[0.9935946,0.0032124391,0.0008459433,0.0010728743,0.0009157013,0.0003584343],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0042001773,0.002073579,0.002274246,0.0028813756,0.0014133408,0.0018289019,0.0028354686,0.002361136,0.002064907],"category_scores_gemma":[0.013601291,0.0007546724,0.001891411,0.0027102553,0.0019309042,0.003038413,0.0033519685,0.0028340777,0.0013133122],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043265754,0.000412117,0.011213926,0.00047158243,0.0002998132,0.00085431803,0.0009830473,0.5704579,0.009107274,0.022385638,0.013674336,0.36970738],"study_design_scores_gemma":[0.000027129048,0.00010816448,0.0020840883,0.000055902674,0.00004680493,0.0005393823,0.00022965562,0.9511726,0.005216702,0.032286555,0.008164907,0.00006813728],"about_ca_topic_score_codex":0.0061558387,"about_ca_topic_score_gemma":0.0033243739,"teacher_disagreement_score":0.0061558387,"about_ca_system_score_codex":0.0015178575,"about_ca_system_score_gemma":0.0011791077,"threshold_uncertainty_score":0.022212923},"labels":[],"label_agreement":null},{"id":"W3034135958","doi":"10.1109/crv50864.2020.00019","title":"Pre-trained CNNs as Visual Feature Extractors: A Broad Evaluation","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Convolutional neural network; Robustness (evolution); Feature (linguistics); Artificial intelligence; Pattern recognition (psychology); Feature extraction; Matching (statistics); Mathematics; Statistics","score_opus":0.04414476406972429,"score_gpt":0.36579046325528797,"score_spread":0.3216456991855637,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034135958","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":[0.6205479,0.054220278,0.255517,0.0014297605,0.0020452715,0.00203797,0.009849179,0.019871945,0.03448063],"genre_scores_gemma":[0.80618066,0.011224835,0.13221216,0.0007446194,0.0003770605,0.0005098768,0.028183222,0.0010027612,0.019564908],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9968618,0.0005537922,0.0002571444,0.0007175234,0.0012381546,0.00037163505],"domain_scores_gemma":[0.99645644,0.0015954054,0.00024852782,0.0005682506,0.0009753513,0.00015595784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004516766,0.0037954848,0.0014344007,0.0022163487,0.00050692935,0.0014522272,0.002415775,0.0018809321,0.00411624],"category_scores_gemma":[0.010154233,0.000651484,0.0012353603,0.0012569526,0.00058251206,0.002832127,0.0016328978,0.0013750913,0.0017553262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.002400193,0.0009865024,0.014433614,0.0019712641,0.0016138755,0.00033983216,0.000077276505,0.14135557,0.020231897,0.0013524301,0.020289728,0.7949478],"study_design_scores_gemma":[0.00026048403,0.0034247395,0.02039803,0.00055886945,0.00091380836,0.0008953034,0.00018176498,0.88889766,0.06459315,0.0019130547,0.017865812,0.00009740988],"about_ca_topic_score_codex":0.012052234,"about_ca_topic_score_gemma":0.018495297,"teacher_disagreement_score":0.012052234,"about_ca_system_score_codex":0.0017145803,"about_ca_system_score_gemma":0.0012530927,"threshold_uncertainty_score":0.023964167},"labels":[],"label_agreement":null},{"id":"W3034326629","doi":"10.1007/s40747-020-00161-4","title":"Overview and methods of correlation filter algorithms in object tracking","year":2020,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":305,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"Science and Technology Program of Hunan Province; State Key Laboratory of Computer Aided Design and Computer Graphics; Zhejiang University","keywords":"Tracking (education); Video tracking; Computer science; Artificial intelligence; Computer vision; Eye tracking; Object (grammar); Tracking system; Reliability (semiconductor); Filter (signal processing); Presentation (obstetrics)","score_opus":0.19359270575486331,"score_gpt":0.3925816649300978,"score_spread":0.19898895917523446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034326629","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":[0.00035997797,0.0036537373,0.9940891,0.00008735019,0.000114176946,0.000031478474,0.000045431567,0.00028979467,0.0013289688],"genre_scores_gemma":[0.032482132,0.016208677,0.9432882,0.0002628451,0.00079508114,0.0003135367,0.0005968735,0.0003275651,0.0057250373],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9975998,0.00047567088,0.00019531809,0.0006005996,0.0010245767,0.000104133884],"domain_scores_gemma":[0.99788696,0.0008170896,0.0001607064,0.0002650136,0.00081375503,0.000056386463],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002821008,0.0014647077,0.0014342839,0.0035237367,0.00084563374,0.0021813076,0.0021254495,0.0021597194,0.0032900246],"category_scores_gemma":[0.00558816,0.0010178462,0.0018651258,0.00586035,0.0008954949,0.0026311271,0.0013651984,0.0025222686,0.003295259],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010178755,0.00007348871,0.0011352217,0.0006718095,0.00018188491,0.0000959344,0.00014661043,0.07980846,0.0074842195,0.067904875,0.010438877,0.83195686],"study_design_scores_gemma":[0.000032110453,0.00010579835,0.0012077742,0.00019811858,0.00010836865,0.00042015163,0.000031469808,0.8747912,0.007072309,0.033931095,0.08199812,0.00010349538],"about_ca_topic_score_codex":0.008582831,"about_ca_topic_score_gemma":0.003275586,"teacher_disagreement_score":0.008582831,"about_ca_system_score_codex":0.0013136406,"about_ca_system_score_gemma":0.0022322033,"threshold_uncertainty_score":0.017065763},"labels":[],"label_agreement":null},{"id":"W3034455598","doi":"10.24963/ijcai.2020/285","title":"Compressed Self-Attention for Deep Metric Learning with Low-Rank Approximation","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Wuhan University; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Hubei Province; National Natural Science Foundation of China","keywords":"Metric (unit); Computer science; Pooling; Landmark; Artificial intelligence; Feature (linguistics); Pairwise comparison; Rank (graph theory); Deep learning; Pattern recognition (psychology); Computation; Similarity (geometry); Task (project management); Machine learning; Algorithm; Mathematics; Image (mathematics)","score_opus":0.020148872269458436,"score_gpt":0.26001784861114835,"score_spread":0.2398689763416899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034455598","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012284935,0.000559663,0.9828011,0.00022995043,0.00006197366,0.000035880363,0.0001229841,0.0028447115,0.001058755],"genre_scores_gemma":[0.5860898,0.00062200567,0.40269953,0.000636713,0.00027219544,0.00021089822,0.0013198965,0.0004433335,0.007705631],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991886,0.00018997915,0.000040825555,0.00019591163,0.00027520023,0.00010945268],"domain_scores_gemma":[0.9990396,0.00032907346,0.00009774614,0.0002443098,0.00023558382,0.000053593016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010201946,0.001288245,0.001239587,0.0008179745,0.0003620716,0.00075532537,0.0020232014,0.0011489295,0.0037668555],"category_scores_gemma":[0.004243676,0.00041707337,0.00065999886,0.0011252268,0.0007189798,0.0021427067,0.0017002408,0.0017023052,0.0013221767],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022768776,0.00021075697,0.0011199695,0.00019664598,0.000113009824,0.00012881067,0.00013399302,0.26708478,0.014788181,0.022194276,0.014277562,0.67952436],"study_design_scores_gemma":[0.000007986873,0.000044574652,0.00013592654,0.0000041282506,0.000006861511,0.000027933316,0.0000065688514,0.99028075,0.0025306628,0.006022611,0.0009254096,0.0000066064076],"about_ca_topic_score_codex":0.007506793,"about_ca_topic_score_gemma":0.009277217,"teacher_disagreement_score":0.007506793,"about_ca_system_score_codex":0.0010949934,"about_ca_system_score_gemma":0.0011982248,"threshold_uncertainty_score":0.014926195},"labels":[],"label_agreement":null},{"id":"W3034703645","doi":"10.1155/2020/3828395","title":"Traffic State Recognition of Intersection Based on Image Model and PCA Hashing","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"North China University of Technology; Beijing Municipal Education Commission","keywords":"Intersection (aeronautics); Computer science; Intelligent transportation system; Feature extraction; Artificial intelligence; Traffic flow (computer networking); Field (mathematics); Feature (linguistics); Data mining; Pattern recognition (psychology); Computer vision; Engineering; Computer network","score_opus":0.029670938855052665,"score_gpt":0.2787021295791527,"score_spread":0.24903119072410002,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3034703645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04841095,0.00014436638,0.9485331,0.00008380598,0.000043538133,0.000052260413,0.000082401086,0.00072288036,0.0019266095],"genre_scores_gemma":[0.7944139,0.00044890653,0.20096543,0.00005908358,0.00005969982,0.00011622919,0.00045322583,0.00006573836,0.003417823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995938,0.000036968286,0.000015521917,0.00013120502,0.00017287166,0.00004962165],"domain_scores_gemma":[0.9998363,0.000021904872,0.000022454054,0.000040702143,0.000067772664,0.000010776543],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002173572,0.0004714028,0.0004671603,0.0007767347,0.00032908682,0.0007792951,0.00062505534,0.000504453,0.0009943836],"category_scores_gemma":[0.00063145405,0.00026283856,0.00068883743,0.0007853306,0.00048964866,0.0017432276,0.0005904374,0.00055382866,0.0005688334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033280643,0.00016960126,0.008434055,0.00017550774,0.00009843713,0.00024638354,0.00038367882,0.15625139,0.1106679,0.021251166,0.0038513097,0.69813776],"study_design_scores_gemma":[0.000009381322,0.0000928379,0.0032489947,0.000006566298,0.000026789357,0.00029121328,0.00006347088,0.9729043,0.019114379,0.0025257666,0.0016735193,0.000042755008],"about_ca_topic_score_codex":0.0030327905,"about_ca_topic_score_gemma":0.001660668,"teacher_disagreement_score":0.0030327905,"about_ca_system_score_codex":0.00035146574,"about_ca_system_score_gemma":0.0005889702,"threshold_uncertainty_score":0.006030321},"labels":[],"label_agreement":null},{"id":"W3037238894","doi":"10.1117/12.770757","title":"Behavior subtraction","year":2007,"lang":"de","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Background subtraction; Artificial intelligence; Computer vision; Object detection; Motion detection; Motion (physics); Segmentation; Tracking (education); Video tracking; Path (computing); Identification (biology); Task (project management); Object (grammar); Pixel","score_opus":0.020350450760668905,"score_gpt":0.2753236180967352,"score_spread":0.25497316733606634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3037238894","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":[0.03524954,0.0008993264,0.8669885,0.00046264275,0.001031673,0.0002500812,0.0022339898,0.009425544,0.08345876],"genre_scores_gemma":[0.34254003,0.0015358832,0.5092318,0.0012383031,0.00034642633,0.00031801802,0.012497674,0.0024141113,0.12987778],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994282,0.000033289954,0.000016270837,0.00024511584,0.00018963279,0.00008739782],"domain_scores_gemma":[0.9996793,0.000023161045,0.000020375635,0.00007271766,0.00017452196,0.000029993033],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028241883,0.0017097326,0.0008429848,0.0012624803,0.00060954224,0.0012933649,0.0018801177,0.001160182,0.020122252],"category_scores_gemma":[0.00082698074,0.00045755206,0.00076774403,0.0007184866,0.00030361098,0.0010904475,0.0012921577,0.0010449145,0.017671203],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003902932,0.00021519841,0.0028275875,0.00029060693,0.00010714716,0.00021372644,0.0001395212,0.007854121,0.115362406,0.010557925,0.02315475,0.83888674],"study_design_scores_gemma":[0.000071857794,0.00041631298,0.020874513,0.000112935995,0.00028219423,0.0019888866,0.00052176975,0.44529027,0.24072392,0.02568409,0.26389328,0.00013999983],"about_ca_topic_score_codex":0.003878013,"about_ca_topic_score_gemma":0.005706327,"teacher_disagreement_score":0.020122252,"about_ca_system_score_codex":0.0005512047,"about_ca_system_score_gemma":0.00096484215,"threshold_uncertainty_score":0.06731564},"labels":[],"label_agreement":null},{"id":"W3039842350","doi":"10.1007/s00530-020-00668-3","title":"Robust visual tracking via part-based model","year":2020,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Robustness (evolution); Computer vision; Active appearance model; Minimum bounding box; Eye tracking; Video tracking; Discriminative model; Kernel (algebra); Pattern recognition (psychology); ENCODE; Object (grammar); Mathematics; Image (mathematics)","score_opus":0.1178469777989681,"score_gpt":0.2980814764935503,"score_spread":0.18023449869458222,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3039842350","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027250766,0.00018259312,0.99613225,0.000046285844,0.000028390572,0.000014651209,0.000049550363,0.00055576296,0.00026552452],"genre_scores_gemma":[0.3965076,0.0011607275,0.5916234,0.0003116178,0.00017316574,0.00019515946,0.0011961529,0.0006494015,0.00818268],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999008,0.00021376814,0.000043968816,0.0003713207,0.0002854051,0.000077561665],"domain_scores_gemma":[0.9989016,0.00039161774,0.00014240303,0.00032040046,0.00019931946,0.00004455824],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012124781,0.0013605768,0.0019853965,0.0013906937,0.00044437803,0.0015386011,0.0019210269,0.0019672324,0.0016215729],"category_scores_gemma":[0.0034572326,0.0011335803,0.001874072,0.0019336859,0.00079841417,0.0020241935,0.0015465793,0.0016420966,0.00154451],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00035247186,0.000106119696,0.000710175,0.00014426924,0.00023144766,0.00010765153,0.000059751335,0.554845,0.03487559,0.007945762,0.0033420895,0.39727974],"study_design_scores_gemma":[0.000004455962,0.000021854048,0.00019804426,0.000005027831,0.000016987484,0.000031553915,0.000002070439,0.9944305,0.0023041414,0.0024979562,0.00047834025,0.000009020755],"about_ca_topic_score_codex":0.005870262,"about_ca_topic_score_gemma":0.0048408476,"teacher_disagreement_score":0.005870262,"about_ca_system_score_codex":0.0007690038,"about_ca_system_score_gemma":0.0010023358,"threshold_uncertainty_score":0.011672139},"labels":[],"label_agreement":null},{"id":"W3042867243","doi":"10.3390/s20143923","title":"Malicious UAV Detection Using Integrated Audio and Visual Features for Public Safety Applications","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Drone; Computer science; Provisioning; Scheme (mathematics); Feature (linguistics); Computer security; Artificial intelligence; Support vector machine; Deep learning; Public security; Real-time computing; Telecommunications","score_opus":0.0445550845053924,"score_gpt":0.3095517292916006,"score_spread":0.26499664478620816,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3042867243","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42643,0.0019446607,0.56400865,0.0003594298,0.00022933957,0.00011422464,0.0005443364,0.0023978457,0.003971516],"genre_scores_gemma":[0.9281134,0.0005276238,0.068626404,0.00008690318,0.000079692785,0.000025011723,0.0006933709,0.00003307343,0.0018146128],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978215,0.000024124403,0.000010180507,0.0000544642,0.000082800616,0.000046343222],"domain_scores_gemma":[0.99973804,0.000055476354,0.000045555684,0.000041388594,0.000092756505,0.000026888109],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021564243,0.00059743616,0.00043509394,0.0012391944,0.00016733061,0.00037410186,0.00041893483,0.00042658998,0.00066994206],"category_scores_gemma":[0.00067408424,0.00014776982,0.00034550953,0.0005531309,0.0002160009,0.00069765636,0.00046932354,0.0004104379,0.00041163538],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006942698,0.00032626712,0.010084413,0.0001328718,0.00008060351,0.00035098975,0.00007346035,0.036773503,0.15069132,0.0008438693,0.0035794894,0.79636896],"study_design_scores_gemma":[0.000025930129,0.00028037705,0.012224412,0.000023048728,0.000074442025,0.00035805683,0.000106550935,0.93207,0.051306162,0.0011685586,0.0023374783,0.000024905483],"about_ca_topic_score_codex":0.0022240714,"about_ca_topic_score_gemma":0.0038772558,"teacher_disagreement_score":0.0022240714,"about_ca_system_score_codex":0.00022866184,"about_ca_system_score_gemma":0.00024702481,"threshold_uncertainty_score":0.004422307},"labels":[],"label_agreement":null},{"id":"W3043718939","doi":"10.1007/s43154-020-00011-8","title":"Using Deep Learning to Find Victims in Unknown Cluttered Urban Search and Rescue Environments","year":2020,"lang":"en","type":"article","venue":"Current Robotics Reports","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; RGB color model; Detector; Deep learning; Feature (linguistics); Pyramid (geometry); Urban search and rescue; Feature extraction; Identification (biology); Single shot; Rescue robot; Mobile robot; Pattern recognition (psychology); Robot; Mathematics","score_opus":0.09172285302467226,"score_gpt":0.3419341077514897,"score_spread":0.25021125472681743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3043718939","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.44006917,0.0007054657,0.5528719,0.00053286826,0.00016689759,0.00006251036,0.00026192213,0.001524066,0.0038052173],"genre_scores_gemma":[0.94817024,0.00016958729,0.048303854,0.00015514814,0.00005121935,0.000019232955,0.0003287313,0.000054391632,0.0027477005],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971324,0.000038530903,0.000011447602,0.00007582516,0.000063763015,0.00009722281],"domain_scores_gemma":[0.9992924,0.00029540333,0.00008566241,0.000079639976,0.00017761686,0.00006924093],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055751303,0.00084671413,0.00067793974,0.0010085457,0.0004467138,0.00081555406,0.0013247461,0.0012810322,0.0009098679],"category_scores_gemma":[0.001781779,0.0005723365,0.000481031,0.0007677111,0.0006207627,0.0013509291,0.001316248,0.0010313081,0.0003416302],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029053725,0.00040943333,0.00787016,0.00007886113,0.00012918389,0.00018598205,0.00016121335,0.67302126,0.012433139,0.0015622309,0.0030812535,0.30077684],"study_design_scores_gemma":[0.000003558805,0.000020731193,0.00038044178,0.0000028559934,0.000005303894,0.000011387274,0.000016620135,0.9978638,0.0007820268,0.0008226411,0.000087745444,0.0000028191153],"about_ca_topic_score_codex":0.012164039,"about_ca_topic_score_gemma":0.01318702,"teacher_disagreement_score":0.012164039,"about_ca_system_score_codex":0.0006238278,"about_ca_system_score_gemma":0.0007744698,"threshold_uncertainty_score":0.024186432},"labels":[],"label_agreement":null},{"id":"W3045524274","doi":"10.1007/978-3-030-58583-9_10","title":"Unsupervised Domain Adaptation in the Dissimilarity Space for Person Re-identification","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Benchmark (surveying); Metric (unit); Pattern recognition (psychology); Domain adaptation; Domain (mathematical analysis); Feature vector; Feature (linguistics); Gradient descent; Adaptation (eye); Representation (politics)","score_opus":0.06383244040056965,"score_gpt":0.29747005504263274,"score_spread":0.2336376146420631,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3045524274","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":[0.01002911,0.00034913747,0.98810005,0.000054853095,0.00006575563,0.000023385644,0.00010657753,0.00043825744,0.00083284074],"genre_scores_gemma":[0.28143176,0.000866206,0.7050264,0.00014979806,0.00019814688,0.00013045376,0.001577395,0.00046900177,0.010150858],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992004,0.0002189878,0.00003288533,0.00026871994,0.00019572198,0.000083321975],"domain_scores_gemma":[0.9991412,0.000279155,0.00006079894,0.0002754303,0.00019545002,0.000047945254],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00080507685,0.0005761006,0.0012427198,0.0009095641,0.00035400936,0.00087585027,0.0011734008,0.00074549887,0.0029582279],"category_scores_gemma":[0.0025590737,0.0003004777,0.00095809327,0.001556659,0.0005450906,0.0011461317,0.0017210706,0.0013355693,0.001950979],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038302774,0.00020139497,0.0014634962,0.00016853905,0.00013838334,0.00010042661,0.00013008321,0.10123484,0.039323036,0.015246214,0.007854289,0.83375627],"study_design_scores_gemma":[0.000009743915,0.000088709756,0.0016359958,0.000015828185,0.00002166715,0.00023778346,0.000066534696,0.97092855,0.009336926,0.0130054,0.004624453,0.000028410832],"about_ca_topic_score_codex":0.0019809627,"about_ca_topic_score_gemma":0.0023787064,"teacher_disagreement_score":0.0029582279,"about_ca_system_score_codex":0.00033920768,"about_ca_system_score_gemma":0.00043968216,"threshold_uncertainty_score":0.009896278},"labels":[],"label_agreement":null},{"id":"W3048448068","doi":"10.5194/isprs-archives-xliii-b2-2020-623-2020","title":"VEHICLE TRACKING AND SPEED ESTIMATION FROM UNMANNED AERIAL VIDEOS","year":2020,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Centre de Géomatique du Québec","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Kalman filter; Orientation (vector space); Tracking (education); Convolutional neural network; Photogrammetry; Pixel; Position (finance); Mathematics","score_opus":0.027816862661567618,"score_gpt":0.2726848083970335,"score_spread":0.2448679457354659,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3048448068","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1562172,0.0008326934,0.83503634,0.00011797445,0.00023279527,0.00009642339,0.0007262746,0.0023369906,0.004403278],"genre_scores_gemma":[0.7962214,0.0005226771,0.1958714,0.00009083572,0.00010838185,0.00007537479,0.0018826732,0.00008858853,0.0051387465],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997389,0.000027169119,0.000009897394,0.000092415394,0.00009332374,0.000038294216],"domain_scores_gemma":[0.99979526,0.000025519304,0.000040490253,0.000029030949,0.000097317155,0.000012262619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00017535975,0.0007484833,0.00037451577,0.0011001021,0.0001695478,0.00037345066,0.0004219203,0.00044356438,0.0010078254],"category_scores_gemma":[0.0006402673,0.00021535436,0.00023808272,0.000670112,0.000116287796,0.00053637044,0.00031937042,0.00035757368,0.0006045785],"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":[0.00020920948,0.0000804735,0.004643813,0.00017124902,0.000094876676,0.0002563388,0.0000770897,0.079871535,0.0823942,0.0019634087,0.0049074967,0.8253303],"study_design_scores_gemma":[0.000019867928,0.000104810664,0.00864639,0.00002773747,0.000030421668,0.00016633279,0.00006451412,0.9472795,0.036064927,0.0015175163,0.0060591544,0.00001889373],"about_ca_topic_score_codex":0.0080755325,"about_ca_topic_score_gemma":0.006899312,"teacher_disagreement_score":0.0080755325,"about_ca_system_score_codex":0.0002382912,"about_ca_system_score_gemma":0.00035915084,"threshold_uncertainty_score":0.016057074},"labels":[],"label_agreement":null},{"id":"W3082558183","doi":"10.1155/2020/2949170","title":"A Front Water Recognition Method Based on Image Data for Off-Road Intelligent Vehicle","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Intelligent transportation system; Computer science; Obstacle; Internet of Things; Key (lock); The Internet; Scale-invariant feature transform; Field (mathematics); Data aggregator; Computer vision; Real-time computing; Artificial intelligence; Feature (linguistics); Feature extraction; Engineering; Wireless sensor network; Embedded system; Computer security; Transport engineering; Computer network","score_opus":0.06911357631719042,"score_gpt":0.3474394717253116,"score_spread":0.27832589540812114,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3082558183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064330414,0.00029945848,0.92809343,0.00017701597,0.00018282152,0.000116814765,0.0001681201,0.0022809429,0.004350836],"genre_scores_gemma":[0.5618251,0.0006366411,0.4287174,0.0002278415,0.00008692459,0.000115420466,0.0005703675,0.0002253294,0.00759493],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995789,0.000024808218,0.000020550815,0.00011634727,0.00020296511,0.00005652798],"domain_scores_gemma":[0.9997075,0.00002392128,0.000021653614,0.000045856268,0.00017996551,0.000021020414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002753865,0.0006177456,0.00055264245,0.0017945528,0.00039567662,0.0007449924,0.0006760361,0.00069483474,0.0021967294],"category_scores_gemma":[0.0006689232,0.0003005367,0.00061571447,0.0010882253,0.0003614475,0.0018067867,0.00065476715,0.0006067993,0.0011353359],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022100078,0.000114003764,0.0030201364,0.00015506048,0.00004627452,0.00021523643,0.00014047814,0.008071051,0.21493651,0.0026488006,0.0047425907,0.7656889],"study_design_scores_gemma":[0.00005521941,0.00026576367,0.009091106,0.00003063862,0.0001290056,0.0007681733,0.00034105158,0.66850495,0.30541232,0.0021414312,0.013145621,0.00011477202],"about_ca_topic_score_codex":0.003854671,"about_ca_topic_score_gemma":0.0030492956,"teacher_disagreement_score":0.003854671,"about_ca_system_score_codex":0.00034124867,"about_ca_system_score_gemma":0.000616229,"threshold_uncertainty_score":0.007664442},"labels":[],"label_agreement":null},{"id":"W3083550465","doi":"10.5539/jgg.v12n2p40","title":"Cemetery Mapping and Digital Data Analysis: A Case Study in Minnesota, USA","year":2020,"lang":"en","type":"article","venue":"Journal of Geography and Geology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Woodland; Data collection; Geography; Global Positioning System; Table (database); Geospatial analysis; Thematic map; Spatial database; German; Georeference; Plan (archaeology); Database; Cartography; Spatial analysis; Computer science; Archaeology; Physical geography; Remote sensing; Statistics","score_opus":0.088746583292147,"score_gpt":0.3072611444520035,"score_spread":0.21851456115985646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3083550465","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":[0.9868487,0.00026698888,0.0019733366,0.0010895467,0.00002260702,0.00011189222,0.00025208134,0.000030177078,0.009404602],"genre_scores_gemma":[0.9879176,0.00060334953,0.006962584,0.00020316869,0.000009624969,0.000047513127,0.00020737686,0.0000173401,0.004031391],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993414,0.00028362463,0.00003457918,0.0000762594,0.00014414357,0.00011992358],"domain_scores_gemma":[0.999173,0.00024919063,0.00012589285,0.00007508386,0.00020991621,0.00016690379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011246685,0.00029296364,0.00014214558,0.0012668823,0.0027789078,0.0015195126,0.0008622791,0.0005317425,0.0013649806],"category_scores_gemma":[0.0022649148,0.00022197083,0.00020831208,0.0024837232,0.0008355571,0.0008897425,0.0014411535,0.0004540261,0.00011808348],"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":[0.00014031849,0.00067025796,0.6062841,0.000573556,0.0001112414,0.0623769,0.1617242,0.0052354294,0.006690851,0.007618115,0.016129727,0.13244535],"study_design_scores_gemma":[0.000015291114,0.0002835094,0.41308242,0.00041418005,0.00008416805,0.009453693,0.46310964,0.009346875,0.002009384,0.0010325756,0.10108804,0.00008020426],"about_ca_topic_score_codex":0.26962,"about_ca_topic_score_gemma":0.67792654,"teacher_disagreement_score":0.26962,"about_ca_system_score_codex":0.004070304,"about_ca_system_score_gemma":0.0028946344,"threshold_uncertainty_score":0.5361013},"labels":[],"label_agreement":null},{"id":"W3084371909","doi":"10.1109/jiot.2020.3022353","title":"Video Scene Segmentation Using Tensor-Train Faster-RCNN for Multimedia IoT Systems","year":2020,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Francis Xavier University; McMaster University","funders":"Department of Science and Technology of Sichuan Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Segmentation; Convolutional neural network; Computer vision; Deep learning; Edge device; Image segmentation; Enhanced Data Rates for GSM Evolution; Tensor (intrinsic definition); Data compression; Edge computing; Pattern recognition (psychology); Multimedia; Cloud computing","score_opus":0.07822525596345084,"score_gpt":0.3272155593089267,"score_spread":0.24899030334547587,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3084371909","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":[0.04845222,0.0005239771,0.9457282,0.00022487069,0.000077349,0.000060835497,0.00020096241,0.002572304,0.0021592898],"genre_scores_gemma":[0.58752227,0.0006134017,0.4053256,0.0001745923,0.000052049294,0.000084163075,0.0011483757,0.00025929394,0.004820262],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998357,0.00002264779,0.000010513575,0.000049686987,0.000048340127,0.000033053308],"domain_scores_gemma":[0.9997924,0.000037838985,0.000033837103,0.00003843838,0.0000776695,0.000019756591],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00032548298,0.00079536607,0.00046990075,0.0005588465,0.0003795208,0.0005499402,0.0008085169,0.0005457066,0.0014836073],"category_scores_gemma":[0.00094124285,0.00031914213,0.0006616864,0.00059320504,0.00027312964,0.0010275225,0.00053815666,0.0007475933,0.00043181467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034366432,0.00012469626,0.0026655123,0.00013784229,0.00009467809,0.00016694545,0.00011336823,0.553271,0.04821325,0.005657139,0.0042899842,0.38492194],"study_design_scores_gemma":[0.0000015673759,0.000009569511,0.00015323328,0.0000025018853,0.0000047906524,0.000010266518,0.0000039494716,0.9967193,0.002429369,0.00040903653,0.00025332608,0.0000031245218],"about_ca_topic_score_codex":0.0241392,"about_ca_topic_score_gemma":0.024342757,"teacher_disagreement_score":0.0241392,"about_ca_system_score_codex":0.001051417,"about_ca_system_score_gemma":0.0009669293,"threshold_uncertainty_score":0.047997355},"labels":[],"label_agreement":null},{"id":"W3086556267","doi":"10.3390/jimaging6090095","title":"Deep Learning-Based Crowd Scene Analysis Survey","year":2020,"lang":"en","type":"review","venue":"Journal of Imaging","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary; Memorial University of Newfoundland; Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Metric (unit); Artificial intelligence; Crowd psychology; Breakout; Crowd simulation; Deep learning; Track (disk drive); Convolutional neural network; Computer vision; Machine learning; Computer security","score_opus":0.0549281263574053,"score_gpt":0.3706207690904379,"score_spread":0.3156926427330326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3086556267","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":[0.0037315476,0.9270589,0.049808495,0.0022891476,0.0010228942,0.00007874653,0.00043768232,0.00043269742,0.015139923],"genre_scores_gemma":[0.030121667,0.9265788,0.02922597,0.001005892,0.0011808864,0.00009967764,0.0013170249,0.000121990815,0.010348189],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999537,0.000072136405,0.000033766573,0.00013472202,0.00018116646,0.000041365514],"domain_scores_gemma":[0.99883515,0.00053935626,0.00006149983,0.000053840675,0.00045103295,0.000059211416],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010692354,0.0012491376,0.0009111918,0.0022172472,0.00027333832,0.0010739409,0.0016417041,0.0009203777,0.003780365],"category_scores_gemma":[0.0028732398,0.00048749032,0.0007082627,0.0025955837,0.00039709063,0.001785433,0.0010387182,0.0011184046,0.002374529],"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":[0.000037502043,0.000084954016,0.0007419292,0.0025730347,0.00007840412,0.00004177822,0.000052110343,0.0042044264,0.0005739191,0.0045848642,0.03305665,0.9539704],"study_design_scores_gemma":[0.000026067288,0.00024865408,0.005485381,0.004948974,0.00035551234,0.00090698275,0.00030315606,0.04554774,0.00512221,0.015343399,0.9215993,0.00011269701],"about_ca_topic_score_codex":0.0064569064,"about_ca_topic_score_gemma":0.0053908513,"teacher_disagreement_score":0.0064569064,"about_ca_system_score_codex":0.000775432,"about_ca_system_score_gemma":0.0015968394,"threshold_uncertainty_score":0.012838662},"labels":[],"label_agreement":null},{"id":"W3089819169","doi":"10.1007/s11042-020-09838-x","title":"Recommendations for evaluating the performance of background subtraction algorithms for surveillance systems","year":2020,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Background subtraction; Algorithm; Key (lock); Process (computing); Subtraction; Task (project management); Set (abstract data type); Machine learning; Data mining; Artificial intelligence; Computer security; Pixel; Arithmetic; Systems engineering","score_opus":0.22395163314056452,"score_gpt":0.39643693846191963,"score_spread":0.17248530532135511,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3089819169","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":[0.04451522,0.054445483,0.65796095,0.074457936,0.009165913,0.012819943,0.028310943,0.03611468,0.08220888],"genre_scores_gemma":[0.07193762,0.014950934,0.8589073,0.0045522866,0.00067008246,0.0037588421,0.01314685,0.0016728474,0.030403337],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.98406166,0.0049606124,0.0025466562,0.00082710077,0.0067182756,0.0008857161],"domain_scores_gemma":[0.85703754,0.028612835,0.005063315,0.0066726473,0.09952462,0.0030891208],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.019879617,0.003247624,0.0015142505,0.006320016,0.0019234072,0.0052135657,0.0054646805,0.0050114277,0.022998836],"category_scores_gemma":[0.12088887,0.0013483969,0.0020671268,0.004458542,0.00064917863,0.0054855193,0.0014180196,0.0023919926,0.014139658],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011326262,0.0011335101,0.012366308,0.0034742947,0.00029208278,0.00029695244,0.00027672676,0.0126815885,0.0172907,0.003508995,0.24188514,0.7056611],"study_design_scores_gemma":[0.0022132231,0.0047954572,0.06907611,0.025205985,0.0029261503,0.0014834851,0.0044786055,0.17969683,0.13847488,0.038785093,0.53150636,0.0013577932],"about_ca_topic_score_codex":0.04007937,"about_ca_topic_score_gemma":0.041429844,"teacher_disagreement_score":0.04007937,"about_ca_system_score_codex":0.0031337705,"about_ca_system_score_gemma":0.006354592,"threshold_uncertainty_score":0.105134785},"labels":[],"label_agreement":null},{"id":"W3090949667","doi":"10.1109/icip40778.2020.9190957","title":"Reliable Temporally Consistent Feature Adaptation for Visual Object Tracking","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; BitTorrent tracker; Video tracking; Reliability (semiconductor); Feature (linguistics); Tracking (education); Adaptation (eye); Consistency (knowledge bases); Pattern recognition (psychology); Eye tracking; Object (grammar); Computer vision; Filter (signal processing); Machine learning","score_opus":0.0731385545903369,"score_gpt":0.31813898331490775,"score_spread":0.24500042872457084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3090949667","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0072774324,0.00031113022,0.9907697,0.0000412375,0.000030629624,0.000017420472,0.00008518726,0.0011329909,0.00033429064],"genre_scores_gemma":[0.5389142,0.00062261574,0.45562756,0.00023378193,0.00019758205,0.00018344399,0.0012895628,0.0005447129,0.002386405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988206,0.00023207485,0.000049250266,0.0004117968,0.00039538715,0.000090772504],"domain_scores_gemma":[0.99789846,0.00074572844,0.00031287255,0.00049703044,0.00048149272,0.00006438689],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001843177,0.0009747499,0.0011225075,0.0010706837,0.00038951435,0.0006898738,0.0013883329,0.0008951771,0.00085235544],"category_scores_gemma":[0.007296417,0.000498973,0.0007017145,0.0018570052,0.0005156541,0.0012296336,0.00091088586,0.0015677619,0.0007744395],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003382348,0.00012803948,0.0030238957,0.00012001441,0.00014833969,0.00012033738,0.00012759469,0.37983495,0.033087276,0.006016086,0.010019608,0.5670357],"study_design_scores_gemma":[0.000010302757,0.00003067662,0.00074093527,0.0000056035155,0.000012828226,0.000049131744,0.0000055650594,0.99218583,0.0033840628,0.0024311554,0.0011319862,0.000011880678],"about_ca_topic_score_codex":0.0066014724,"about_ca_topic_score_gemma":0.0062271114,"teacher_disagreement_score":0.0066014724,"about_ca_system_score_codex":0.0006915385,"about_ca_system_score_gemma":0.001102254,"threshold_uncertainty_score":0.013126135},"labels":[],"label_agreement":null},{"id":"W3092050193","doi":"10.1016/j.engappai.2020.103974","title":"Robust RGB-D tracking via compact CNN features","year":2020,"lang":"en","type":"article","venue":"Engineering Applications of Artificial Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Outstanding Youth Science Fund Project of National Natural Science Foundation of China; State Key Laboratory of Robotics and System; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; RGB color model; Computer vision; Convolutional neural network; Tracking (education); Feature (linguistics); Benchmark (surveying); Pattern recognition (psychology); Eye tracking","score_opus":0.06975407455061977,"score_gpt":0.29649578179978303,"score_spread":0.22674170724916326,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092050193","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018132,0.0005175904,0.97624534,0.00012478889,0.00011731152,0.000039682134,0.00026144765,0.0013490999,0.0032126552],"genre_scores_gemma":[0.5159291,0.0011168595,0.46915537,0.00034017157,0.00012876514,0.000112796995,0.001496313,0.00026954358,0.011451108],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996517,0.00002595203,0.000015344543,0.0001184937,0.00014176998,0.00004670844],"domain_scores_gemma":[0.99967647,0.000060538783,0.000052172916,0.00010132317,0.00009127233,0.000018156492],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037638555,0.00090413657,0.0006845546,0.00074189604,0.00024201862,0.00087793113,0.00081161765,0.00069741806,0.001898901],"category_scores_gemma":[0.0011498141,0.0005522612,0.0004710399,0.0010366377,0.0002600141,0.0010405465,0.0012035433,0.0006639748,0.0014445158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003185752,0.00009759876,0.0017837018,0.000113979215,0.00010036783,0.00009973198,0.000038576673,0.097132355,0.12687245,0.0056010373,0.006559114,0.7612825],"study_design_scores_gemma":[0.000009751963,0.000042858133,0.0018889809,0.000017898721,0.000023525005,0.00013613599,0.000007458288,0.9681221,0.023820104,0.0024016441,0.0035161402,0.000013366944],"about_ca_topic_score_codex":0.0059509757,"about_ca_topic_score_gemma":0.009185831,"teacher_disagreement_score":0.0059509757,"about_ca_system_score_codex":0.00057261175,"about_ca_system_score_gemma":0.0006320354,"threshold_uncertainty_score":0.011832714},"labels":[],"label_agreement":null},{"id":"W3092677312","doi":"10.1109/icpr48806.2021.9412206","title":"An Empirical Analysis of Visual Features for Multiple Object Tracking in Urban Scenes","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Artificial intelligence; Bhattacharyya distance; Computer science; Pattern recognition (psychology); Histogram; Convolutional neural network; Similarity (geometry); Detector; Identification (biology); Computer vision; Object (grammar); Bounding overwatch; Image (mathematics)","score_opus":0.059927644266223226,"score_gpt":0.4134357856683556,"score_spread":0.3535081414021324,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3092677312","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":[0.95691216,0.0016612612,0.03787204,0.0003035282,0.000056191057,0.000065378816,0.0015827386,0.0003664023,0.0011802242],"genre_scores_gemma":[0.9947931,0.00013110971,0.0030229562,0.00001975747,0.00001910957,0.000018223962,0.0016988156,0.00003188604,0.00026501817],"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979278,0.000575885,0.00013068365,0.0006910106,0.00046139566,0.00021306975],"domain_scores_gemma":[0.97395384,0.018292248,0.002925822,0.0023860184,0.0018135699,0.0006284945],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005209118,0.0005769809,0.0007531184,0.0025972493,0.0006555164,0.0012166058,0.00086710975,0.00090901565,0.0012554228],"category_scores_gemma":[0.028440988,0.00024628968,0.000714844,0.0021071325,0.001094996,0.002030329,0.0007299059,0.0011611881,0.0003795365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001142014,0.0003757783,0.7355535,0.00035656526,0.000608583,0.00058006245,0.00030144688,0.10623242,0.0035141325,0.002302669,0.006625605,0.14240712],"study_design_scores_gemma":[0.000030551735,0.00032819988,0.39041546,0.00006647604,0.00013238078,0.0012609038,0.00033214383,0.5992395,0.0026226947,0.003676278,0.0018300633,0.00006527077],"about_ca_topic_score_codex":0.004453933,"about_ca_topic_score_gemma":0.0043574763,"teacher_disagreement_score":0.005209118,"about_ca_system_score_codex":0.0009932923,"about_ca_system_score_gemma":0.00048560137,"threshold_uncertainty_score":0.02754879},"labels":[],"label_agreement":null},{"id":"W3093349865","doi":"10.1155/2020/8843113","title":"Person Detection for an Orthogonally Placed Monocular Camera","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"European Social Fund; European Regional Development Fund; Univerzita Pardubice","keywords":"Computer science; Convolutional neural network; Classifier (UML); Histogram; Real-time computing; Public transport; Flow network; Artificial intelligence; Machine learning; Engineering; Transport engineering","score_opus":0.03460574420929887,"score_gpt":0.2949771930132966,"score_spread":0.26037144880399776,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3093349865","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.42858106,0.0012212226,0.5318404,0.00043600646,0.00057662127,0.00041747073,0.0072786105,0.014485593,0.015163021],"genre_scores_gemma":[0.72716683,0.00057995354,0.25302607,0.0003317306,0.0001363587,0.00018127724,0.007951512,0.00024394543,0.010382256],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992704,0.00006420543,0.000014427683,0.00028576286,0.00023746057,0.00012786553],"domain_scores_gemma":[0.9997719,0.000023511917,0.000020478554,0.000053417552,0.000097602635,0.000033100783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033093267,0.0007630883,0.00069724227,0.0012544988,0.00026866875,0.0005208024,0.0006539059,0.0007013675,0.0060185078],"category_scores_gemma":[0.00077655265,0.0003264196,0.0006091804,0.0007028319,0.00016867109,0.00061963696,0.0006145677,0.00039607572,0.0035874357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015205592,0.00047208928,0.008028403,0.00029247094,0.00020367802,0.00049154303,0.000066092165,0.028789887,0.15197253,0.0011802777,0.027480923,0.77950144],"study_design_scores_gemma":[0.000071814764,0.0003197585,0.03423114,0.000045043165,0.00008666943,0.0011656064,0.00007976188,0.8458767,0.106962964,0.00091188506,0.010189469,0.000059200556],"about_ca_topic_score_codex":0.008964736,"about_ca_topic_score_gemma":0.010348444,"teacher_disagreement_score":0.008964736,"about_ca_system_score_codex":0.00050315907,"about_ca_system_score_gemma":0.00052713987,"threshold_uncertainty_score":0.020133972},"labels":[],"label_agreement":null},{"id":"W3094855230","doi":"10.48550/arxiv.2010.14802","title":"SFU-Store-Nav: A Multimodal Dataset for Indoor Human Navigation","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Robotics; Computer science; Robot; Gesture; Set (abstract data type); Orientation (vector space); Data set; Motion (physics); Human–computer interaction; Computer vision; Human–robot interaction","score_opus":0.16615476460214115,"score_gpt":0.2680623197593545,"score_spread":0.10190755515721336,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3094855230","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03820115,0.0020212424,0.009862114,0.00052315684,0.00046268041,0.00044344226,0.9308061,0.0103910025,0.0072891405],"genre_scores_gemma":[0.033561956,0.00029169727,0.010489477,0.00017320986,0.000054967004,0.00046345958,0.9528939,0.00022303671,0.0018482989],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99903417,0.00021542265,0.00008455387,0.00027906147,0.00024045384,0.0001463897],"domain_scores_gemma":[0.9990355,0.00014992806,0.00006509867,0.0002986109,0.00031010315,0.00014079199],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005126357,0.002434932,0.00153634,0.0020407925,0.0010140361,0.00078389584,0.0025375506,0.0026735868,0.007685571],"category_scores_gemma":[0.0020803637,0.00039430882,0.0012436522,0.0029171575,0.00048158452,0.00087145244,0.001959041,0.001150129,0.011219909],"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":[0.0010675597,0.00067749334,0.01511563,0.0024326378,0.0003301971,0.0006776145,0.00048212026,0.0055569364,0.00585496,0.0010084047,0.88441515,0.082381256],"study_design_scores_gemma":[0.0008261554,0.0012843051,0.14941432,0.0012502712,0.00039243465,0.0028097683,0.003376708,0.0639209,0.015085057,0.0074059027,0.7536593,0.0005748689],"about_ca_topic_score_codex":0.04392542,"about_ca_topic_score_gemma":0.11202493,"teacher_disagreement_score":0.04392542,"about_ca_system_score_codex":0.00084737275,"about_ca_system_score_gemma":0.0013164518,"threshold_uncertainty_score":0.08733946},"labels":[],"label_agreement":null},{"id":"W3098328330","doi":"10.22215/etd/2020-14287","title":"Improving the Performance of Video Processing on Hadoop Clusters","year":2020,"lang":"en","type":"dissertation","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; SPARK (programming language); Big data; Data processing; Video processing; Data-intensive computing; Database; Real-time computing; Data mining; Artificial intelligence","score_opus":0.02077544453054981,"score_gpt":0.2845855546082132,"score_spread":0.2638101100776634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098328330","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":[0.46167287,0.0025558998,0.43326983,0.0027336075,0.0016454796,0.0010568773,0.001858581,0.03974454,0.0554624],"genre_scores_gemma":[0.700205,0.0010518896,0.28653327,0.00030973656,0.00015185519,0.00032805614,0.0028830643,0.0013603924,0.007176731],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99767584,0.00030770455,0.00012635904,0.0004203156,0.0010817193,0.00038819492],"domain_scores_gemma":[0.9956391,0.0010005358,0.00010020229,0.00065315183,0.0022225266,0.00038445732],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024475292,0.0010218852,0.0006552343,0.0010005864,0.0016894918,0.0022310491,0.0021189132,0.0005338111,0.0019478146],"category_scores_gemma":[0.0076913144,0.00039788202,0.00056255946,0.0015239321,0.0004839985,0.002559731,0.0013150878,0.0014406603,0.0012152583],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019093322,0.00097361725,0.00924472,0.000843112,0.00025776256,0.0005321466,0.0011360843,0.21447068,0.11362515,0.013597123,0.08500612,0.5584042],"study_design_scores_gemma":[0.00015327928,0.0003293273,0.0052062,0.000060853406,0.00006173042,0.00012642317,0.00066173536,0.8761071,0.08635232,0.008188807,0.022659352,0.00009285109],"about_ca_topic_score_codex":0.010675653,"about_ca_topic_score_gemma":0.008730445,"teacher_disagreement_score":0.010675653,"about_ca_system_score_codex":0.0017322182,"about_ca_system_score_gemma":0.0029156753,"threshold_uncertainty_score":0.021227002},"labels":[],"label_agreement":null},{"id":"W3098579597","doi":"10.48550/arxiv.2011.07590","title":"MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models","year":2020,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; University of Waterloo","funders":"","keywords":"Lidar; Octree; Computer science; Entropy (arrow of time); Conditional entropy; Data compression; ENCODE; Artificial intelligence; Computer vision; Algorithm; Remote sensing; Principle of maximum entropy; Geology; Physics","score_opus":0.1951961842901658,"score_gpt":0.2289645217330441,"score_spread":0.0337683374428783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3098579597","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":[0.061817046,0.00083583436,0.9310484,0.00038364614,0.00012521623,0.000086577566,0.0008492264,0.00286172,0.0019924315],"genre_scores_gemma":[0.6268897,0.0008694329,0.36214456,0.0003739639,0.00016634644,0.00027445672,0.0034401745,0.00034728477,0.005494076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997186,0.000030150482,0.000016295535,0.000048878523,0.00015529341,0.00003069789],"domain_scores_gemma":[0.9996107,0.00014523872,0.000043005177,0.00009077225,0.0000886614,0.0000216661],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004542209,0.00066479284,0.00056528026,0.00091544277,0.00025263845,0.00067164033,0.0011586215,0.00051298423,0.0017447021],"category_scores_gemma":[0.0019890554,0.00023446801,0.00043504356,0.0011535909,0.00034401572,0.0019182074,0.0011792335,0.00112057,0.00054185226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036412734,0.00017927823,0.0019562035,0.00010888706,0.00006686857,0.00021011401,0.00012553135,0.30523932,0.02201162,0.011176683,0.00842453,0.6501368],"study_design_scores_gemma":[0.000010252536,0.00004378115,0.0003192882,0.000009026819,0.000007553159,0.00005468152,0.000014551488,0.9875,0.00717443,0.0034644213,0.0013942424,0.000007792833],"about_ca_topic_score_codex":0.0031184324,"about_ca_topic_score_gemma":0.0045069014,"teacher_disagreement_score":0.0031184324,"about_ca_system_score_codex":0.00054940925,"about_ca_system_score_gemma":0.00070707814,"threshold_uncertainty_score":0.006200552},"labels":[],"label_agreement":null},{"id":"W3099842735","doi":"10.1016/j.dib.2020.106539","title":"SFU-store-nav: A multimodal dataset for indoor human navigation","year":2020,"lang":"en","type":"article","venue":"Data in Brief","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Simon Fraser University","funders":"Simon Fraser University","keywords":"Robotics; Artificial intelligence; Computer science; Gesture; Robot; Set (abstract data type); Orientation (vector space); Human–computer interaction; Data set; Motion (physics); Human–robot interaction; Computer vision","score_opus":0.12629043120579359,"score_gpt":0.3828896222944214,"score_spread":0.25659919108862783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3099842735","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03878398,0.002166597,0.010958349,0.00052349333,0.0005040946,0.0005262576,0.93012875,0.009148721,0.0072596236],"genre_scores_gemma":[0.032757044,0.00031623276,0.011695175,0.0001805826,0.00006009161,0.0005566859,0.95221454,0.00021024079,0.002009382],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.998936,0.00022528504,0.00009412026,0.000299528,0.0002890401,0.00015610961],"domain_scores_gemma":[0.99891484,0.00017080527,0.00007937693,0.0003090198,0.00037364804,0.00015224687],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056860433,0.0023614406,0.0014878659,0.0021734226,0.0010596799,0.0007872548,0.0025531969,0.0026094797,0.006786087],"category_scores_gemma":[0.002094201,0.0003781244,0.0012278056,0.002945678,0.00049761083,0.00085320417,0.0018568989,0.0011635498,0.01024807],"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":[0.00097629137,0.00075335137,0.016566135,0.0027605467,0.00034685177,0.00067397894,0.0005064701,0.0058421185,0.0065653548,0.0010327658,0.8752743,0.08870189],"study_design_scores_gemma":[0.00073608814,0.0012748451,0.16733386,0.0013229523,0.00037891057,0.0026548917,0.0035035864,0.058761142,0.01648082,0.006080398,0.7408946,0.00057780906],"about_ca_topic_score_codex":0.046901185,"about_ca_topic_score_gemma":0.12213564,"teacher_disagreement_score":0.046901185,"about_ca_system_score_codex":0.00093364064,"about_ca_system_score_gemma":0.0014542387,"threshold_uncertainty_score":0.093256414},"labels":[],"label_agreement":null},{"id":"W3104886315","doi":"10.1155/2020/8848874","title":"A New Video-Based Crash Detection Method: Balancing Speed and Accuracy Using a Feature Fusion Deep Learning Framework","year":2020,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Government of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Crash; Convolutional neural network; Artificial intelligence; Feature (linguistics); Deep learning; Computer vision; Feature extraction; Machine learning; Pattern recognition (psychology)","score_opus":0.018786301480632026,"score_gpt":0.3128654528932765,"score_spread":0.29407915141264446,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3104886315","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09504603,0.00093991985,0.8986172,0.00018659898,0.00011510094,0.00013148249,0.00023065161,0.0033171778,0.0014158244],"genre_scores_gemma":[0.74586487,0.0005790645,0.24911846,0.00016694321,0.000096464915,0.000101717065,0.00065269193,0.00010052053,0.003319228],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99948287,0.00005154153,0.00003143671,0.00017253018,0.00018231422,0.00007932297],"domain_scores_gemma":[0.9994823,0.00007926425,0.000060277544,0.000054498774,0.00028424032,0.000039410486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007180554,0.0010204463,0.00075865845,0.0018097605,0.000233694,0.00054599094,0.0012060411,0.0007608298,0.0011352678],"category_scores_gemma":[0.0012380147,0.00031734357,0.00056935434,0.00069352414,0.00027190667,0.0015014774,0.0008776184,0.00091154437,0.00042589597],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040090832,0.00026627313,0.0059617446,0.00015018557,0.00015032056,0.00017950911,0.00007882602,0.04972139,0.08052498,0.001413202,0.0032261377,0.8579266],"study_design_scores_gemma":[0.000013530208,0.0001454131,0.0027468528,0.0000093096105,0.000050253562,0.00012478021,0.000023091445,0.9713874,0.02406535,0.00042625188,0.0009890904,0.000018759394],"about_ca_topic_score_codex":0.005943472,"about_ca_topic_score_gemma":0.006411772,"teacher_disagreement_score":0.005943472,"about_ca_system_score_codex":0.0006728923,"about_ca_system_score_gemma":0.0006191741,"threshold_uncertainty_score":0.011817753},"labels":[],"label_agreement":null},{"id":"W31066653","doi":"10.1080/22423982.2018.1517581","title":"Implementation and Evaluation of an Adaptive CCTV Display System for Incident Detection for the Highways Agency","year":2009,"lang":"en","type":"article","venue":"16th ITS World Congress and Exhibition on Intelligent Transport Systems and ServicesITS AmericaERTICOITS Japan","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; Polar Knowledge Canada","keywords":"Computer science; Computer vision; Zoom; Constant false alarm rate; Closed circuit; Artificial intelligence; Probabilistic logic; Real-time computing; Engineering; Telecommunications","score_opus":0.04887048324106865,"score_gpt":0.32674163784184035,"score_spread":0.2778711546007717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W31066653","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":[0.98470956,0.00020276035,0.004315074,0.0004377375,0.00005742598,0.004580905,0.00032369077,0.0005216925,0.004851157],"genre_scores_gemma":[0.9750088,0.00034553773,0.01988639,0.00023301519,0.000020739897,0.0018425542,0.00040048538,0.000048417918,0.0022140602],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9947127,0.0028449243,0.00032918795,0.00041890724,0.0011907978,0.00050355354],"domain_scores_gemma":[0.9893801,0.0044400827,0.0006497004,0.0006220307,0.0040330393,0.0008749498],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007550177,0.0006005284,0.00046642838,0.00095355493,0.0014170489,0.0016641238,0.0017413062,0.0009851505,0.0022031728],"category_scores_gemma":[0.02442368,0.00037113222,0.000628338,0.0006680485,0.00060344214,0.000954367,0.0009771441,0.0007930951,0.0004087326],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.010335444,0.041281242,0.077652305,0.0034773066,0.00061977125,0.0008396676,0.020382684,0.019606821,0.021527711,0.0010860439,0.008936668,0.79425436],"study_design_scores_gemma":[0.011729794,0.12795344,0.6332241,0.0013162547,0.0025352344,0.00039836497,0.036586702,0.10495823,0.032962475,0.00077476905,0.046991233,0.0005694149],"about_ca_topic_score_codex":0.23513393,"about_ca_topic_score_gemma":0.22740485,"teacher_disagreement_score":0.23513393,"about_ca_system_score_codex":0.0065014507,"about_ca_system_score_gemma":0.009905408,"threshold_uncertainty_score":0.4675306},"labels":[],"label_agreement":null},{"id":"W3109991383","doi":"10.1007/978-3-030-58536-5_36","title":"V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":449,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Viewpoints; Perception; Artificial intelligence; Bandwidth (computing); Joint (building); Feature (linguistics); Computer vision; Range (aeronautics); Real-time computing; Telecommunications; Engineering","score_opus":0.03757137983663022,"score_gpt":0.2797818468397802,"score_spread":0.24221046700314997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3109991383","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0049213427,0.0010005578,0.9722239,0.00015344593,0.0006782786,0.000071493654,0.00089982175,0.010313762,0.009737459],"genre_scores_gemma":[0.41778165,0.0021143637,0.506105,0.00044260867,0.00057323993,0.00048990233,0.008888494,0.001966824,0.06163798],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996234,0.000057575577,0.000014879342,0.00010586781,0.00014414465,0.00005404306],"domain_scores_gemma":[0.9997757,0.0000727029,0.000010895636,0.00006067884,0.00006168644,0.000018346602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044117597,0.001234465,0.000979696,0.0004902911,0.00037490905,0.0010883289,0.00215625,0.0009854537,0.013276812],"category_scores_gemma":[0.001061894,0.0003807817,0.00033206344,0.0008072632,0.00031741196,0.0012755345,0.0014878722,0.0010103129,0.0042565693],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006189415,0.00014955917,0.00048371,0.00022552547,0.0000935425,0.0001606194,0.000069536094,0.1395086,0.014261881,0.017039843,0.11370176,0.7136864],"study_design_scores_gemma":[0.000038422662,0.00007732739,0.00023036389,0.000025612078,0.0000115008625,0.00008347048,0.000018742057,0.95198745,0.0077058445,0.011113053,0.02868915,0.000019037203],"about_ca_topic_score_codex":0.00571372,"about_ca_topic_score_gemma":0.0057033896,"teacher_disagreement_score":0.013276812,"about_ca_system_score_codex":0.00038104574,"about_ca_system_score_gemma":0.0007481677,"threshold_uncertainty_score":0.044415355},"labels":[],"label_agreement":null},{"id":"W3110279291","doi":"10.1109/ccece47787.2020.9255776","title":"MODSiam: Moving Object Detection using Siamese Networks","year":2020,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Artificial intelligence; Computer science; Object detection; Recall rate; Benchmark (surveying); Frame (networking); Computer vision; Object (grammar); Frame rate; Convolutional neural network; Pattern recognition (psychology); Precision and recall; Class (philosophy); Backbone network","score_opus":0.05102107987842786,"score_gpt":0.2897830879774231,"score_spread":0.23876200809899523,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110279291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.062924534,0.0015884541,0.9019965,0.0004790713,0.0002627357,0.00030601159,0.0014644357,0.025538636,0.0054396796],"genre_scores_gemma":[0.40661287,0.00076916604,0.57307494,0.000401936,0.00016042893,0.00024992475,0.0056542554,0.0005006866,0.012575843],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99969256,0.000048915626,0.0000138928535,0.00012031368,0.00008659326,0.000037637164],"domain_scores_gemma":[0.9995679,0.00012270974,0.000048668604,0.00009855707,0.00012823839,0.00003399577],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010328827,0.0012398373,0.0006853405,0.0015254384,0.0002841899,0.0009261801,0.0019107161,0.0009992408,0.0032247151],"category_scores_gemma":[0.0017028068,0.0003838904,0.0007458161,0.0007479713,0.00040085134,0.0012995494,0.0010262326,0.0010680397,0.0015632132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040176237,0.00033857732,0.0038101394,0.00019179763,0.0003527806,0.00016722329,0.000051188064,0.21792126,0.017996624,0.00580484,0.021898594,0.7310653],"study_design_scores_gemma":[0.000013277297,0.000055705583,0.00052935816,0.0000058577457,0.000012529339,0.00005470759,0.0000059014424,0.99198145,0.003680903,0.0019167358,0.001735687,0.0000079135425],"about_ca_topic_score_codex":0.013974377,"about_ca_topic_score_gemma":0.017243756,"teacher_disagreement_score":0.013974377,"about_ca_system_score_codex":0.00095703953,"about_ca_system_score_gemma":0.0011119345,"threshold_uncertainty_score":0.027786076},"labels":[],"label_agreement":null},{"id":"W3110876735","doi":"10.1186/s13640-021-00562-6","title":"Exploiting prunability for person re-identification","year":2021,"lang":"en","type":"article","venue":"EURASIP Journal on Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Pruning; Convolutional neural network; Artificial intelligence; Deep learning; Identification (biology); Feature (linguistics); FLOPS; Machine learning; Computational complexity theory; Pattern recognition (psychology); Feature extraction; Domain (mathematical analysis); Algorithm; Mathematics","score_opus":0.06440125554445895,"score_gpt":0.34867574507114063,"score_spread":0.28427448952668166,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3110876735","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17424534,0.0011954173,0.8157954,0.00041527685,0.00012636947,0.00008706585,0.00016425307,0.003884994,0.004085862],"genre_scores_gemma":[0.8151981,0.0005023834,0.17800455,0.00024344467,0.000058597634,0.00007996532,0.0005658309,0.00027606447,0.0050710742],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991485,0.00014896796,0.00004979652,0.00017143997,0.00033542284,0.00014594644],"domain_scores_gemma":[0.9976503,0.00081000355,0.00021568508,0.0007398491,0.0005120012,0.00007214327],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010062559,0.0009749514,0.00070988445,0.0008650128,0.00044814878,0.0007826858,0.0016257456,0.0010063766,0.0024870867],"category_scores_gemma":[0.0059524737,0.00042328332,0.0005289617,0.0005017455,0.0005543306,0.001942842,0.0013646296,0.0011159148,0.0012905102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009089025,0.00017194997,0.0052828398,0.00023632527,0.00015997645,0.0009154303,0.00024789455,0.2567055,0.09460276,0.00972532,0.0064757606,0.6245674],"study_design_scores_gemma":[0.000013479969,0.0001598535,0.0013385303,0.00003028715,0.00003928917,0.00040885338,0.00005512017,0.9453908,0.043977153,0.0046036076,0.0039677387,0.000015286012],"about_ca_topic_score_codex":0.0026458497,"about_ca_topic_score_gemma":0.0042054257,"teacher_disagreement_score":0.0026458497,"about_ca_system_score_codex":0.00043841716,"about_ca_system_score_gemma":0.00064636435,"threshold_uncertainty_score":0.008320153},"labels":[],"label_agreement":null},{"id":"W3111262543","doi":"10.1109/tcsvt.2020.3042559","title":"Deep Variation Transformation Network for Foreground Detection","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Natural Sciences and Engineering Research Council of Canada; Chongqing Research Program of Basic Research and Frontier Technology; Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Pixel; Artificial intelligence; Computer science; Pattern recognition (psychology); Foreground detection; Benchmark (surveying); Transformation (genetics); Deep learning; Computer vision; Variation (astronomy); Classifier (UML); Background subtraction","score_opus":0.0344171522939143,"score_gpt":0.263994802236814,"score_spread":0.2295776499428997,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3111262543","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02424602,0.0010848958,0.96884537,0.00028880796,0.00007750207,0.00004588059,0.00035235603,0.0025913601,0.0024678682],"genre_scores_gemma":[0.75165194,0.0011296879,0.2329059,0.0005050148,0.00015216516,0.00011930021,0.0025712105,0.00039274836,0.010571974],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995689,0.00006433612,0.00001510684,0.00017127412,0.000105274274,0.00007519467],"domain_scores_gemma":[0.99965274,0.0001249132,0.00005790491,0.000049880793,0.00009063014,0.000023847675],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00052421325,0.0012993662,0.0009370552,0.0009424475,0.00030485587,0.000785188,0.0015493968,0.0008325216,0.002064799],"category_scores_gemma":[0.001679104,0.00047304176,0.0008077556,0.0010529782,0.0005654581,0.0011598725,0.0010071133,0.0014334222,0.0006829772],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029163732,0.00010590029,0.0025966333,0.00014130972,0.00013320727,0.00020692994,0.00008629971,0.38617215,0.013041758,0.010723506,0.008034817,0.5784658],"study_design_scores_gemma":[0.0000033437263,0.000013536346,0.00023607031,0.0000054611155,0.000009559498,0.000036069425,0.0000043453274,0.9935081,0.002016058,0.0035331952,0.0006295121,0.0000047824],"about_ca_topic_score_codex":0.0080832215,"about_ca_topic_score_gemma":0.009451148,"teacher_disagreement_score":0.0080832215,"about_ca_system_score_codex":0.0012540036,"about_ca_system_score_gemma":0.0008023351,"threshold_uncertainty_score":0.016072333},"labels":[],"label_agreement":null},{"id":"W3116671538","doi":"10.22215/etd/2009-08897","title":"Using thermal imaging to promote independent living","year":2009,"lang":"en","type":"dissertation","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University; Canadian Heritage; Library and Archives Canada","funders":"","keywords":"Stove; Population; Engineering; Combustor; Computer science; Waste management; Environmental health; Medicine; Combustion","score_opus":0.03146175419259803,"score_gpt":0.3423994969031618,"score_spread":0.31093774271056374,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3116671538","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.34398058,0.0040664533,0.5475373,0.001548092,0.00066563056,0.00023866161,0.0001438701,0.0026536968,0.09916573],"genre_scores_gemma":[0.7728948,0.0026968983,0.16864878,0.00045243348,0.00016523899,0.0003473632,0.00014207889,0.0005156704,0.054136835],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979764,0.00004317449,0.0000065073104,0.00005137871,0.00006790007,0.00003328372],"domain_scores_gemma":[0.99959165,0.00013394418,0.00004553216,0.00009388502,0.000075664466,0.00005938174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040554962,0.00036179056,0.0001803008,0.00039958963,0.00042994512,0.00078793097,0.00045927352,0.000409202,0.007208262],"category_scores_gemma":[0.0011002325,0.00018826158,0.0002947095,0.00023975589,0.0004804284,0.0010412495,0.001329957,0.00053739466,0.0015981147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004405117,0.0005349622,0.0011609263,0.00029424243,0.00004069134,0.00019341355,0.0013106607,0.0032004828,0.5457643,0.009998584,0.0074469754,0.42961425],"study_design_scores_gemma":[0.00018017697,0.0019474578,0.02309415,0.00043145157,0.00024100229,0.0017859914,0.0016488895,0.052035164,0.74641955,0.025759226,0.14624086,0.00021608063],"about_ca_topic_score_codex":0.0003479878,"about_ca_topic_score_gemma":0.0011867932,"teacher_disagreement_score":0.007208262,"about_ca_system_score_codex":0.00017964322,"about_ca_system_score_gemma":0.00026946605,"threshold_uncertainty_score":0.024114013},"labels":[],"label_agreement":null},{"id":"W3117941406","doi":"10.1109/tmm.2022.3142398","title":"STNet: Scale Tree Network With Multi-Level Auxiliator for Crowd Counting","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":54,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of Guelph","funders":"University of Guelph","keywords":"Computer science; Tree (set theory); Scale (ratio); Artificial intelligence; Machine learning; Data mining; Pattern recognition (psychology)","score_opus":0.05101680530524946,"score_gpt":0.2914439014463287,"score_spread":0.24042709614107924,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3117941406","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06495651,0.0007449062,0.92249703,0.00039970732,0.0002487423,0.00012989355,0.0005696003,0.005036633,0.0054169027],"genre_scores_gemma":[0.66926533,0.0005536339,0.31762397,0.00052161264,0.00017420016,0.00022985828,0.0016141017,0.00039380463,0.009623476],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997687,0.000035564444,0.000008913328,0.000078804354,0.000059518527,0.000048580336],"domain_scores_gemma":[0.99973077,0.00007795524,0.000038002276,0.000032645614,0.000087070825,0.00003343942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005439371,0.001310406,0.0007842238,0.0009892827,0.0005310228,0.0006768039,0.001586153,0.0010629796,0.0022550572],"category_scores_gemma":[0.0014893487,0.00041437853,0.0006316,0.00083097257,0.00045404985,0.0016277613,0.0013395426,0.000993866,0.00079235295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000365733,0.00018797141,0.003934538,0.00014988946,0.00016696981,0.00028413412,0.00022477181,0.45750925,0.015819352,0.009257995,0.02021971,0.4918797],"study_design_scores_gemma":[0.0000062003674,0.000025092555,0.00030792266,0.000008050254,0.000011219848,0.000029264076,0.000014956494,0.9942147,0.0016568409,0.0027371554,0.0009812536,0.0000073687806],"about_ca_topic_score_codex":0.0090926,"about_ca_topic_score_gemma":0.014265289,"teacher_disagreement_score":0.0090926,"about_ca_system_score_codex":0.00091899483,"about_ca_system_score_gemma":0.0007727764,"threshold_uncertainty_score":0.01807934},"labels":[],"label_agreement":null},{"id":"W3119632963","doi":"10.1007/s10489-020-02127-y","title":"Inception single shot multi-box detector with affinity propagation clustering and their application in multi-class vehicle counting","year":2021,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Centroid; Artificial intelligence; Cluster analysis; Pascal (unit); Single shot; Convolutional neural network; Detector; Computer vision; Matching (statistics); Feature (linguistics); Shot (pellet); Pattern recognition (psychology); Telecommunications; Mathematics","score_opus":0.06144484400027519,"score_gpt":0.2904769153021751,"score_spread":0.22903207130189993,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3119632963","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.018128585,0.00069887657,0.9790189,0.000088771805,0.00009828262,0.000053791908,0.000052831274,0.0007709492,0.0010889077],"genre_scores_gemma":[0.235543,0.0008972263,0.7539745,0.00013174613,0.000086198925,0.00009755803,0.00026451497,0.00019879208,0.008806438],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998757,0.0002161081,0.00004586434,0.00029966264,0.000569738,0.00011167297],"domain_scores_gemma":[0.99847966,0.0004571743,0.00007821206,0.00020074811,0.00070470455,0.00007958394],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016517941,0.0006717239,0.001073852,0.0023108213,0.0008431475,0.001192066,0.0018662348,0.001700891,0.0018421935],"category_scores_gemma":[0.0029130701,0.000640469,0.0009884932,0.0023067303,0.0007061182,0.0013212255,0.000928105,0.0011923045,0.0010259114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025757813,0.0002401705,0.0028497763,0.00017556125,0.00022684563,0.0000915566,0.0001808009,0.13593552,0.034983408,0.013064969,0.0030149133,0.808979],"study_design_scores_gemma":[0.0000035032451,0.000030015824,0.00086338964,0.000005715011,0.00001649565,0.000060394017,0.000017542921,0.9876874,0.008718521,0.0013545519,0.0012251637,0.000017383874],"about_ca_topic_score_codex":0.012200986,"about_ca_topic_score_gemma":0.009921808,"teacher_disagreement_score":0.012200986,"about_ca_system_score_codex":0.00086541596,"about_ca_system_score_gemma":0.0012655002,"threshold_uncertainty_score":0.024259925},"labels":[],"label_agreement":null},{"id":"W3127795108","doi":"10.3390/s21030988","title":"Activity Recognition in Residential Spaces with Internet of Things Devices and Thermal Imaging","year":2021,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"RGB color model; Computer science; Computer vision; The Internet; Artificial intelligence; Automation; Activity recognition; Thermal; Engineering; Geography","score_opus":0.018458118149390374,"score_gpt":0.2649695963617268,"score_spread":0.2465114782123364,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3127795108","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02752016,0.00018367165,0.96934503,0.000066939196,0.00004402893,0.000042552667,0.000050160197,0.00084640615,0.001900999],"genre_scores_gemma":[0.5346547,0.00050642696,0.46121615,0.00012640347,0.000060926497,0.00014702854,0.000335219,0.000115469855,0.0028376232],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99976915,0.000043499174,0.0000139596095,0.000063964704,0.000080991405,0.00002836544],"domain_scores_gemma":[0.99985266,0.000043477256,0.00002021221,0.000024111821,0.000047115147,0.000012384897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00020731858,0.00048389245,0.00044260774,0.00076772395,0.00021919947,0.0004982677,0.0005385908,0.00043320324,0.0010295454],"category_scores_gemma":[0.0005629567,0.00025605183,0.0006988691,0.0005458286,0.00024522564,0.00070875254,0.00038011442,0.0002635683,0.00059469696],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023414785,0.00022441888,0.008953991,0.00026382654,0.00011710268,0.0004781028,0.000227623,0.121479444,0.0805012,0.0040732245,0.0028873284,0.78055966],"study_design_scores_gemma":[0.000014447148,0.000121429024,0.010172476,0.000036387006,0.00005384733,0.00089010975,0.00017110752,0.92839545,0.04966908,0.0048231375,0.0056147277,0.000037720558],"about_ca_topic_score_codex":0.0011343207,"about_ca_topic_score_gemma":0.0028292357,"teacher_disagreement_score":0.0011343207,"about_ca_system_score_codex":0.0002242461,"about_ca_system_score_gemma":0.00020846148,"threshold_uncertainty_score":0.0034441948},"labels":[],"label_agreement":null},{"id":"W3129372643","doi":"10.1016/j.neucom.2021.01.112","title":"Interlayer and intralayer scale aggregation for scale-invariant crowd counting","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland; University of Guelph","funders":"","keywords":"Transferability; Scale invariance; Invariant (physics); Computer science; Scale (ratio); Transformation (genetics); Pattern recognition (psychology); Artificial intelligence; Algorithm; Machine learning; Mathematics; Statistics","score_opus":0.019961089146479885,"score_gpt":0.28016974884254714,"score_spread":0.26020865969606727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3129372643","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031475455,0.00029374348,0.9662829,0.00009854668,0.000075093296,0.000044441353,0.00007451475,0.000506055,0.001149265],"genre_scores_gemma":[0.6607925,0.00041217447,0.3337304,0.00014006729,0.00020475325,0.000106657215,0.0003934516,0.00021664689,0.0040033185],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992449,0.00013300353,0.00005176588,0.00019411233,0.00021418478,0.0001619656],"domain_scores_gemma":[0.9987036,0.0004012274,0.00014167822,0.00028369782,0.00035384408,0.00011591572],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013851547,0.0008974552,0.0012597728,0.001299016,0.0007020737,0.0012719217,0.0015805425,0.0010524266,0.0012122333],"category_scores_gemma":[0.004234773,0.00044723143,0.0008913242,0.0011903674,0.0006205539,0.0017301332,0.0021940188,0.00096607895,0.00039422896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046670568,0.00029501793,0.004764824,0.00020385254,0.00026092923,0.00019548243,0.00031981082,0.38524196,0.031632226,0.024547312,0.004975519,0.5470964],"study_design_scores_gemma":[0.000002923311,0.000016890059,0.000669724,0.000005389817,0.00001539492,0.000022731678,0.000014220441,0.99285924,0.0022564,0.0037904312,0.00033977287,0.0000069465164],"about_ca_topic_score_codex":0.0059733386,"about_ca_topic_score_gemma":0.0065869074,"teacher_disagreement_score":0.0059733386,"about_ca_system_score_codex":0.0008960066,"about_ca_system_score_gemma":0.00082676124,"threshold_uncertainty_score":0.01187712},"labels":[],"label_agreement":null},{"id":"W3130921824","doi":"10.1155/2021/6664281","title":"A Crowd Counting Framework Combining with Crowd Location","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education of the People's Republic of China; Education Department of Hunan Province; U.S. Department of Transportation","keywords":"Computer science; Upsampling; Crowds; Convolutional neural network; Feature (linguistics); Artificial intelligence; Computer vision; Pattern recognition (psychology); Data mining; Image (mathematics); Computer security","score_opus":0.013246913860225607,"score_gpt":0.2838999331759412,"score_spread":0.2706530193157156,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3130921824","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017602835,0.0006010906,0.97657096,0.0002658316,0.00010795868,0.00010746008,0.00037578755,0.0016073813,0.0027606853],"genre_scores_gemma":[0.5779235,0.0011857799,0.40908557,0.00032702112,0.00046016191,0.00029994294,0.0021426913,0.00031763755,0.0082577355],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99915695,0.00013483388,0.000036740414,0.00030463975,0.00020971916,0.000157129],"domain_scores_gemma":[0.9993729,0.00013416313,0.00007755116,0.00006255095,0.00027405896,0.00007882855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011519865,0.0020579267,0.0014510191,0.0034097915,0.0009340882,0.0014069563,0.0025669145,0.0012041505,0.0020180333],"category_scores_gemma":[0.002433076,0.0006832099,0.0011492535,0.0017828082,0.0008182014,0.0021876218,0.00275757,0.0010522858,0.0007817783],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003754573,0.00025502802,0.008233234,0.0002865318,0.0002360065,0.0007847797,0.0004611659,0.4889521,0.0084837945,0.03324314,0.015508703,0.4431801],"study_design_scores_gemma":[0.000009451152,0.00003349533,0.00058236084,0.000017570486,0.00003020282,0.00008339909,0.000048283717,0.98805785,0.0016010184,0.007142281,0.002368606,0.000025572512],"about_ca_topic_score_codex":0.02362823,"about_ca_topic_score_gemma":0.015324411,"teacher_disagreement_score":0.02362823,"about_ca_system_score_codex":0.0011159845,"about_ca_system_score_gemma":0.0020900408,"threshold_uncertainty_score":0.046981394},"labels":[],"label_agreement":null},{"id":"W3133598367","doi":"10.1109/cvpr46437.2021.00845","title":"Categorical Depth Distribution Network for Monocular 3D Object Detection","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Monocular; Artificial intelligence; Computer vision; Computer science; Object detection; Benchmark (surveying); Bounding overwatch; Object (grammar); Monocular vision; Projection (relational algebra); Categorical variable; Pixel; Feature (linguistics); Minimum bounding box; Pattern recognition (psychology); Image (mathematics); Algorithm; Geography; Machine learning","score_opus":0.03501080092167862,"score_gpt":0.3029737313526659,"score_spread":0.2679629304309873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3133598367","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.040130317,0.0006526774,0.9426584,0.00031514402,0.00008597288,0.0001535447,0.0040794746,0.008984689,0.0029397088],"genre_scores_gemma":[0.39099488,0.00033405318,0.59011155,0.00031326164,0.000065974666,0.00034174617,0.012567275,0.00042520493,0.00484609],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99940276,0.00008182394,0.000019885265,0.00021235683,0.00020545658,0.00007762924],"domain_scores_gemma":[0.9993563,0.00019527892,0.00006646097,0.00013901664,0.00020038473,0.000042463762],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005420762,0.0011805738,0.00082425075,0.0011051269,0.00042027002,0.0008428813,0.002322115,0.0010172243,0.0036921583],"category_scores_gemma":[0.0030977156,0.00046131093,0.0005529338,0.0011465603,0.00035810244,0.0014522383,0.0016215167,0.001049774,0.001593337],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00065120397,0.00021076897,0.006736151,0.00028824562,0.00010658357,0.00010325076,0.00009200688,0.26685247,0.01655561,0.007460059,0.025351414,0.67559224],"study_design_scores_gemma":[0.000014635833,0.000027434444,0.0010639584,0.000007723414,0.0000066172324,0.000044610963,0.000017270984,0.9884849,0.0032562157,0.0047850586,0.002282167,0.000009380691],"about_ca_topic_score_codex":0.012343923,"about_ca_topic_score_gemma":0.022023492,"teacher_disagreement_score":0.012343923,"about_ca_system_score_codex":0.0016876897,"about_ca_system_score_gemma":0.0010208052,"threshold_uncertainty_score":0.02454412},"labels":[],"label_agreement":null},{"id":"W3134101093","doi":"10.1109/tmm.2021.3062481","title":"AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Manitoba; Huawei Technologies (Canada); Simon Fraser University","funders":"University of Manitoba","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Adaptation (eye); Computer vision; Code (set theory); Counting problem; Image (mathematics); Pattern recognition (psychology); Algorithm","score_opus":0.04566899353662336,"score_gpt":0.3040500184347916,"score_spread":0.2583810248981683,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3134101093","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":[0.012652309,0.00025171152,0.982941,0.00024747307,0.00013077105,0.00015628991,0.00021870935,0.0017445895,0.0016570668],"genre_scores_gemma":[0.36834222,0.00039450402,0.6209887,0.0008072753,0.00032315517,0.00049075054,0.0014783464,0.0008247942,0.0063502267],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982784,0.0004532516,0.000057533318,0.00071857695,0.00030068183,0.00019164903],"domain_scores_gemma":[0.99833703,0.0005739744,0.00019347358,0.00032000596,0.00037850966,0.00019694943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023965882,0.0023616944,0.0024680886,0.0018081339,0.0013327174,0.0015949156,0.0052080806,0.0021116,0.002384946],"category_scores_gemma":[0.004391213,0.001198903,0.001600302,0.0014076788,0.0016614943,0.0032076552,0.0041989367,0.0021409295,0.0010503903],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036637345,0.00036245526,0.0032590812,0.0002957282,0.00019271785,0.00031597572,0.0005060076,0.66630363,0.009368182,0.015809907,0.013522184,0.28969777],"study_design_scores_gemma":[0.000009077823,0.000029963003,0.00019828044,0.000013242575,0.000010935137,0.000053665735,0.00003679212,0.9901264,0.0018916273,0.005760676,0.0018536905,0.000015809886],"about_ca_topic_score_codex":0.009556235,"about_ca_topic_score_gemma":0.00981864,"teacher_disagreement_score":0.009556235,"about_ca_system_score_codex":0.0016613093,"about_ca_system_score_gemma":0.0018592549,"threshold_uncertainty_score":0.019001245},"labels":[],"label_agreement":null},{"id":"W3135446078","doi":"10.1109/tcsvt.2021.3063001","title":"Feature Aggregation Networks Based on Dual Attention Capsules for Visual Object Tracking","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Scholarship Council; National Natural Science Foundation of China; Compute Canada","keywords":"Computer science; Artificial intelligence; Discriminative model; Convolutional neural network; Pattern recognition (psychology); Feature (linguistics); Histogram of oriented gradients; Feature vector; BitTorrent tracker; Feature learning; Feature extraction; Video tracking; Computer vision; Eye tracking; Histogram; Object (grammar); Image (mathematics)","score_opus":0.02583028711931734,"score_gpt":0.28789121152742786,"score_spread":0.2620609244081105,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3135446078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.047387403,0.0004732002,0.9475945,0.000102840764,0.0000562164,0.000059081438,0.00010615731,0.002498596,0.0017219542],"genre_scores_gemma":[0.8372209,0.00027468958,0.15709722,0.00022402112,0.000059211383,0.00010674029,0.0005370754,0.00015053611,0.0043295384],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99960035,0.00003910043,0.000021596727,0.00015656005,0.00011135362,0.00007120029],"domain_scores_gemma":[0.9995229,0.00012476221,0.00008773558,0.000105194566,0.000114937,0.000044474265],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005895231,0.00094511686,0.00084315514,0.000692157,0.0003443883,0.00059667416,0.0015159689,0.00065623055,0.0013469208],"category_scores_gemma":[0.0015547978,0.00035515951,0.00060561666,0.00082673645,0.0004944439,0.0015611871,0.001505983,0.00095656636,0.00044921867],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045874374,0.00019903995,0.004242103,0.000110448105,0.00014183427,0.00022497619,0.00015888078,0.2784295,0.053888455,0.00733816,0.004641263,0.65016663],"study_design_scores_gemma":[0.0000082890065,0.000093316616,0.00082177354,0.00000462836,0.00002591492,0.00004694076,0.0000084250405,0.98944,0.007018893,0.0017258014,0.0007970965,0.000008836266],"about_ca_topic_score_codex":0.007829663,"about_ca_topic_score_gemma":0.008435774,"teacher_disagreement_score":0.007829663,"about_ca_system_score_codex":0.0009978042,"about_ca_system_score_gemma":0.00073551224,"threshold_uncertainty_score":0.015568197},"labels":[],"label_agreement":null},{"id":"W3136055001","doi":"10.1007/s00138-021-01185-7","title":"Multiple convolutional features in Siamese networks for object tracking","year":2021,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université TÉLUQ; Polytechnique Montréal","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Computer science; Artificial intelligence; BitTorrent tracker; Convolutional neural network; Feature (linguistics); Representation (politics); Pattern recognition (psychology); Similarity (geometry); Video tracking; Object (grammar); Tracking (education); Active appearance model; Code (set theory); Computer vision; Abstraction; Eye tracking; Image (mathematics)","score_opus":0.016070380033527728,"score_gpt":0.3221726984705156,"score_spread":0.3061023184369879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3136055001","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021509737,0.0005351535,0.9758696,0.00018125161,0.00005838597,0.000024141265,0.000089330744,0.00063555135,0.0010968735],"genre_scores_gemma":[0.56046337,0.0006883774,0.4246571,0.00023430849,0.00011527542,0.000099459234,0.0005988808,0.00025894016,0.012884356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996588,0.000072322655,0.000020805754,0.00011889141,0.00007965923,0.000049448852],"domain_scores_gemma":[0.9990508,0.00041289243,0.000065474735,0.00016421932,0.00025282332,0.000053791067],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012822603,0.00058076304,0.0007384148,0.00085729396,0.0004348684,0.000915873,0.0013562642,0.0011389387,0.002483166],"category_scores_gemma":[0.002848437,0.00058184867,0.00056461315,0.0011319482,0.0006350756,0.0019733158,0.0011874326,0.0013869903,0.0007601739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025587273,0.00014480847,0.0015381087,0.000075316246,0.00011647123,0.00006210726,0.00007445775,0.47316527,0.010274932,0.03468389,0.0044716997,0.47513697],"study_design_scores_gemma":[0.0000031118873,0.000008421517,0.00012940075,0.0000019219433,0.000005845014,0.000007603369,0.0000024830579,0.9943773,0.0009181032,0.0042397836,0.00030313677,0.0000028012287],"about_ca_topic_score_codex":0.018230848,"about_ca_topic_score_gemma":0.0242831,"teacher_disagreement_score":0.018230848,"about_ca_system_score_codex":0.0011464581,"about_ca_system_score_gemma":0.0010878687,"threshold_uncertainty_score":0.03624946},"labels":[],"label_agreement":null},{"id":"W3137858533","doi":"10.1109/tifs.2021.3107157","title":"Unsupervised and Self-Adaptative Techniques for Cross-Domain Person Re-Identification","year":2021,"lang":"en","type":"preprint","venue":"IEEE Transactions on Information Forensics and Security","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Computer science; Overfitting; Artificial intelligence; Machine learning; Identification (biology); Generalization; Task (project management); Feature (linguistics); Domain (mathematical analysis); Function (biology); Annotation; Adaptation (eye); Pattern recognition (psychology); Artificial neural network","score_opus":0.026237316399433202,"score_gpt":0.2951894032612501,"score_spread":0.2689520868618169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3137858533","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":[0.017809227,0.0005549576,0.9767553,0.00010784815,0.00012821596,0.00007379294,0.00008776928,0.0029286938,0.0015542543],"genre_scores_gemma":[0.3852896,0.00064378005,0.60017294,0.00044601675,0.00016725197,0.00019702231,0.0010795392,0.00073869136,0.011265182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983707,0.00038971577,0.0000702574,0.00067135505,0.00032029185,0.00017776657],"domain_scores_gemma":[0.99822587,0.00033459353,0.00021338598,0.000778892,0.00035746983,0.00008984378],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00224119,0.0018273568,0.0015577697,0.0015230755,0.00072205433,0.0008764543,0.0032744408,0.0017814212,0.0023838594],"category_scores_gemma":[0.0042483835,0.00077696686,0.001635659,0.0012606082,0.00084922503,0.0023620483,0.0024761045,0.0022235275,0.003251938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029175897,0.00030312946,0.0029762876,0.00014017477,0.00027753707,0.0002760954,0.00026349706,0.13755353,0.024467554,0.0046489555,0.008915513,0.8198859],"study_design_scores_gemma":[0.000011847658,0.000100026664,0.0013735748,0.000021302916,0.00004022555,0.00043652116,0.00008625847,0.9726739,0.015912503,0.004751024,0.004550245,0.000042663345],"about_ca_topic_score_codex":0.002665187,"about_ca_topic_score_gemma":0.00415054,"teacher_disagreement_score":0.0032744408,"about_ca_system_score_codex":0.0005464254,"about_ca_system_score_gemma":0.0008355006,"threshold_uncertainty_score":0.011852682},"labels":[],"label_agreement":null},{"id":"W3138357105","doi":"10.1016/j.dsp.2021.103030","title":"An improved scheme for multifeature-based foreground detection using challenging conditions","year":2021,"lang":"en","type":"article","venue":"Digital Signal Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Computer science; Pixel; Computer vision; Pattern recognition (psychology); Foreground detection; Feature (linguistics); Feature vector; Frame (networking); Diagonal; Mathematics; Background subtraction","score_opus":0.03987047743835182,"score_gpt":0.32542371683773774,"score_spread":0.2855532393993859,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3138357105","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007410527,0.00037063484,0.99089617,0.00005727356,0.00007753866,0.00005694873,0.00006844844,0.00047031906,0.00059218385],"genre_scores_gemma":[0.07377303,0.00038222832,0.9227535,0.000090770605,0.00009480075,0.00008534602,0.00027247847,0.00009020296,0.0024576702],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990823,0.00013860778,0.000056640274,0.00022680777,0.0003808546,0.00011469486],"domain_scores_gemma":[0.9991134,0.0002037328,0.000063773085,0.0002331466,0.0003199834,0.000066012915],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00093999616,0.0012092757,0.0013459001,0.0014244607,0.00070899987,0.001102085,0.0017900195,0.0015831203,0.0033586738],"category_scores_gemma":[0.0021518245,0.00055336085,0.0009287785,0.0014144644,0.00045678136,0.0016681,0.0020836557,0.0013132929,0.0021441581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005853434,0.00015610027,0.00089205615,0.00022196947,0.00010407825,0.00012919711,0.00014414408,0.021595204,0.22808598,0.0047521167,0.002173757,0.74116004],"study_design_scores_gemma":[0.00005025224,0.00019517902,0.0018127818,0.000030607192,0.00009690697,0.00048532803,0.000027045835,0.93045527,0.058434386,0.0026049544,0.005733915,0.00007340351],"about_ca_topic_score_codex":0.0035321235,"about_ca_topic_score_gemma":0.0056695696,"teacher_disagreement_score":0.0035321235,"about_ca_system_score_codex":0.00053215114,"about_ca_system_score_gemma":0.0011241602,"threshold_uncertainty_score":0.011235833},"labels":[],"label_agreement":null},{"id":"W3152390103","doi":"10.1016/j.knosys.2021.106990","title":"A robust and fast multispectral pedestrian detection deep network","year":2021,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Multispectral image; Pedestrian detection; Computer science; Artificial intelligence; Kernel (algebra); Feature (linguistics); Pattern recognition (psychology); Computer vision; Pyramid (geometry); Convolutional neural network; Representation (politics); Receptive field; Field (mathematics); Pedestrian; Geography; Mathematics","score_opus":0.03498350239768094,"score_gpt":0.26415783783564883,"score_spread":0.2291743354379679,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3152390103","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023901783,0.00061837427,0.96919596,0.0003346892,0.00020916028,0.00006333401,0.0005178552,0.0029771067,0.0021817142],"genre_scores_gemma":[0.44993138,0.0008460219,0.52257615,0.00076792575,0.00017866571,0.00017367331,0.002764811,0.00026335844,0.022498047],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996526,0.000031277712,0.000013008968,0.00012826345,0.00010613647,0.00006867366],"domain_scores_gemma":[0.99958533,0.00008200999,0.000032407308,0.00007738219,0.00018219542,0.000040674855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061687984,0.0011648834,0.0010446422,0.0009642535,0.0004905985,0.0008366526,0.0019306199,0.0014991016,0.0029500325],"category_scores_gemma":[0.0010793441,0.00078419317,0.000791708,0.0008001072,0.00036260302,0.0013762416,0.0018138497,0.0015434904,0.0021177016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030468442,0.00032592178,0.0014813836,0.0001016182,0.00017600784,0.000099795856,0.000036386067,0.11574241,0.026588505,0.0042880913,0.011325162,0.83953005],"study_design_scores_gemma":[0.00000615174,0.000028195827,0.0003722167,0.0000075160297,0.000021254342,0.000038847837,0.000005966777,0.99077785,0.0057310974,0.001742458,0.0012592133,0.000009230973],"about_ca_topic_score_codex":0.013508834,"about_ca_topic_score_gemma":0.022874499,"teacher_disagreement_score":0.013508834,"about_ca_system_score_codex":0.0010039111,"about_ca_system_score_gemma":0.0012561914,"threshold_uncertainty_score":0.026860416},"labels":[],"label_agreement":null},{"id":"W3153110208","doi":"10.2316/j.2021.206-0625","title":"VEHICLE TYPE DETECTION BASED ON RETINANET WITH ADAPTIVE LEARNING RATE ATTENUATION","year":2021,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Attenuation; Computer science; Type (biology); Artificial intelligence; Control theory (sociology); Algorithm; Physics; Geology; Optics","score_opus":0.018109055354126945,"score_gpt":0.27160647209840155,"score_spread":0.2534974167442746,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3153110208","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":[0.071910255,0.0006334609,0.9193675,0.00024251852,0.00018183052,0.00007431587,0.00014210137,0.004298616,0.0031493017],"genre_scores_gemma":[0.81519526,0.00049300096,0.17462105,0.0003055511,0.000088574205,0.00012148086,0.0003384703,0.000119900564,0.008716718],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997056,0.000027621327,0.000010485854,0.00011015338,0.00009167026,0.00005446366],"domain_scores_gemma":[0.99971205,0.00005024691,0.000034227363,0.000038899405,0.00014007013,0.000024502917],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004093075,0.00070733525,0.00092035223,0.0009566648,0.0004431954,0.00070832786,0.0014963299,0.0007121208,0.0015157287],"category_scores_gemma":[0.00093790866,0.00039967254,0.000538347,0.0006341454,0.00038156807,0.0014309993,0.000830246,0.00061211776,0.00048665045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003667478,0.00017615616,0.0045721116,0.000107790394,0.00014952803,0.00020089577,0.00010732809,0.24120368,0.043157477,0.0070253457,0.004225164,0.69870776],"study_design_scores_gemma":[0.0000074193576,0.00006147647,0.0006257216,0.000005384844,0.00001834203,0.000071664246,0.000010603412,0.9912282,0.0059239306,0.0013231697,0.0007098189,0.000014084099],"about_ca_topic_score_codex":0.009357963,"about_ca_topic_score_gemma":0.009731636,"teacher_disagreement_score":0.009357963,"about_ca_system_score_codex":0.00088573823,"about_ca_system_score_gemma":0.0009195572,"threshold_uncertainty_score":0.01860696},"labels":[],"label_agreement":null},{"id":"W3154019088","doi":"10.1016/j.scs.2021.102908","title":"Adapting Gaussian YOLOv3 with transfer learning for overhead view human detection in smart cities and societies","year":2021,"lang":"en","type":"article","venue":"Sustainable Cities and Society","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":51,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"Deanship of Scientific Research, King Saud University; King Saud University","keywords":"Overhead (engineering); Transfer of learning; Computer science; Gaussian; Transfer (computing); Artificial intelligence; Architectural engineering; Engineering; Physics","score_opus":0.016137824693535873,"score_gpt":0.25953997258396644,"score_spread":0.24340214789043058,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154019088","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06627406,0.00032912786,0.9306323,0.00016864785,0.00009146264,0.000043158143,0.00010171218,0.0013519166,0.0010075528],"genre_scores_gemma":[0.8053827,0.000238657,0.18767086,0.00030258365,0.00009565595,0.000098117314,0.00059412065,0.00020181076,0.005415593],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999508,0.00011733023,0.000018421977,0.00014267021,0.000108443,0.000105050334],"domain_scores_gemma":[0.9992473,0.0002897938,0.00004904347,0.00011853097,0.000239937,0.00005545695],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013522389,0.0006294295,0.0010299477,0.0007267763,0.00043434536,0.0005601181,0.0016579618,0.0013264188,0.0018018004],"category_scores_gemma":[0.0032057378,0.00038161865,0.0007886359,0.0007635913,0.00062645297,0.0012370344,0.0019717005,0.0011670247,0.0007374461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00053709745,0.00027624596,0.0050302707,0.00012869229,0.00012648077,0.000099579505,0.00018812717,0.46308848,0.011840837,0.006134276,0.004959735,0.50759023],"study_design_scores_gemma":[0.0000032661203,0.000014849585,0.00025278467,0.0000016682142,0.0000034760146,0.0000075889116,0.0000066169955,0.99824286,0.00061546505,0.0006916432,0.00015674067,0.0000030249166],"about_ca_topic_score_codex":0.01501864,"about_ca_topic_score_gemma":0.011802372,"teacher_disagreement_score":0.01501864,"about_ca_system_score_codex":0.0006954538,"about_ca_system_score_gemma":0.0010531398,"threshold_uncertainty_score":0.029862463},"labels":[],"label_agreement":null},{"id":"W3154570199","doi":"10.1139/juvs-2020-0018","title":"Video analysis for the detection of animals using convolutional neural networks and consumer-grade drones","year":2021,"lang":"en","type":"article","venue":"Journal of Unmanned Vehicle Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Facilities Council; Research Councils UK","keywords":"Drone; Computer science; Proof of concept; Convolutional neural network; Intersection (aeronautics); Artificial intelligence; Frame (networking); Real-time computing; Transfer of learning; Process (computing); Deep learning; Inference; Machine learning; Embedded system; Pattern recognition (psychology); Operating system; Cartography; Telecommunications","score_opus":0.046732418506534326,"score_gpt":0.30361618245231276,"score_spread":0.25688376394577844,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3154570199","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5899793,0.00070008164,0.3916646,0.0003017168,0.00013016314,0.00021373459,0.00097032037,0.006603114,0.009437016],"genre_scores_gemma":[0.9036642,0.0002822694,0.089981824,0.00010325502,0.000021429192,0.000067045235,0.0008920209,0.00005947481,0.0049285265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998227,0.00001781755,0.000007905856,0.00004584528,0.000084027204,0.000021796985],"domain_scores_gemma":[0.9997626,0.00006990837,0.00003911976,0.000027289761,0.00008888772,0.000012231467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030860835,0.0004787461,0.00017355323,0.0007740076,0.00015487452,0.0004020561,0.0004952975,0.00040603452,0.0021752703],"category_scores_gemma":[0.00078936596,0.0001809494,0.0002152033,0.0003716881,0.00015022868,0.00041593646,0.00028982604,0.00036478078,0.000606497],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00057696435,0.00038511126,0.014606446,0.0002926164,0.00020228549,0.0005284106,0.0001415253,0.10993785,0.3169764,0.0015635862,0.0039453446,0.5508435],"study_design_scores_gemma":[0.000015990016,0.00016338774,0.018876502,0.00002740843,0.000040481198,0.00022323128,0.000047430876,0.90385115,0.073923066,0.0004692067,0.0023398143,0.000022367783],"about_ca_topic_score_codex":0.0153306,"about_ca_topic_score_gemma":0.026044605,"teacher_disagreement_score":0.0153306,"about_ca_system_score_codex":0.00060937117,"about_ca_system_score_gemma":0.00032394106,"threshold_uncertainty_score":0.030482769},"labels":[],"label_agreement":null},{"id":"W3156049363","doi":"10.1016/j.compag.2021.106139","title":"A CNN-based posture change detection for lactating sow in untrimmed depth videos","year":2021,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"The Scarborough Hospital; University of Toronto","funders":"","keywords":"Artificial intelligence; Computer vision; Computer science; Change detection; Task (project management); Focus (optics); Pattern recognition (psychology); Engineering","score_opus":0.0227582806434493,"score_gpt":0.2711118533257488,"score_spread":0.24835357268229946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3156049363","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3717872,0.0042576683,0.59582174,0.00058881467,0.0013080392,0.00035602626,0.0050150943,0.008334206,0.01253121],"genre_scores_gemma":[0.8088494,0.0019317669,0.16567679,0.0005280716,0.00022370007,0.00020519533,0.0045708898,0.00016824491,0.017845951],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999824,0.0000085228585,0.0000046193745,0.00006709665,0.000042648608,0.000053127034],"domain_scores_gemma":[0.9998971,0.000016992743,0.000011497635,0.000012425554,0.000046712066,0.000015325202],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002091025,0.00084415,0.00061810104,0.0006901638,0.00019721154,0.00033862222,0.0008812328,0.0006807535,0.002327117],"category_scores_gemma":[0.00037038623,0.00034764627,0.00050262053,0.0005392144,0.0001142732,0.0003197933,0.00044016948,0.0005090371,0.0011531514],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004973195,0.00029596893,0.008615982,0.00016808858,0.00014463963,0.00029788062,0.00004986975,0.018321903,0.09836713,0.00031962423,0.009190814,0.8637307],"study_design_scores_gemma":[0.000022732716,0.00027133434,0.023508772,0.00005040459,0.000108403656,0.00028966504,0.000052079246,0.9404793,0.03149132,0.0004306987,0.00326654,0.00002876989],"about_ca_topic_score_codex":0.015089088,"about_ca_topic_score_gemma":0.023306949,"teacher_disagreement_score":0.015089088,"about_ca_system_score_codex":0.00041837708,"about_ca_system_score_gemma":0.0005723466,"threshold_uncertainty_score":0.030002534},"labels":[],"label_agreement":null},{"id":"W3158771472","doi":"10.1504/ijcat.2021.10037705","title":"Real-time robust tracking with part-based and spatio-temporal context","year":2021,"lang":"en","type":"article","venue":"International Journal of Computer Applications in Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Western University","funders":"","keywords":"Context (archaeology); Tracking (education); Computer science; Artificial intelligence; Geography; Psychology","score_opus":0.01807728134159166,"score_gpt":0.28605565675648664,"score_spread":0.267978375414895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3158771472","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01814586,0.0007601597,0.9782839,0.000052319574,0.000075983604,0.000041874027,0.00008353017,0.0015912728,0.0009652059],"genre_scores_gemma":[0.522641,0.00092219893,0.47115192,0.00016659543,0.000101046295,0.00009663124,0.00064257026,0.0002351542,0.004042839],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99906415,0.00009675542,0.000039422786,0.00034408504,0.00038187692,0.00007381361],"domain_scores_gemma":[0.9991948,0.0001202876,0.00012640342,0.00027410002,0.00023336914,0.000051073937],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095552293,0.0009149091,0.0011171257,0.0011069524,0.0004813328,0.0008769739,0.0012763445,0.00081863767,0.0010433189],"category_scores_gemma":[0.0019905865,0.000495767,0.0007761905,0.0018772163,0.00041563166,0.0012133792,0.0011372654,0.00070677156,0.0008169323],"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":[0.00047849238,0.0001559538,0.0028174086,0.00016103142,0.00019158478,0.00029720555,0.00012048223,0.1738933,0.118558295,0.0057198536,0.0046441494,0.6929623],"study_design_scores_gemma":[0.000019653871,0.00010903683,0.002630182,0.000012881754,0.00006817262,0.00034927507,0.000015285737,0.9703249,0.020538753,0.001881962,0.004015484,0.000034382123],"about_ca_topic_score_codex":0.0060356073,"about_ca_topic_score_gemma":0.0075765597,"teacher_disagreement_score":0.0060356073,"about_ca_system_score_codex":0.00042769912,"about_ca_system_score_gemma":0.0012728191,"threshold_uncertainty_score":0.012000918},"labels":[],"label_agreement":null},{"id":"W3162356234","doi":"10.1109/wcnc49053.2021.9417463","title":"Driver Identification Using Vehicular Sensing Data: A Deep Learning Approach","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Classifier (UML); Identification (biology); Benchmark (surveying); Support vector machine; Architecture; Encoder; Artificial intelligence; Deep learning; Advanced driver assistance systems; Machine learning; Data modeling; Real-time computing; Database","score_opus":0.0803018506557022,"score_gpt":0.3238158038821682,"score_spread":0.243513953226466,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3162356234","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20894758,0.0009894663,0.7842279,0.0005366374,0.00015354669,0.00009262386,0.0005580888,0.0011161414,0.0033779708],"genre_scores_gemma":[0.9439451,0.00035967256,0.051252216,0.00009325162,0.00006666903,0.000048958515,0.0011177998,0.000019898469,0.00309652],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99984324,0.00002429352,0.00000875959,0.000048137514,0.000033471642,0.000042163807],"domain_scores_gemma":[0.9998091,0.00005116878,0.000020330528,0.000027449132,0.00007748296,0.000014510518],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040813943,0.0005805646,0.00038847193,0.00067048403,0.00022230484,0.0004477279,0.0008213514,0.00063690863,0.0005946502],"category_scores_gemma":[0.00078634714,0.00027693357,0.000388213,0.0005504138,0.00017181368,0.0005463538,0.0005803882,0.00088330335,0.0003228567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022925892,0.0005324612,0.013037075,0.00009711462,0.00013896481,0.00016652448,0.00013156736,0.37362725,0.012796122,0.004265086,0.0039808867,0.59099776],"study_design_scores_gemma":[0.0000021050846,0.00002637535,0.0009859585,0.0000058731935,0.000008464192,0.000014827379,0.000020320369,0.9958133,0.0015745905,0.0010846598,0.00045877093,0.0000046745226],"about_ca_topic_score_codex":0.007022421,"about_ca_topic_score_gemma":0.009343815,"teacher_disagreement_score":0.007022421,"about_ca_system_score_codex":0.00040895925,"about_ca_system_score_gemma":0.0005629505,"threshold_uncertainty_score":0.013963044},"labels":[],"label_agreement":null},{"id":"W3163249082","doi":"10.18280/isi.260207","title":"An Optimised Allotment and Tracking Using Django and Opencv","year":2021,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Movement (music); Artificial intelligence; Key (lock); Computer vision; Allotment; Tracking (education); Contemplation; Object (grammar); Psychology; Computer security; Art","score_opus":0.037122591564956856,"score_gpt":0.294813375558035,"score_spread":0.25769078399307815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3163249082","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012722235,0.0004545327,0.962462,0.00019301948,0.0002990568,0.00019504997,0.00068619294,0.019681534,0.0033063022],"genre_scores_gemma":[0.065431766,0.00027521868,0.9205355,0.00019102314,0.000051913074,0.0003879925,0.0031708989,0.0015756601,0.008379975],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988569,0.00010416106,0.000057571877,0.00045831213,0.00034655049,0.00017647396],"domain_scores_gemma":[0.9994711,0.00008649139,0.000029333742,0.000099885634,0.00027357612,0.00003959211],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012407978,0.001395378,0.0010489383,0.0022366673,0.0010829355,0.0017925611,0.002383426,0.0015465396,0.009304204],"category_scores_gemma":[0.0025903462,0.0008305515,0.0017837222,0.0017270608,0.00047904646,0.0013286999,0.0013557994,0.001739938,0.00513452],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00047656635,0.00020820196,0.0014456782,0.00026807954,0.00018870262,0.000117624026,0.0001857767,0.024485126,0.035976116,0.003630018,0.025190176,0.907828],"study_design_scores_gemma":[0.00007650492,0.00010824562,0.0035958828,0.00006365253,0.0000899269,0.00030246167,0.000115430106,0.9041675,0.04126123,0.003042423,0.047090493,0.00008627402],"about_ca_topic_score_codex":0.026099237,"about_ca_topic_score_gemma":0.029067716,"teacher_disagreement_score":0.026099237,"about_ca_system_score_codex":0.0011517522,"about_ca_system_score_gemma":0.0019738136,"threshold_uncertainty_score":0.051894665},"labels":[],"label_agreement":null},{"id":"W3164119733","doi":"10.18280/ria.350210","title":"MODFAT: Moving Object Detection by Removing Shadow Based on Fuzzy Technique with an Adaptive Thresholding Method","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer vision; Shadow (psychology); Thresholding; Computer science; Object detection; Object (grammar); Fuzzy logic; Projection (relational algebra); Segmentation; Image (mathematics)","score_opus":0.04121119767389284,"score_gpt":0.30492597726837084,"score_spread":0.263714779594478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3164119733","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":[0.04642038,0.0005761147,0.94885,0.00012215327,0.00013646079,0.000080052945,0.000092440154,0.0011299092,0.0025926176],"genre_scores_gemma":[0.33136204,0.0005020333,0.66299665,0.00011465574,0.0000939341,0.00010314073,0.000273151,0.000084637126,0.004469699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996884,0.000027118796,0.00001879672,0.00008715944,0.00014674132,0.00003188486],"domain_scores_gemma":[0.9997069,0.00007403388,0.000028477458,0.000031156276,0.00014252574,0.000016836348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004385457,0.00042398277,0.0006100321,0.0011146022,0.0003700388,0.000576022,0.0009119796,0.00074022094,0.00164184],"category_scores_gemma":[0.0008658131,0.00023409275,0.0006424204,0.0005987301,0.00030351084,0.0006505874,0.00034091147,0.0005622569,0.00048697068],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003472962,0.00014586862,0.0029638347,0.0002550215,0.000099396,0.00022428413,0.00017676635,0.027711397,0.16229601,0.0028137742,0.0031832454,0.79978305],"study_design_scores_gemma":[0.000037821057,0.00023206971,0.0060710222,0.00003471056,0.00008132929,0.00079408835,0.00007505293,0.91181326,0.07302713,0.0018670432,0.0059149084,0.000051552677],"about_ca_topic_score_codex":0.0030625386,"about_ca_topic_score_gemma":0.002694159,"teacher_disagreement_score":0.0030625386,"about_ca_system_score_codex":0.00036683655,"about_ca_system_score_gemma":0.0004262908,"threshold_uncertainty_score":0.006089449},"labels":[],"label_agreement":null},{"id":"W3165887029","doi":"10.32920/ryerson.14665893.v1","title":"SoC for real - time object tracking in 3D space","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Alertness; Object (grammar); Computer science; Tracking (education); Video tracking; Warning system; Space (punctuation); Work (physics); Hazardous waste; Human–computer interaction; Simulation; Computer security; Real-time computing; Computer vision; Artificial intelligence; Engineering; Psychology","score_opus":0.04381131404775677,"score_gpt":0.33987635932931465,"score_spread":0.29606504528155786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3165887029","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024906944,0.00043764335,0.95030516,0.000097590826,0.00021620911,0.00018142622,0.00030950562,0.01537682,0.008168701],"genre_scores_gemma":[0.6081305,0.000656187,0.3730026,0.00039941506,0.00009454879,0.0005221115,0.0009077424,0.000507954,0.015779037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954814,0.000043905955,0.000023527873,0.00006760352,0.00025767757,0.00005911044],"domain_scores_gemma":[0.99950254,0.000107872926,0.0000601992,0.00013459579,0.00015504796,0.00003974927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040935766,0.0006770939,0.00044547292,0.00051522243,0.00020603118,0.0009320909,0.0010227594,0.0007865379,0.0076804343],"category_scores_gemma":[0.0009913802,0.0002716776,0.0005063776,0.0003256009,0.00025615122,0.00070577743,0.000821166,0.00045685645,0.0023203923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008940298,0.00032913018,0.0029586568,0.00096428086,0.00024225435,0.0009544829,0.00045779604,0.076200105,0.29444337,0.014498882,0.021118209,0.58693874],"study_design_scores_gemma":[0.00015722463,0.0012753621,0.0036587194,0.0001466371,0.00013512619,0.0012276404,0.00010120658,0.80000716,0.10816391,0.006412214,0.07859882,0.00011598014],"about_ca_topic_score_codex":0.001976582,"about_ca_topic_score_gemma":0.0023867812,"teacher_disagreement_score":0.0076804343,"about_ca_system_score_codex":0.0002779464,"about_ca_system_score_gemma":0.0005218417,"threshold_uncertainty_score":0.025693655},"labels":[],"label_agreement":null},{"id":"W3167816005","doi":"10.1007/s00521-021-06193-2","title":"Towards human distance estimation using a thermal sensor array","year":2021,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Trent University; Nottingham Trent University","keywords":"Estimator; Computer science; Artificial intelligence; Algorithm; Mean squared error; Machine learning; Statistics; Mathematics","score_opus":0.04424781283077385,"score_gpt":0.34780241431892245,"score_spread":0.3035546014881486,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3167816005","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10402951,0.00048161094,0.89199734,0.00012017245,0.00017255277,0.0000318751,0.000093463736,0.00094907515,0.002124434],"genre_scores_gemma":[0.78363585,0.0003020339,0.21269867,0.000108227665,0.0000710846,0.000045807687,0.00013132392,0.00003608578,0.0029709097],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994684,0.00012592264,0.000018035167,0.00018053279,0.00017063055,0.000036587993],"domain_scores_gemma":[0.99958557,0.00009201703,0.00005330959,0.000056127083,0.0001893796,0.00002370201],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029851688,0.00054981787,0.0004990427,0.00057838246,0.00019321225,0.00062769366,0.0005344595,0.0006998532,0.0011241009],"category_scores_gemma":[0.0010019639,0.0002198012,0.00032325706,0.0005913991,0.00020603983,0.0006690157,0.00053274166,0.0005215719,0.0008565415],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006807372,0.0001651462,0.007978672,0.00026714563,0.00013153187,0.00020787313,0.00024341523,0.08452143,0.39793012,0.0021305208,0.0029981944,0.5027452],"study_design_scores_gemma":[0.000022921718,0.00029182163,0.008030708,0.000026434114,0.00004083122,0.00037417933,0.00009621338,0.8922001,0.09523284,0.00093840994,0.002698222,0.0000472949],"about_ca_topic_score_codex":0.0009990575,"about_ca_topic_score_gemma":0.0007844862,"teacher_disagreement_score":0.0011241009,"about_ca_system_score_codex":0.00024159992,"about_ca_system_score_gemma":0.00024432736,"threshold_uncertainty_score":0.0037605166},"labels":[],"label_agreement":null},{"id":"W3169558736","doi":"10.1109/access.2021.3086499","title":"Closed-Loop Region of Interest Enabling High Spatial and Temporal Resolutions in Object Detection and Tracking via Wireless Camera","year":2021,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Massachusetts Institute of Technology; Brigham and Women's Hospital","keywords":"Computer science; Computer vision; Tracking (education); Wireless; Artificial intelligence; Video tracking; Object detection; Object (grammar); Loop (graph theory); Telecommunications; Pattern recognition (psychology); Mathematics","score_opus":0.07702225832568384,"score_gpt":0.32464943415617425,"score_spread":0.24762717583049043,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169558736","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055651236,0.00051897124,0.940674,0.00007085683,0.00004818951,0.00006316704,0.000033624026,0.0016225006,0.001317499],"genre_scores_gemma":[0.5596749,0.0004295825,0.4361995,0.00016970828,0.00005587692,0.00014930069,0.000119836586,0.00015156018,0.0030496332],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946135,0.00008997866,0.000025167541,0.00017903066,0.00018942001,0.000055021632],"domain_scores_gemma":[0.99925095,0.00028639784,0.00012886897,0.00009488564,0.00019887337,0.00003989335],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055097096,0.00046645393,0.00045040983,0.00029508624,0.00019950929,0.0005462575,0.0010705981,0.0006605381,0.00096753146],"category_scores_gemma":[0.0018194763,0.0002794469,0.00025204226,0.00023163496,0.00034858432,0.0012292396,0.00064084906,0.0005292153,0.0004549302],"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":[0.0005988437,0.00018920116,0.0015831742,0.00022445664,0.000042873693,0.0004941857,0.00031712532,0.027670333,0.69574565,0.0051242188,0.0018944292,0.26611558],"study_design_scores_gemma":[0.000058219368,0.0006237247,0.0021427232,0.000027849743,0.000049280065,0.0010202122,0.000068447574,0.6076764,0.37729648,0.0018058644,0.009168409,0.00006239704],"about_ca_topic_score_codex":0.0007086557,"about_ca_topic_score_gemma":0.0006491087,"teacher_disagreement_score":0.0010705981,"about_ca_system_score_codex":0.00028795726,"about_ca_system_score_gemma":0.00035343523,"threshold_uncertainty_score":0.003236711},"labels":[],"label_agreement":null},{"id":"W3169647498","doi":"10.1109/icme51207.2021.9428185","title":"Deepmix: Online Auto Data Augmentation for Robust Visual Object Tracking","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Discriminative model; Computer science; Artificial intelligence; Video tracking; Object (grammar); Tracking (education); Focus (optics); Eye tracking; Key (lock); Computer vision; Online model; Online learning; Deep learning; Object detection; Machine learning; Pattern recognition (psychology); Multimedia","score_opus":0.16558939704694467,"score_gpt":0.40580645164085893,"score_spread":0.24021705459391426,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3169647498","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":[0.014388797,0.00038687958,0.9784574,0.0000857917,0.00010315662,0.000057946134,0.0003050166,0.005317893,0.00089712144],"genre_scores_gemma":[0.37533963,0.0004970699,0.61401933,0.00036104157,0.00011644105,0.00026531893,0.0035649368,0.00058897096,0.0052473815],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99930847,0.000083487525,0.000031403215,0.00031376854,0.0001879256,0.000074803764],"domain_scores_gemma":[0.9993699,0.00014902215,0.00007299729,0.0002510609,0.0001119352,0.000045005872],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012120573,0.0015993767,0.0014457735,0.0008079827,0.00045191546,0.0008997498,0.0024185597,0.0009643592,0.0023037416],"category_scores_gemma":[0.0025658011,0.00078848004,0.000874964,0.001012713,0.0007959604,0.0026127407,0.0026299537,0.0020663312,0.0011836321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039806962,0.00024106902,0.0024875544,0.00016112888,0.00012238346,0.00010886951,0.0001543956,0.16937812,0.021159507,0.0065146456,0.008552282,0.79072195],"study_design_scores_gemma":[0.000015266325,0.000054950986,0.00032340962,0.000009812646,0.000011620278,0.000049438368,0.00001147638,0.98804605,0.006037514,0.0031347298,0.002294114,0.000011579435],"about_ca_topic_score_codex":0.0050532403,"about_ca_topic_score_gemma":0.007743624,"teacher_disagreement_score":0.0050532403,"about_ca_system_score_codex":0.0007494946,"about_ca_system_score_gemma":0.0012581575,"threshold_uncertainty_score":0.010047674},"labels":[],"label_agreement":null},{"id":"W3170523166","doi":"10.1109/vtc2021-spring51267.2021.9448692","title":"Multimodal Machine Learning for Pedestrian Detection","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Pedestrian detection; Computer science; Pedestrian; Artificial intelligence; Convolutional neural network; Object detection; Computer vision; Focus (optics); Context (archaeology); Deep learning; Cluster analysis; Machine learning; Pattern recognition (psychology); Engineering","score_opus":0.03129910344959924,"score_gpt":0.3017127363908818,"score_spread":0.27041363294128257,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3170523166","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":[0.09541677,0.0036925927,0.8812638,0.0013362536,0.00039216178,0.0002454486,0.0013019718,0.0057239467,0.010627139],"genre_scores_gemma":[0.8090388,0.00081854535,0.18025373,0.00044113977,0.00018280176,0.00018857647,0.001630401,0.00012339708,0.0073226118],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99934036,0.00024337858,0.000023003879,0.0001538013,0.00012201833,0.00011741262],"domain_scores_gemma":[0.99928397,0.00028910796,0.000054860142,0.000104307896,0.00022235443,0.00004537486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014587569,0.000753612,0.00070828095,0.0013593878,0.00049671065,0.00064960663,0.000705003,0.00091506564,0.005713043],"category_scores_gemma":[0.003078418,0.00022529408,0.00071576226,0.0009158236,0.00031172534,0.00090226316,0.00076363457,0.00094041554,0.0019811117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003937305,0.00030560794,0.006169292,0.0001857035,0.00012275609,0.00012100379,0.00008415085,0.07448535,0.008521982,0.004616019,0.015928015,0.8890664],"study_design_scores_gemma":[0.000008859401,0.00008835873,0.0023307248,0.00002472889,0.000022742357,0.000079409896,0.000033251363,0.9838207,0.0043801605,0.005805261,0.0033875192,0.000018320326],"about_ca_topic_score_codex":0.0046902825,"about_ca_topic_score_gemma":0.006746316,"teacher_disagreement_score":0.005713043,"about_ca_system_score_codex":0.00088737614,"about_ca_system_score_gemma":0.0005543026,"threshold_uncertainty_score":0.01911205},"labels":[],"label_agreement":null},{"id":"W3174493884","doi":"10.21203/rs.3.rs-84743/v1","title":"Exploiting Prunability for Person Re-identification","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Identification (biology); Computer science; Psychology; Biology","score_opus":0.36536975232635077,"score_gpt":0.4920928535283065,"score_spread":0.12672310120195573,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3174493884","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":[0.19669724,0.0013632525,0.79115605,0.0004937484,0.00014808957,0.000098110475,0.000244507,0.0047470964,0.005051894],"genre_scores_gemma":[0.8162453,0.00050702284,0.17616312,0.0002618227,0.00005685488,0.000085909734,0.00075910357,0.00031202415,0.005608754],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925536,0.00012331965,0.000042790776,0.00015955356,0.00028126064,0.00013769195],"domain_scores_gemma":[0.9981061,0.00064757373,0.00017077118,0.0005898405,0.00042063024,0.00006513818],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089598994,0.0009665765,0.00071049365,0.00084442185,0.00043519225,0.00078177796,0.001657444,0.0010019318,0.0028456782],"category_scores_gemma":[0.0054071667,0.00040540504,0.0005199215,0.0004886846,0.0005368095,0.0019224873,0.0013342509,0.0010681134,0.0013970614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090041145,0.0001716004,0.005401753,0.00025189226,0.00015848012,0.0008855879,0.0002511979,0.25825062,0.08707444,0.009179219,0.0074682944,0.6300065],"study_design_scores_gemma":[0.000014194717,0.00015387654,0.001405259,0.00003364396,0.000041973653,0.0003929169,0.00006112246,0.94492996,0.043815874,0.0044715335,0.0046639196,0.000015765117],"about_ca_topic_score_codex":0.0032026537,"about_ca_topic_score_gemma":0.0052016685,"teacher_disagreement_score":0.0032026537,"about_ca_system_score_codex":0.00047075126,"about_ca_system_score_gemma":0.00066157954,"threshold_uncertainty_score":0.009519756},"labels":[],"label_agreement":null},{"id":"W3175172519","doi":"10.1016/j.neucom.2021.06.055","title":"A smartly simple way for joint crowd counting and localization","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Code (set theory); Simple (philosophy); Artificial intelligence; Task (project management); Pattern recognition (psychology); Interval (graph theory); Computer vision; Mathematics; Set (abstract data type)","score_opus":0.03722787786192351,"score_gpt":0.2862476532103509,"score_spread":0.2490197753484274,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3175172519","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":[0.0019585257,0.000040913314,0.9911271,0.00008833964,0.00011437801,0.000061998595,0.00012755458,0.0052439035,0.001237173],"genre_scores_gemma":[0.05326489,0.00007431223,0.93926316,0.00016872247,0.00008851862,0.00017055056,0.000490683,0.0006671805,0.0058120894],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980531,0.00028818208,0.000118338285,0.000583431,0.0007994918,0.00015743215],"domain_scores_gemma":[0.9980033,0.00031945968,0.00011100112,0.0009360733,0.00046623882,0.00016392015],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010892905,0.0018140059,0.0018411637,0.0018194823,0.0012000679,0.002498138,0.0034040795,0.0021835028,0.014018996],"category_scores_gemma":[0.005156863,0.0014283226,0.001255274,0.0013391766,0.00082387624,0.0036224306,0.005751157,0.0024418694,0.011198832],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007276666,0.0003227277,0.0015561878,0.0001809068,0.0002250685,0.0002504242,0.00023520915,0.024856227,0.08859987,0.015911087,0.0257642,0.84137034],"study_design_scores_gemma":[0.00011687422,0.00016538448,0.0017411924,0.00005302137,0.000099877674,0.0007018207,0.00018172509,0.8338952,0.0847888,0.046332996,0.03175826,0.00016481247],"about_ca_topic_score_codex":0.003159785,"about_ca_topic_score_gemma":0.0063229906,"teacher_disagreement_score":0.014018996,"about_ca_system_score_codex":0.0004674608,"about_ca_system_score_gemma":0.0011438641,"threshold_uncertainty_score":0.046898186},"labels":[],"label_agreement":null},{"id":"W3177703589","doi":"10.48550/arxiv.2107.07067","title":"MeNToS: Tracklets Association with a Space-Time Memory Network","year":2021,"lang":"en","type":"preprint","venue":"PolyPublie (École Polytechnique de Montréal)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Association (psychology); Computer science; Segmentation; Artificial intelligence; Benchmark (surveying); Metric (unit); Hyperparameter; Object (grammar); Computer vision; Space (punctuation); Dropout (neural networks); Selection (genetic algorithm); Tracking (education); Machine learning; Geography; Cartography; Psychology","score_opus":0.010840430790830812,"score_gpt":0.23869738729416387,"score_spread":0.22785695650333307,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177703589","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":[0.010242563,0.00026486866,0.9803611,0.00015423242,0.00016510217,0.00007736839,0.00035992224,0.0068260836,0.0015486222],"genre_scores_gemma":[0.23548555,0.00025231723,0.7482965,0.00035556752,0.00027851996,0.00038773872,0.0025109889,0.001121451,0.011311305],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999127,0.00015327272,0.000041189694,0.0003468259,0.00022818998,0.000103484934],"domain_scores_gemma":[0.99863297,0.00035379425,0.00015424188,0.00052043935,0.00021588578,0.00012264827],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012929173,0.0011711367,0.0011041841,0.0010205441,0.00065658556,0.001710717,0.0029050328,0.0016618029,0.0047917026],"category_scores_gemma":[0.0047754124,0.0006366288,0.0008497743,0.0012009294,0.0005020569,0.002503918,0.002463409,0.0018608157,0.0026403528],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011917072,0.00024128941,0.0023770526,0.00017656175,0.00018322888,0.00019328776,0.00017666366,0.16084002,0.017443579,0.02052246,0.026088478,0.7705656],"study_design_scores_gemma":[0.00004458265,0.0001086235,0.0003392198,0.000012862169,0.00002443848,0.00010112597,0.000017272423,0.9743052,0.0071540712,0.009348285,0.008528818,0.000015441317],"about_ca_topic_score_codex":0.0037821068,"about_ca_topic_score_gemma":0.005659361,"teacher_disagreement_score":0.0047917026,"about_ca_system_score_codex":0.00088374864,"about_ca_system_score_gemma":0.0012273496,"threshold_uncertainty_score":0.016029835},"labels":[],"label_agreement":null},{"id":"W3177973888","doi":"10.1155/2021/2085876","title":"A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or Violent Environment","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Beijing Social Science Fund","keywords":"Computer science; Pedestrian; Artificial intelligence; Video tracking; Tracking (education); Deep learning; Credibility; Computer vision; Intersection (aeronautics); Data mining; Machine learning; Computer security; Object (grammar); Engineering; Transport engineering","score_opus":0.03359370150386461,"score_gpt":0.3029799128227301,"score_spread":0.26938621131886553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3177973888","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15602365,0.001495379,0.8304094,0.0007759759,0.00040997344,0.00006502057,0.0011083627,0.0028945664,0.0068177315],"genre_scores_gemma":[0.9290124,0.00069894944,0.05316364,0.00041933966,0.00008040752,0.00008846354,0.0015475049,0.00009113058,0.014898162],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998677,0.000013666273,0.000005593719,0.00005539076,0.000026679854,0.000030890344],"domain_scores_gemma":[0.9998703,0.000027222542,0.000013275486,0.000011593735,0.00005732967,0.000020350397],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00030545812,0.00063935225,0.00057720754,0.0004532924,0.00024261583,0.0005276021,0.0011655982,0.00075729436,0.0020187565],"category_scores_gemma":[0.0005922681,0.0003774351,0.0007280337,0.00046885133,0.00024928196,0.000661086,0.00072822766,0.00089533237,0.0006969865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003125607,0.00012922118,0.010551466,0.000095386196,0.00011777645,0.00024045903,0.00011838794,0.722412,0.006289617,0.0047058836,0.0069690235,0.24805827],"study_design_scores_gemma":[0.000003993859,0.00002193085,0.00046796858,0.00000450399,0.000010556258,0.00002997929,0.000004113303,0.99782526,0.000461707,0.00071268715,0.0004529251,0.000004463918],"about_ca_topic_score_codex":0.020350764,"about_ca_topic_score_gemma":0.017122177,"teacher_disagreement_score":0.020350764,"about_ca_system_score_codex":0.0007167397,"about_ca_system_score_gemma":0.0008522743,"threshold_uncertainty_score":0.04046458},"labels":[],"label_agreement":null},{"id":"W3181969869","doi":"10.1016/j.imavis.2021.104248","title":"Task-based parameter isolation for foreground segmentation without catastrophic forgetting using multi-scale region and edges fusion network","year":2021,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Forgetting; Artificial intelligence; Fusion; Task (project management); Segmentation; Isolation (microbiology); Computer science; Scale (ratio); Computer vision; Pattern recognition (psychology); Image fusion; Image (mathematics); Engineering; Cartography; Geography","score_opus":0.04703583984345797,"score_gpt":0.35723927355533214,"score_spread":0.31020343371187414,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3181969869","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":[0.036451798,0.0007549559,0.9592333,0.00009429423,0.000095758056,0.000059273076,0.00011250842,0.0021283457,0.0010697789],"genre_scores_gemma":[0.6920589,0.0007164574,0.3012103,0.00026807506,0.00010969154,0.000109722205,0.0008327209,0.00040181284,0.0042923274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941695,0.000054110227,0.000033838136,0.00022761409,0.0001418458,0.00012556552],"domain_scores_gemma":[0.99940133,0.00014443899,0.0000619569,0.00014075346,0.00019809253,0.000053358046],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008200243,0.0014876626,0.0015117307,0.0008809743,0.00066556275,0.00092502654,0.0017838937,0.001156522,0.0015028085],"category_scores_gemma":[0.0017215271,0.00058145757,0.000988603,0.0009504139,0.00047045294,0.0017153266,0.0016648734,0.0013251984,0.00093804946],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00081323937,0.0002938075,0.0019990094,0.00013461459,0.00017491588,0.0002186901,0.00015851494,0.08378339,0.07090918,0.0018141367,0.00346401,0.8362366],"study_design_scores_gemma":[0.000014846145,0.00009981628,0.0017856403,0.000013096281,0.00007837906,0.0001392036,0.00003501486,0.96838045,0.025951792,0.0022411721,0.0012354469,0.000025089994],"about_ca_topic_score_codex":0.007966131,"about_ca_topic_score_gemma":0.011055961,"teacher_disagreement_score":0.007966131,"about_ca_system_score_codex":0.0005224927,"about_ca_system_score_gemma":0.0012063019,"threshold_uncertainty_score":0.015839517},"labels":[],"label_agreement":null},{"id":"W3182753879","doi":"10.1155/2021/2934943","title":"Group Behavior Pattern Recognition Algorithm Based on Spatio-Temporal Graph Convolutional Networks","year":2021,"lang":"en","type":"article","venue":"Scientific Programming","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"China Scholarship Council","keywords":"Computer science; Artificial intelligence; Group behavior; Crowd psychology; Pattern recognition (psychology); Graph; Convolutional neural network; Group (periodic table); Population; Theoretical computer science","score_opus":0.03495288141130015,"score_gpt":0.27890200205288973,"score_spread":0.24394912064158958,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3182753879","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":[0.11915223,0.00032265155,0.87279713,0.00038193693,0.00009228038,0.00018125538,0.00042076537,0.0031201367,0.0035316437],"genre_scores_gemma":[0.8181399,0.00029205703,0.17247391,0.00016659818,0.000042156415,0.00018401125,0.0012041753,0.00009568618,0.007401473],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997178,0.000025786669,0.00001562937,0.000105838924,0.000076302815,0.00005868764],"domain_scores_gemma":[0.99975854,0.000040413808,0.00004418228,0.000029907034,0.00010067274,0.000026272726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037299257,0.0008091678,0.0005936015,0.0013437611,0.00037073705,0.00045343547,0.0013255595,0.0005841291,0.0011735489],"category_scores_gemma":[0.00081536634,0.00027903076,0.00062184903,0.0008715334,0.00029068528,0.00083260966,0.00051953114,0.0006925265,0.0004092913],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029412226,0.00030738927,0.010726919,0.000080254875,0.000176545,0.00021822193,0.00013564188,0.2818338,0.01897405,0.0044695837,0.00613116,0.6766523],"study_design_scores_gemma":[0.0000045796264,0.00001767272,0.001002165,0.0000024941278,0.000011483442,0.000024338422,0.000010201601,0.9958163,0.0020186442,0.00074325537,0.0003449604,0.000003918787],"about_ca_topic_score_codex":0.033794917,"about_ca_topic_score_gemma":0.03143321,"teacher_disagreement_score":0.033794917,"about_ca_system_score_codex":0.0010746338,"about_ca_system_score_gemma":0.0010435287,"threshold_uncertainty_score":0.06719643},"labels":[],"label_agreement":null},{"id":"W3183941876","doi":"10.18280/ts.380307","title":"A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Liaoning Revitalization Talents Program","keywords":"Artificial intelligence; Pattern recognition (psychology); Fitness function; Computer science; Classifier (UML); Support vector machine; Feature (linguistics); Computer vision; Motion (physics); Genetic algorithm; Feature extraction; Feature selection; Machine learning","score_opus":0.03611304983442879,"score_gpt":0.27321124685132747,"score_spread":0.2370981970168987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3183941876","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014261276,0.0001671504,0.9836975,0.00007252623,0.00003031057,0.000032463933,0.000022429942,0.00031677855,0.0013996006],"genre_scores_gemma":[0.71610034,0.00036785245,0.27779412,0.00012010138,0.000036469595,0.0002988638,0.00014461014,0.00007745215,0.0050602565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997179,0.000043086067,0.000014649932,0.00010456827,0.000083794665,0.00003597165],"domain_scores_gemma":[0.9998442,0.000049054517,0.00002266466,0.000013394679,0.00006158315,0.0000091169195],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036131387,0.0006327529,0.00078267546,0.00064470846,0.00037768923,0.0006122044,0.001044586,0.0008317014,0.000767299],"category_scores_gemma":[0.0006688254,0.0003132047,0.0008362297,0.0006279342,0.00037763538,0.00073844154,0.00034530886,0.0006336119,0.00023708149],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00003229592,0.000029910516,0.00081350474,0.00002824855,0.000041622035,0.000052886284,0.000042386528,0.9202456,0.0059628636,0.0034753836,0.00039521555,0.068880126],"study_design_scores_gemma":[0.0000021304334,0.000012177914,0.00011583541,0.0000016778785,0.0000042629867,0.000011280229,0.0000015094895,0.9989172,0.0003845821,0.00040153673,0.0001441786,0.0000036536946],"about_ca_topic_score_codex":0.013776331,"about_ca_topic_score_gemma":0.006312507,"teacher_disagreement_score":0.013776331,"about_ca_system_score_codex":0.0007510937,"about_ca_system_score_gemma":0.00083385425,"threshold_uncertainty_score":0.027392268},"labels":[],"label_agreement":null},{"id":"W3185147019","doi":"10.1007/s11554-021-01156-1","title":"Special issue on deep learning for emerging embedded real-time image and video processing systems","year":2021,"lang":"en","type":"article","venue":"Journal of Real-Time Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Chicoutimi","funders":"","keywords":"Computer science; Image processing; Artificial intelligence; Deep learning; Computer vision; Image (mathematics); Multimedia","score_opus":0.014056585781232287,"score_gpt":0.30720523911284614,"score_spread":0.29314865333161383,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3185147019","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":[0.0047423826,0.07917237,0.058205977,0.02269329,0.75640535,0.00036020437,0.0015255315,0.0013532756,0.07554155],"genre_scores_gemma":[0.02375614,0.05023651,0.017237868,0.005753015,0.5081228,0.000266381,0.0039089583,0.001506215,0.38921213],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990688,0.00012037238,0.00006930569,0.00023473131,0.0003978383,0.00010898098],"domain_scores_gemma":[0.99728954,0.0006832908,0.00012754781,0.0002607771,0.0010848319,0.0005540161],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018133561,0.0016355133,0.0018609972,0.001705574,0.0008726236,0.003262809,0.0019336445,0.002262839,0.08717585],"category_scores_gemma":[0.002743956,0.00046575285,0.00097082753,0.0014791108,0.00069496775,0.0027233618,0.0021047657,0.0030165135,0.020881575],"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":[0.000109298955,0.00011042368,0.0002655194,0.00062507327,0.00007943794,0.00013848528,0.00001841987,0.0013416441,0.0015760793,0.0058957366,0.86219215,0.1276477],"study_design_scores_gemma":[0.000051513314,0.0003984471,0.0014769775,0.00038489138,0.000094118346,0.00052127277,0.000039220595,0.01779521,0.0021192094,0.018212099,0.9588631,0.00004400297],"about_ca_topic_score_codex":0.0007371071,"about_ca_topic_score_gemma":0.0018092722,"teacher_disagreement_score":0.08717585,"about_ca_system_score_codex":0.0009691488,"about_ca_system_score_gemma":0.0013707426,"threshold_uncertainty_score":0.29163224},"labels":[],"label_agreement":null},{"id":"W3188089627","doi":"10.1109/icc42927.2021.9500776","title":"An Efficient Real-Time Vehicle Re-Identification Scheme Using Urban Surveillance Videos","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Concatenation (mathematics); Discriminative model; Benchmark (surveying); Artificial intelligence; Identification (biology); Feature extraction; Scheme (mathematics); Feature (linguistics); Pattern recognition (psychology); Computer vision","score_opus":0.029552381418095262,"score_gpt":0.313481472521853,"score_spread":0.28392909110375775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188089627","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18445066,0.0012586976,0.8007373,0.0002984107,0.00025890695,0.0002879045,0.00070922205,0.007121259,0.004877562],"genre_scores_gemma":[0.8400631,0.0005527099,0.15151599,0.00019123855,0.00008026707,0.0000897405,0.0015391082,0.00011191404,0.0058559747],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968755,0.000031739033,0.00001610138,0.00012773629,0.0000758894,0.000061011597],"domain_scores_gemma":[0.99973756,0.000041229097,0.00003278011,0.00006802179,0.00009767368,0.00002262221],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046576327,0.0009540688,0.0008103993,0.0011458468,0.00028941434,0.00046329512,0.0014164924,0.00052616856,0.0011164215],"category_scores_gemma":[0.0008533067,0.00031151087,0.00051749486,0.0006429055,0.00023384356,0.0011276379,0.0007748355,0.00056058355,0.0007497101],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005261642,0.00024551328,0.0023206999,0.00011715369,0.00008853951,0.00025982215,0.00007047005,0.13197023,0.036259048,0.0021010172,0.006177303,0.819864],"study_design_scores_gemma":[0.000010457691,0.00007298992,0.00084340264,0.000006805813,0.000022504668,0.00009434839,0.000021700977,0.9850339,0.011819819,0.00068839925,0.0013750179,0.000010629358],"about_ca_topic_score_codex":0.009423747,"about_ca_topic_score_gemma":0.010044187,"teacher_disagreement_score":0.009423747,"about_ca_system_score_codex":0.0007679537,"about_ca_system_score_gemma":0.0006993765,"threshold_uncertainty_score":0.018737733},"labels":[],"label_agreement":null},{"id":"W3188801641","doi":"10.14569/ijacsa.2021.0120701","title":"Edge-based Video Analytic for Smart Cities","year":2021,"lang":"en","type":"article","venue":"International Journal of Advanced Computer Science and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Video tracking; Cloud computing; Convolutional neural network; Enhanced Data Rates for GSM Evolution; Edge device; Artificial intelligence; Real-time computing; Analytics; Edge computing; Bandwidth (computing); Video processing; Computer vision; Data mining; Computer network","score_opus":0.020364381339458924,"score_gpt":0.32723712658793913,"score_spread":0.3068727452484802,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3188801641","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.060076226,0.00066034554,0.9249467,0.0004863618,0.00011243203,0.00008507341,0.00030935198,0.001623783,0.011699654],"genre_scores_gemma":[0.8785146,0.00083234353,0.11357151,0.00021184246,0.000048860704,0.00006722455,0.00047918144,0.00009414052,0.006180216],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99985266,0.000022755017,0.0000055946325,0.000036461624,0.000054669228,0.00002781361],"domain_scores_gemma":[0.9998369,0.000034293626,0.000022337388,0.000022845046,0.000065491215,0.0000181349],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001941267,0.00041659732,0.00030995617,0.00040216692,0.00037673357,0.00068418565,0.000799178,0.0005697129,0.0018345027],"category_scores_gemma":[0.00048606773,0.00018526072,0.00036633905,0.00045711175,0.00026743463,0.0014201489,0.00058803125,0.00049087283,0.00049804733],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032442616,0.00013580458,0.0045573576,0.00014416974,0.000037941252,0.00027663368,0.000119105134,0.7537487,0.030083347,0.021840308,0.00907303,0.17965908],"study_design_scores_gemma":[0.000004194564,0.00001799445,0.00033729064,0.000003737824,0.0000044452995,0.000018283507,0.000014297912,0.9931803,0.002777126,0.0018840552,0.0017531172,0.000005176327],"about_ca_topic_score_codex":0.013801737,"about_ca_topic_score_gemma":0.009290032,"teacher_disagreement_score":0.013801737,"about_ca_system_score_codex":0.0008609007,"about_ca_system_score_gemma":0.0006000804,"threshold_uncertainty_score":0.027442753},"labels":[],"label_agreement":null},{"id":"W3189878313","doi":"10.1016/j.jvcir.2021.103250","title":"Sequence-tracker: Multiple object tracking with sequence features in severe occlusion scene","year":2021,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University; University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Computer science; Computer vision; Video tracking; Tracking (education); Object (grammar); Frame (networking); Sequence (biology); Feature (linguistics); Identification (biology); Association (psychology); Trajectory; Pattern recognition (psychology)","score_opus":0.05556893168682955,"score_gpt":0.39456592500009413,"score_spread":0.3389969933132646,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3189878313","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009682219,0.000286353,0.98165846,0.000037181522,0.00012805143,0.000081204016,0.00026566468,0.007342545,0.00051837123],"genre_scores_gemma":[0.11109382,0.00028649878,0.8818682,0.000098406315,0.000088536006,0.00014215027,0.0015097915,0.00072765566,0.0041849716],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999332,0.00009425675,0.00002573236,0.00021206577,0.00026573348,0.00007010114],"domain_scores_gemma":[0.9994597,0.00016428246,0.00004432351,0.00014370505,0.0001297377,0.00005821645],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015205462,0.0011518806,0.0015425105,0.0010849135,0.00047481197,0.00082970626,0.0016567137,0.001448434,0.0030650045],"category_scores_gemma":[0.0022839252,0.0007480533,0.00051538995,0.0014287089,0.00034202336,0.001244407,0.0016331271,0.0009928969,0.002467829],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001028738,0.00022302425,0.0019465038,0.00019824953,0.00024006594,0.00035061614,0.00012876866,0.03843372,0.06519646,0.0035675268,0.017505225,0.8711812],"study_design_scores_gemma":[0.000074905845,0.00012814188,0.0011867196,0.000010337415,0.000031331863,0.00032203316,0.000017819308,0.975801,0.015691651,0.0018508794,0.004862252,0.000022978254],"about_ca_topic_score_codex":0.004936746,"about_ca_topic_score_gemma":0.0075350646,"teacher_disagreement_score":0.004936746,"about_ca_system_score_codex":0.00033500438,"about_ca_system_score_gemma":0.0008490583,"threshold_uncertainty_score":0.010253429},"labels":[],"label_agreement":null},{"id":"W3192039469","doi":"10.1155/2021/8153474","title":"Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation and Constrained Multiple Kernels","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Higher Education Discipline Innovation Project; China Scholarship Council","keywords":"Computer vision; Artificial intelligence; Computer science; Tracking (education); Tracking system; Ground plane; Kalman filter; Kernel (algebra); Image plane; Video tracking; 3D pose estimation; Pose; Track (disk drive); Noise (video); Object (grammar); Image (mathematics); Mathematics","score_opus":0.030816292199768378,"score_gpt":0.2979665789017782,"score_spread":0.2671502867020098,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3192039469","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02512729,0.00021683893,0.97283,0.000032554653,0.000042050222,0.000020392243,0.000032056818,0.0010490792,0.0006498989],"genre_scores_gemma":[0.61725086,0.00043016073,0.37912613,0.00007215216,0.000046404773,0.00006050869,0.00031757395,0.00013411925,0.0025620323],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946016,0.000050632876,0.00002466214,0.00023744111,0.00017254503,0.000054625467],"domain_scores_gemma":[0.99941146,0.0000835111,0.00011239802,0.0001388007,0.00022249311,0.00003137981],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042498932,0.00063562364,0.0008778304,0.0009861153,0.00040857928,0.0007922714,0.0012421168,0.0006647447,0.00076643005],"category_scores_gemma":[0.0015123938,0.00038834164,0.0005691756,0.0013062777,0.00034511846,0.0013488233,0.0009868273,0.00071776565,0.00057822093],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003187954,0.00012833429,0.005188164,0.00014384551,0.00017390441,0.00022373426,0.00030185448,0.10082339,0.09352618,0.005013558,0.0026203496,0.791538],"study_design_scores_gemma":[0.000014703933,0.0000628534,0.0022774385,0.000009824156,0.00003944561,0.00014957332,0.000027699116,0.9753941,0.01917083,0.0010160655,0.0018102583,0.000027323022],"about_ca_topic_score_codex":0.009202655,"about_ca_topic_score_gemma":0.0064750197,"teacher_disagreement_score":0.009202655,"about_ca_system_score_codex":0.0005524005,"about_ca_system_score_gemma":0.0006689704,"threshold_uncertainty_score":0.018298209},"labels":[],"label_agreement":null},{"id":"W3194123727","doi":"10.1109/icip42928.2021.9506698","title":"ShuffleCount: Task-Specific Knowledge Distillation for Crowd Counting","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Benchmark (surveying); Computer science; Distillation; Task (project management); Artificial intelligence; Feature (linguistics); Machine learning; Code (set theory); Task analysis; Computational complexity theory; Algorithm","score_opus":0.043009316618085505,"score_gpt":0.318982189110016,"score_spread":0.27597287249193053,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194123727","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039399818,0.00091265974,0.9363676,0.0006014462,0.00030382862,0.00022645779,0.0010290514,0.015694115,0.005465052],"genre_scores_gemma":[0.5309368,0.00052789046,0.44763634,0.0009172464,0.00020752268,0.0005240829,0.003949897,0.0012058299,0.014094461],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999453,0.00009857723,0.000021951839,0.00021419935,0.00011059062,0.000101732214],"domain_scores_gemma":[0.9993304,0.00022215578,0.000064341075,0.00018160103,0.000118186836,0.00008336381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011340362,0.0021311683,0.0014844828,0.0010477523,0.0010885679,0.0012136388,0.0038100719,0.0020589072,0.005567672],"category_scores_gemma":[0.0037166837,0.000691301,0.0009856133,0.00086615136,0.0012357103,0.0038827404,0.0036939846,0.0028694554,0.0018839148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00061344926,0.00039072864,0.0020854715,0.0004184998,0.00019510264,0.00028276973,0.000301759,0.3487749,0.012552648,0.01977445,0.026093856,0.58851635],"study_design_scores_gemma":[0.000033437413,0.000060273793,0.0001686287,0.000021733336,0.000016771988,0.00004507144,0.000031546453,0.9792533,0.0060555767,0.011309531,0.0029849545,0.000019147654],"about_ca_topic_score_codex":0.008000595,"about_ca_topic_score_gemma":0.012985181,"teacher_disagreement_score":0.008000595,"about_ca_system_score_codex":0.0013113081,"about_ca_system_score_gemma":0.0019109825,"threshold_uncertainty_score":0.018625736},"labels":[],"label_agreement":null},{"id":"W3194322321","doi":"10.1061/jtepbs.0000596","title":"Effect of Redesigning Public Shared Space Amid the COVID-19 Pandemic on Physical Distancing and Traffic Safety","year":2021,"lang":"en","type":"article","venue":"Journal of Transportation Engineering Part A Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Alberta Environment and Protected Areas; University of Alberta","funders":"","keywords":"Distancing; Pandemic; Social distance; Coronavirus disease 2019 (COVID-19); Computer security; Public space; Space (punctuation); Computer science; Transport engineering; Physical space; Business; Internet privacy; Engineering; Geography; Architectural engineering; Medicine; Cartography","score_opus":0.02955777338463437,"score_gpt":0.28597381440550984,"score_spread":0.25641604102087545,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194322321","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":[0.9910047,0.000058386744,0.0072169895,0.00012918554,0.000023823153,0.00010993108,0.00012309173,0.00029229643,0.0010415296],"genre_scores_gemma":[0.9924315,0.000023693428,0.0071631363,0.00002769604,0.0000040209993,0.00004142292,0.000109142675,0.000009376891,0.00018987681],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.9983277,0.00074581127,0.0000782491,0.0002844703,0.00021151005,0.0003522642],"domain_scores_gemma":[0.9957386,0.0019034056,0.0007731384,0.0006063678,0.0005700415,0.00040846205],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025603678,0.000687135,0.00040827776,0.0009127741,0.00061198155,0.001021545,0.0008421274,0.001007453,0.0012953584],"category_scores_gemma":[0.010094156,0.00023249055,0.00040924,0.0006018712,0.00079692167,0.0016068975,0.0012447408,0.00050147314,0.00017713656],"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":[0.0022659164,0.0041367714,0.25538746,0.00044861317,0.00028723318,0.00058963377,0.0021047213,0.39160046,0.030990183,0.0038759992,0.0028273084,0.30548573],"study_design_scores_gemma":[0.00018614344,0.0042088544,0.27286547,0.00010319499,0.00028026666,0.00024627,0.0075479564,0.6848959,0.020900905,0.0024317375,0.006172039,0.00016129878],"about_ca_topic_score_codex":0.019194428,"about_ca_topic_score_gemma":0.020039996,"teacher_disagreement_score":0.019194428,"about_ca_system_score_codex":0.0013925452,"about_ca_system_score_gemma":0.0018809921,"threshold_uncertainty_score":0.03816539},"labels":[],"label_agreement":null},{"id":"W3194940645","doi":"10.1109/iccv48922.2021.01160","title":"Explainable Person Re-Identification with Attribute-guided Metric Distillation","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Discriminative model; Computer science; Metric (unit); Interpreter; Artificial intelligence; Identification (biology); Machine learning; Convolutional neural network; Distillation; Focus (optics); Natural language processing; Data mining; Information retrieval","score_opus":0.09240357064307565,"score_gpt":0.34621654228643334,"score_spread":0.2538129716433577,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3194940645","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023919199,0.00029300377,0.96512634,0.00060080463,0.00008815684,0.000059727547,0.0004948852,0.007048379,0.0023696397],"genre_scores_gemma":[0.60816,0.0003732408,0.37649152,0.0009147449,0.00010739611,0.0001633708,0.0026229797,0.000991803,0.010174816],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99931633,0.00019790894,0.000022874214,0.00028751075,0.0001011254,0.00007420751],"domain_scores_gemma":[0.9991474,0.00031533383,0.000089670575,0.00028597875,0.00010250306,0.000059017693],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010562345,0.0016647292,0.00078940473,0.0007281587,0.0004731128,0.0009614922,0.0023608059,0.001590648,0.0047953445],"category_scores_gemma":[0.004091966,0.0005995479,0.0015879438,0.0006576734,0.000961457,0.0032850837,0.0031733387,0.002790396,0.0016366107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051263,0.00018650354,0.004792834,0.0002248744,0.00024898638,0.00066003273,0.0007921446,0.47404248,0.010108303,0.04819892,0.018814735,0.4414176],"study_design_scores_gemma":[0.000011316852,0.000035859463,0.00031825536,0.000013917788,0.000018169452,0.00010388696,0.000037186735,0.9668568,0.0037075917,0.02589497,0.0029817643,0.000020342824],"about_ca_topic_score_codex":0.006332347,"about_ca_topic_score_gemma":0.007750493,"teacher_disagreement_score":0.006332347,"about_ca_system_score_codex":0.0011414868,"about_ca_system_score_gemma":0.00082661706,"threshold_uncertainty_score":0.016041994},"labels":[],"label_agreement":null},{"id":"W3195170463","doi":"10.1109/icip42928.2021.9506051","title":"IDECF: Improved Deep Embedding Clustering With Deep Fuzzy Supervision","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University","funders":"","keywords":"Autoencoder; Cluster analysis; Computer science; Artificial intelligence; Fuzzy clustering; Deep learning; Clustering high-dimensional data; Correlation clustering; Benchmark (surveying); Artificial neural network; Data stream clustering; Pattern recognition (psychology); CURE data clustering algorithm; Canopy clustering algorithm; Data mining","score_opus":0.017827085047699854,"score_gpt":0.28394728280425535,"score_spread":0.2661201977565555,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195170463","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":[0.011735471,0.00023001226,0.9844126,0.00009763027,0.00004778724,0.00005412077,0.00013918923,0.002252641,0.0010306041],"genre_scores_gemma":[0.26214197,0.00023400562,0.728587,0.00033419306,0.00005571276,0.00016023003,0.0014152414,0.00038201752,0.006689665],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99935335,0.00008189172,0.00002795109,0.0002186196,0.00022923073,0.00008892958],"domain_scores_gemma":[0.9992323,0.00016137179,0.000076189506,0.00018751637,0.00028319092,0.000059470767],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011209117,0.0013698896,0.0014391526,0.0012189838,0.000692574,0.0008841753,0.003156883,0.0017610766,0.0021447039],"category_scores_gemma":[0.0024335496,0.00066543877,0.00088972424,0.0010608864,0.0006696089,0.0020553882,0.001841772,0.0021774117,0.0010942828],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024666282,0.00023639442,0.0014039708,0.000099361845,0.00012473433,0.00011343973,0.00013191669,0.4224789,0.012733071,0.008241347,0.010130236,0.54406],"study_design_scores_gemma":[0.000008111794,0.00002391556,0.00010266428,0.000005174841,0.0000042040156,0.000026925092,0.000007215337,0.99562895,0.001820221,0.0018587363,0.00050522696,0.0000086710315],"about_ca_topic_score_codex":0.013643366,"about_ca_topic_score_gemma":0.020130306,"teacher_disagreement_score":0.013643366,"about_ca_system_score_codex":0.0013662805,"about_ca_system_score_gemma":0.0017456174,"threshold_uncertainty_score":0.027127862},"labels":[],"label_agreement":null},{"id":"W3195489957","doi":"10.1155/2021/4592124","title":"Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China Stem Cell and Translational Research; National Key Research and Development Program of China","keywords":"Robustness (evolution); Computer science; Sensor fusion; Computer vision; Data collection; Radar; Artificial intelligence; Adaptability; Detector; Clutter; Remote sensing; Mathematics; Geography","score_opus":0.03093778077607018,"score_gpt":0.293678065530852,"score_spread":0.26274028475478184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3195489957","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":[0.44140273,0.00037768786,0.54688096,0.00023086152,0.00018598116,0.00015318254,0.0013305653,0.0039686384,0.0054693567],"genre_scores_gemma":[0.86256796,0.0002700628,0.13262449,0.00009350735,0.00007943467,0.00009392078,0.0029069788,0.00011127456,0.0012522682],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950874,0.00005642211,0.000020195625,0.00017394839,0.00014956012,0.00009122466],"domain_scores_gemma":[0.9996245,0.000043003885,0.000043381366,0.00007518365,0.00018727501,0.000026634425],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047590488,0.00076230045,0.0006492717,0.0015924611,0.0002954984,0.0005821028,0.00062887155,0.00044612098,0.0005297614],"category_scores_gemma":[0.0011889595,0.00021808251,0.00053381897,0.0010318053,0.00029917533,0.0012039366,0.0007411018,0.00067358935,0.0003863445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046556932,0.00043116207,0.01836433,0.00020739611,0.00017601058,0.00020553893,0.00022736272,0.100226626,0.12312605,0.0014284228,0.0060855146,0.749056],"study_design_scores_gemma":[0.000026564432,0.00013002628,0.023175642,0.000021147081,0.00009112268,0.0001922386,0.00015435524,0.89910054,0.07173815,0.0021335585,0.0031828033,0.000053915806],"about_ca_topic_score_codex":0.0027508643,"about_ca_topic_score_gemma":0.0030306245,"teacher_disagreement_score":0.0027508643,"about_ca_system_score_codex":0.00040527948,"about_ca_system_score_gemma":0.00047264848,"threshold_uncertainty_score":0.0054697394},"labels":[],"label_agreement":null},{"id":"W3196188284","doi":"10.11159/mvml21.101","title":"New Object Tracker Based On Adaptive Intensity Models of Object and Its Surroundings","year":2021,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Vlaamse regering; Flanders Make; Agentschap Innoveren en Ondernemen","keywords":"Computer vision; Object (grammar); Artificial intelligence; Computer science; Intensity (physics); Physics; Optics","score_opus":0.018612674703070518,"score_gpt":0.23220399322579274,"score_spread":0.21359131852272223,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3196188284","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0041422863,0.0002447519,0.99402905,0.000030523708,0.000054475247,0.000018143019,0.000030419384,0.0010095838,0.00044080665],"genre_scores_gemma":[0.1357958,0.0009772227,0.8558473,0.00018618979,0.00013788002,0.000114862654,0.0006444425,0.00058344414,0.0057127397],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999161,0.000075655385,0.00003560301,0.0003430463,0.00033422868,0.000050443803],"domain_scores_gemma":[0.9993605,0.00016163326,0.000072617484,0.00012737609,0.00023170169,0.0000460874],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012304187,0.0006964211,0.0012687112,0.0010641493,0.00036124652,0.0012504577,0.0013535732,0.0013173216,0.0014522307],"category_scores_gemma":[0.001912938,0.00061989273,0.0010627634,0.0011840503,0.0005000095,0.0021992894,0.001185862,0.001228067,0.001170511],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026814,0.000099775316,0.0053191734,0.00021797506,0.00024404806,0.00018974826,0.00026925927,0.08356489,0.095828205,0.007731967,0.0063060997,0.79996073],"study_design_scores_gemma":[0.000025009867,0.00010321792,0.002804744,0.000021757753,0.00006641783,0.0005373386,0.000029718114,0.9544888,0.027587231,0.0028175735,0.011471885,0.0000462627],"about_ca_topic_score_codex":0.0025784944,"about_ca_topic_score_gemma":0.0023861795,"teacher_disagreement_score":0.0025784944,"about_ca_system_score_codex":0.0005571404,"about_ca_system_score_gemma":0.000859778,"threshold_uncertainty_score":0.0065071583},"labels":[],"label_agreement":null},{"id":"W3199689759","doi":"10.1155/2021/4427945","title":"Visual Object Tracking with Online Updating for Car Sharing Services","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Jinling Institute of Technology; Government of Jiangsu Province","keywords":"Intersection (aeronautics); Computer science; Minimum bounding box; Video tracking; Tracking (education); Computer vision; Object (grammar); Artificial intelligence; Frame (networking); Baseline (sea); Eye tracking; Bounding overwatch; Key (lock); Frame rate; Image (mathematics); Computer security; Engineering; Computer network","score_opus":0.018931116319336982,"score_gpt":0.3227854796358846,"score_spread":0.30385436331654764,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3199689759","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.042345084,0.0008380792,0.9519784,0.00012640344,0.000117177085,0.00005637853,0.00009076323,0.0030905646,0.0013571285],"genre_scores_gemma":[0.8333698,0.0004772274,0.1619902,0.00016891942,0.00013923703,0.000064610664,0.00039198925,0.0002206794,0.0031772968],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99854386,0.00016002246,0.000054895576,0.0005035518,0.0005683006,0.00016940471],"domain_scores_gemma":[0.9984964,0.0002948363,0.0002131265,0.00046623664,0.00041966658,0.00010957951],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012717558,0.0009831716,0.0016064299,0.00095254753,0.0006974848,0.0012383567,0.0025986827,0.0010427843,0.0010810415],"category_scores_gemma":[0.0049208337,0.000573195,0.0004916431,0.0012939656,0.0005315867,0.001835743,0.0015932067,0.0012336322,0.0010209647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004437117,0.00018767359,0.00794848,0.00013902469,0.000108024906,0.00028009978,0.0003760704,0.15557624,0.027600428,0.0035861093,0.0050138896,0.7987402],"study_design_scores_gemma":[0.000007710572,0.00004501865,0.0009975231,0.0000051003835,0.00002175432,0.00015129049,0.00003567108,0.9903846,0.0054273405,0.0011580589,0.0017520763,0.000013784833],"about_ca_topic_score_codex":0.014143415,"about_ca_topic_score_gemma":0.008435929,"teacher_disagreement_score":0.014143415,"about_ca_system_score_codex":0.0009084149,"about_ca_system_score_gemma":0.0012642912,"threshold_uncertainty_score":0.028122187},"labels":[],"label_agreement":null},{"id":"W3200294965","doi":"10.1007/s00521-021-06439-z","title":"Real-time stage-wise object tracking in traffic scenes: an online tracker selection method via deep reinforcement learning","year":2021,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Computational Science and Engineering; Artificial intelligence; Computer science; Reinforcement learning; Tracking (education); Selection (genetic algorithm); Object (grammar); Computer vision; Online learning; Video tracking; Stage (stratigraphy); Pattern recognition (psychology); Machine learning; Geology; Psychology","score_opus":0.03488067607535853,"score_gpt":0.34382145139547754,"score_spread":0.308940775320119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3200294965","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019653352,0.00023873361,0.97852933,0.00010396668,0.00005565753,0.00003427741,0.000030196272,0.0006453487,0.0007091755],"genre_scores_gemma":[0.7062712,0.00021766426,0.28800547,0.00028023077,0.00009136697,0.00010676422,0.00024201054,0.00018025203,0.004605075],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995555,0.00007975992,0.000019187526,0.00017310589,0.00010107023,0.00007139366],"domain_scores_gemma":[0.9990345,0.00044883825,0.00010210164,0.00009521241,0.00020291416,0.00011649659],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013351891,0.0009120913,0.0015306058,0.00049536454,0.00039918377,0.00066201034,0.002222379,0.0014349273,0.0018197171],"category_scores_gemma":[0.0024067413,0.00083499955,0.0005865216,0.000582448,0.00058883574,0.00095056155,0.0012803552,0.0016024222,0.0006466632],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006007773,0.00030123908,0.0026398718,0.0001259998,0.00016019114,0.00019194523,0.00012868602,0.5484419,0.017387388,0.004923001,0.0043237302,0.42077532],"study_design_scores_gemma":[0.000008152946,0.000016843862,0.0000951951,0.0000018488753,0.000005680156,0.000013191817,0.0000017058793,0.9989844,0.00045127762,0.00031813027,0.000100435565,0.000002995691],"about_ca_topic_score_codex":0.008083832,"about_ca_topic_score_gemma":0.010303816,"teacher_disagreement_score":0.008083832,"about_ca_system_score_codex":0.0006153103,"about_ca_system_score_gemma":0.001223813,"threshold_uncertainty_score":0.016073525},"labels":[],"label_agreement":null},{"id":"W3201550731","doi":"10.14569/ijacsa.2021.0120902","title":"Monitoring Indoor Activity of Daily Living using Thermal Imaging: A Case Study","year":2021,"lang":"en","type":"preprint","venue":"International Journal of Advanced Computer Science and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Activities of daily living; Computer science; Identification (biology); Assisted living; Internet of Things; Real-time computing; Dependency (UML); Field (mathematics); Stability (learning theory); Artificial intelligence; Environmental science; Human–computer interaction; Computer vision; Machine learning; Internet privacy; Psychology; Ecology; Mathematics; Gerontology; Medicine","score_opus":0.04028310823059859,"score_gpt":0.376168591546849,"score_spread":0.3358854833162504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201550731","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":[0.96431047,0.0010810768,0.028052408,0.0005769425,0.000060579332,0.00020442795,0.00064768235,0.0001839812,0.00488248],"genre_scores_gemma":[0.979157,0.00066173595,0.017131785,0.000116901734,0.000048199025,0.00008304941,0.00024772418,0.000024052957,0.0025296304],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993824,0.00022348805,0.000039950035,0.00011997322,0.00015784176,0.00007637162],"domain_scores_gemma":[0.99892896,0.00050813914,0.000116632786,0.00011583069,0.00019647286,0.00013398081],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000511774,0.000447938,0.00041735137,0.0006796538,0.0006027202,0.0007345515,0.0006831341,0.0013169171,0.0010869959],"category_scores_gemma":[0.0014361577,0.00015913496,0.0005056867,0.0008928125,0.00051812246,0.0005472093,0.0004712047,0.00052301714,0.00034410474],"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":[0.00271823,0.0047245445,0.3498403,0.003018525,0.0006563688,0.118398465,0.015648853,0.057233237,0.07408265,0.007204367,0.018505018,0.34796947],"study_design_scores_gemma":[0.00029481453,0.005330435,0.3944504,0.0005834779,0.0007044668,0.10769872,0.03097993,0.28336126,0.10361461,0.0060933502,0.066439375,0.00044906413],"about_ca_topic_score_codex":0.004782779,"about_ca_topic_score_gemma":0.008483177,"teacher_disagreement_score":0.004782779,"about_ca_system_score_codex":0.00042605333,"about_ca_system_score_gemma":0.00026208186,"threshold_uncertainty_score":0.0095098615},"labels":[],"label_agreement":null},{"id":"W3201592903","doi":"10.1109/ijcnn52387.2021.9533905","title":"Efficient Parameter Based Online Object Tracking","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Pattern recognition (psychology); Histogram; Discriminative model; Video tracking; Computer science; Dirichlet distribution; Mixture model; Latent Dirichlet allocation; Computer vision; Feature (linguistics); Representation (politics); Object (grammar); Generative model; Mathematics; Image (mathematics); Topic model; Generative grammar","score_opus":0.045687352586420434,"score_gpt":0.31532015877997216,"score_spread":0.26963280619355173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3201592903","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":[0.006367907,0.00022079179,0.99017406,0.000033377106,0.000026782962,0.000023639444,0.000058059402,0.0020775003,0.0010178745],"genre_scores_gemma":[0.39266858,0.0004058867,0.59867847,0.0001356856,0.000072082694,0.00014356863,0.0009792688,0.00038020717,0.006536249],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99875104,0.00015821391,0.000056589426,0.00040568033,0.00050412794,0.00012443958],"domain_scores_gemma":[0.99887246,0.00034871485,0.00011089117,0.00037540557,0.00023755465,0.000054953878],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010555353,0.0010190527,0.002392158,0.0014018809,0.0005945528,0.0013280654,0.0024544857,0.0013796792,0.002314754],"category_scores_gemma":[0.002744013,0.0005812376,0.0008128715,0.0020002525,0.000525388,0.0020874427,0.0017668734,0.0012728361,0.0022501156],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024680566,0.00016823511,0.0011235516,0.000076030665,0.00006761489,0.00009977367,0.000100684694,0.18963753,0.021376727,0.0076100095,0.0043893745,0.7751037],"study_design_scores_gemma":[0.000008553236,0.000017588689,0.00032823678,0.000003828441,0.000006965945,0.00008003417,0.000009346512,0.9900856,0.0044764667,0.0037676904,0.0012036682,0.000012139707],"about_ca_topic_score_codex":0.005038176,"about_ca_topic_score_gemma":0.0061909393,"teacher_disagreement_score":0.005038176,"about_ca_system_score_codex":0.0009907405,"about_ca_system_score_gemma":0.0012705451,"threshold_uncertainty_score":0.010017693},"labels":[],"label_agreement":null},{"id":"W3202312310","doi":"10.1109/iccv48922.2021.01076","title":"BV-Person: A Large-scale Dataset for Bird-view Person Re-identification","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Identification (biology); Focus (optics); Object (grammar); Scale (ratio); Computer vision; Geography; Cartography","score_opus":0.10271386693878644,"score_gpt":0.3737466514381948,"score_spread":0.2710327844994084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202312310","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.080403894,0.016752413,0.043241683,0.0013278897,0.0026408974,0.0024567158,0.8022297,0.029999595,0.020947201],"genre_scores_gemma":[0.033892207,0.0010048931,0.037405524,0.0003969346,0.0001226551,0.0004903566,0.9225065,0.00037901686,0.0038019165],"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99586487,0.0006293473,0.00037288107,0.0013483117,0.0012768335,0.0005077677],"domain_scores_gemma":[0.9964122,0.00039681184,0.0004118051,0.0015299255,0.00092476985,0.00032453836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022527378,0.0051691234,0.0029561298,0.004991805,0.002417334,0.0019361888,0.0074290656,0.004536754,0.0096188905],"category_scores_gemma":[0.0055517596,0.0008422694,0.003087626,0.0052734613,0.00089729705,0.0036738596,0.004154101,0.002922619,0.019173106],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014569485,0.00094173936,0.013028785,0.0032867743,0.0008334458,0.0011570675,0.00031583934,0.0058774636,0.007470087,0.0016508931,0.7751013,0.18887965],"study_design_scores_gemma":[0.00058348017,0.0008911803,0.094613984,0.0016452885,0.0006409294,0.010288313,0.0019636608,0.09382466,0.024957247,0.004988539,0.7649488,0.0006539812],"about_ca_topic_score_codex":0.037580617,"about_ca_topic_score_gemma":0.08822694,"teacher_disagreement_score":0.037580617,"about_ca_system_score_codex":0.0022911658,"about_ca_system_score_gemma":0.002139125,"threshold_uncertainty_score":0.07472372},"labels":[],"label_agreement":null},{"id":"W3202375650","doi":"10.1155/2021/7669438","title":"Detection for Dangerous Goods Vehicles in Expressway Service Station Based on Surveillance Videos","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Fundamental Research Funds for the Central Universities","keywords":"Service (business); Process (computing); Dangerous goods; Computer science; Workload; Computer security; Real-time computing; Transport engineering; Engineering","score_opus":0.01913540360734172,"score_gpt":0.29022059926492033,"score_spread":0.2710851956575786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202375650","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":[0.6014043,0.00048243487,0.39156628,0.0001108292,0.00008199957,0.000097516204,0.0004275357,0.001446881,0.0043822094],"genre_scores_gemma":[0.9175319,0.0005186475,0.07875693,0.000046455072,0.000040442243,0.000030647236,0.00073989003,0.000044756816,0.0022902696],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998667,0.000011558477,0.000004630621,0.00003898944,0.00004941143,0.000028569879],"domain_scores_gemma":[0.9998481,0.000020223204,0.000027528667,0.000018689194,0.00006560578,0.00001981944],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00011018598,0.0005114765,0.00027863227,0.0011331274,0.00016487071,0.0003776721,0.00038211394,0.00032465492,0.00057209464],"category_scores_gemma":[0.00036579467,0.00018649017,0.0002995555,0.00044626815,0.00014762928,0.00053371367,0.00028026386,0.00028077714,0.00031557857],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00052632514,0.00018915054,0.036772776,0.00026610217,0.000101305064,0.0008743705,0.0004511398,0.03988329,0.2942308,0.0020570199,0.004041231,0.6206064],"study_design_scores_gemma":[0.000023356868,0.00024151983,0.048447873,0.000046622343,0.000123623,0.00066980993,0.0004620915,0.8295875,0.114394054,0.00062732823,0.0053388593,0.000037336416],"about_ca_topic_score_codex":0.0074753147,"about_ca_topic_score_gemma":0.010636889,"teacher_disagreement_score":0.0074753147,"about_ca_system_score_codex":0.00026881913,"about_ca_system_score_gemma":0.00034400207,"threshold_uncertainty_score":0.01486361},"labels":[],"label_agreement":null},{"id":"W3202919459","doi":"10.18280/ts.380437","title":"Integration Between Cascade Region-Based Convolutional Neural Network and Bi-Directional Feature Pyramid Network for Live Object Tracking and Detection","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Cascade; Artificial intelligence; Computer science; Pyramid (geometry); Convolutional neural network; Object detection; Pattern recognition (psychology); Feature (linguistics); Computer vision; Tracking (education); Feature extraction; Video tracking; Frame (networking); Object (grammar); Mathematics; Engineering; Telecommunications","score_opus":0.03435486891027862,"score_gpt":0.2724292273098361,"score_spread":0.23807435839955748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3202919459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.057235904,0.0010783314,0.93585235,0.00014813355,0.00012103899,0.00007605261,0.00016754896,0.002785651,0.002534912],"genre_scores_gemma":[0.74600273,0.0008845083,0.24790819,0.00020578786,0.000064144915,0.000082254126,0.0006330348,0.000094077914,0.004125334],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99956006,0.000040250638,0.000019488714,0.00016074633,0.00014920693,0.000070239075],"domain_scores_gemma":[0.9996712,0.00006123438,0.00003343593,0.000055470213,0.00015398214,0.000024605346],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006918204,0.0009364808,0.00059609837,0.0008479903,0.00026196495,0.0004405114,0.0012151616,0.0006804457,0.0010589976],"category_scores_gemma":[0.0010617119,0.00038401634,0.00068932574,0.000721404,0.00027763072,0.0014727925,0.00074650656,0.0006671072,0.00040327103],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000341351,0.00023268904,0.004935514,0.00016398377,0.00025357617,0.0002711441,0.00007870429,0.1610393,0.08965568,0.0032679222,0.004670761,0.7350895],"study_design_scores_gemma":[0.000006818756,0.00009554455,0.0015119131,0.000006221796,0.00005423397,0.00009388456,0.000008777033,0.9820955,0.014180109,0.0008014386,0.001130932,0.000014563142],"about_ca_topic_score_codex":0.013487995,"about_ca_topic_score_gemma":0.016040958,"teacher_disagreement_score":0.013487995,"about_ca_system_score_codex":0.00092906534,"about_ca_system_score_gemma":0.0008296738,"threshold_uncertainty_score":0.02681899},"labels":[],"label_agreement":null},{"id":"W3203515347","doi":"10.23977/jeis.2021.060204","title":"Multi-Person Detection of Drivers Based on Yolo Network","year":2021,"lang":"en","type":"article","venue":"Journal of Electronics and Information Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Task (project management); Artificial intelligence; Brightness; Real-time computing; Set (abstract data type); Grayscale; Computer vision; Engineering; Systems engineering; Image (mathematics)","score_opus":0.016196187890721008,"score_gpt":0.2682488720622197,"score_spread":0.2520526841714987,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203515347","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":[0.9811921,0.00017687632,0.015810188,0.00005635865,0.000043884294,0.000018559926,0.00035119103,0.00015643943,0.0021943727],"genre_scores_gemma":[0.9922638,0.00012364393,0.0050702686,0.000021376889,0.00001736494,0.000013675781,0.00047695378,0.000009257434,0.0020036995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99988925,0.000009038289,0.0000031200007,0.000036770678,0.000026995265,0.000034836954],"domain_scores_gemma":[0.99991107,0.000015300006,0.000018936242,0.000005135379,0.00003088126,0.000018652623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00014396812,0.00032291855,0.00030820462,0.00081928854,0.00017784754,0.0002563442,0.0002314451,0.00023228253,0.0010993037],"category_scores_gemma":[0.00030520078,0.00010036331,0.00013396691,0.0003363956,0.00007142146,0.0002589646,0.00029399665,0.00015471055,0.00027237838],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019648357,0.0005710159,0.3166042,0.0003664993,0.00021041185,0.0014612805,0.0012452665,0.03456149,0.2139184,0.0011801285,0.007539218,0.42037734],"study_design_scores_gemma":[0.00002242881,0.00032586686,0.42602277,0.000050372168,0.00010817441,0.0005591807,0.0006148217,0.5432053,0.02531449,0.00028444198,0.0034459506,0.000046240544],"about_ca_topic_score_codex":0.005835901,"about_ca_topic_score_gemma":0.014716572,"teacher_disagreement_score":0.005835901,"about_ca_system_score_codex":0.0001681266,"about_ca_system_score_gemma":0.00015625716,"threshold_uncertainty_score":0.011603832},"labels":[],"label_agreement":null},{"id":"W3203742755","doi":"10.3390/bdcc5040050","title":"Advances in Convolution Neural Networks Based Crowd Counting and Density Estimation","year":2021,"lang":"en","type":"article","venue":"Big Data and Cognitive Computing","topic":"Video Surveillance and Tracking Methods","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":"Centre de Géomatique du Québec; University of Calgary; Université de Moncton","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Computer science; Convolutional neural network; Artificial intelligence; Deep learning; Convolution (computer science); Scale (ratio); Density estimation; Face (sociological concept); Machine learning; Perspective (graphical); Artificial neural network; Computer vision; Pattern recognition (psychology); Geography; Statistics; Mathematics; Cartography","score_opus":0.06108384129666245,"score_gpt":0.32185007448230757,"score_spread":0.2607662331856451,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203742755","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020965878,0.0066777086,0.9628427,0.0006712637,0.00028877964,0.00005514242,0.00037764345,0.0012718961,0.0068490678],"genre_scores_gemma":[0.51738566,0.01578847,0.45041573,0.0006227463,0.0009937736,0.00022163535,0.0018583263,0.00036549818,0.01234813],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993318,0.00013323537,0.000053876433,0.00020842285,0.00019797524,0.00007468035],"domain_scores_gemma":[0.9987244,0.00054768566,0.0001787402,0.00011780198,0.00037882954,0.000052526477],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012978486,0.0016185028,0.0011883226,0.002375034,0.00044140161,0.001074424,0.0016173751,0.0011319047,0.0014634953],"category_scores_gemma":[0.005578661,0.0006584765,0.0009199126,0.0018296839,0.0006162428,0.0021740391,0.0014095354,0.0011399899,0.0006562862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013122044,0.00009289468,0.005996705,0.0003784066,0.00016317617,0.00019533896,0.00021115218,0.27172318,0.0045611025,0.023768796,0.008251657,0.6845264],"study_design_scores_gemma":[0.0000035294554,0.000016944326,0.0011720471,0.00006077628,0.000030373983,0.00008335836,0.000023303872,0.9841192,0.0024487854,0.0074801245,0.0045408183,0.000020846985],"about_ca_topic_score_codex":0.015021097,"about_ca_topic_score_gemma":0.008977971,"teacher_disagreement_score":0.015021097,"about_ca_system_score_codex":0.0012377129,"about_ca_system_score_gemma":0.0009537939,"threshold_uncertainty_score":0.029867291},"labels":[],"label_agreement":null},{"id":"W3203857058","doi":"10.1109/iccv48922.2021.00971","title":"High-Performance Discriminative Tracking with Transformers","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":145,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Research and Development; National Natural Science Foundation of China","keywords":"Discriminative model; Computer science; Artificial intelligence; Robustness (evolution); Video tracking; Minimum bounding box; Pattern recognition (psychology); Computer vision; BitTorrent tracker; Encoder; Transformer; Object detection; Eye tracking; Object (grammar); Engineering","score_opus":0.04434457477481431,"score_gpt":0.3145527255423552,"score_spread":0.2702081507675409,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3203857058","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":[0.005513601,0.00014315458,0.98197687,0.000037727736,0.000050622988,0.000041038697,0.00014716509,0.010553278,0.0015365351],"genre_scores_gemma":[0.4165952,0.00030792083,0.57104963,0.00023736074,0.000049100247,0.00012618811,0.0020368197,0.00092915597,0.008668681],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999355,0.00005427199,0.000027426779,0.00019955261,0.00028852554,0.00007515965],"domain_scores_gemma":[0.9993315,0.0001690314,0.000048156995,0.00023047582,0.0001660652,0.00005487845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000804402,0.0010284979,0.0008513794,0.00057936314,0.00033965358,0.0009661614,0.0018352721,0.0006585634,0.0055971765],"category_scores_gemma":[0.0026419016,0.0005221462,0.00039077035,0.0008918845,0.00040395223,0.0016127325,0.0014951427,0.00123746,0.004871625],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064533716,0.00017772558,0.0023277213,0.00020127236,0.00008406835,0.00021295343,0.00010293602,0.11817205,0.0626142,0.012130308,0.019603346,0.7837282],"study_design_scores_gemma":[0.000051667266,0.000107713924,0.0005348149,0.000012039889,0.000022527327,0.00026516343,0.00001914767,0.9545366,0.02965557,0.0060693533,0.008700511,0.000024978535],"about_ca_topic_score_codex":0.005180425,"about_ca_topic_score_gemma":0.008680781,"teacher_disagreement_score":0.0055971765,"about_ca_system_score_codex":0.0006540957,"about_ca_system_score_gemma":0.0013087615,"threshold_uncertainty_score":0.018724442},"labels":[],"label_agreement":null},{"id":"W3204085076","doi":"10.18280/ria.350409","title":"Classification of Rigid and Non-Rigid Objects Using CNN","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Computer science; Python (programming language); Computer vision; Rigid body; Rigid transformation; Pattern recognition (psychology); Set (abstract data type); Binary number; Mathematics; Physics","score_opus":0.08292672757183751,"score_gpt":0.331396817286512,"score_spread":0.2484700897146745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204085076","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":[0.38015723,0.0015823614,0.58743227,0.000528116,0.0005880832,0.0003664462,0.0042433594,0.007291751,0.017810322],"genre_scores_gemma":[0.82315105,0.00070138654,0.1588699,0.0002287972,0.000082627885,0.00010830861,0.0062477426,0.00023491961,0.010375286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964035,0.00002006799,0.000017908038,0.00012130059,0.000097810705,0.000102616046],"domain_scores_gemma":[0.99977607,0.000030526182,0.000031331136,0.00005217483,0.00008774272,0.00002212983],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036304403,0.0009972668,0.00045128955,0.0012603711,0.0002977611,0.000830762,0.0006038745,0.0005844028,0.002097514],"category_scores_gemma":[0.00090172596,0.0003252761,0.00078489515,0.000869413,0.000307675,0.0006390641,0.00054792094,0.0005845702,0.0010171821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006298842,0.00022182042,0.013799943,0.00020879974,0.00019656472,0.00044170165,0.00010183941,0.1244632,0.13760963,0.0037039276,0.013954587,0.70466805],"study_design_scores_gemma":[0.000007793248,0.000084408966,0.010980841,0.00004032093,0.000037130896,0.0001617307,0.000046717818,0.9369009,0.04556358,0.0019910668,0.0041606687,0.000024835224],"about_ca_topic_score_codex":0.014537444,"about_ca_topic_score_gemma":0.012456468,"teacher_disagreement_score":0.014537444,"about_ca_system_score_codex":0.0008708121,"about_ca_system_score_gemma":0.00048253435,"threshold_uncertainty_score":0.02890569},"labels":[],"label_agreement":null},{"id":"W3204653578","doi":"10.18280/ts.380432","title":"A Visual Tracking Algorithm Based on Estimation of Regression Probability Distribution","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Regression; Frame (networking); Computer science; Tracking (education); Artificial intelligence; Regression analysis; Algorithm; Pattern recognition (psychology); Interference (communication); Probability distribution; Joint probability distribution; Eye tracking; Machine learning; Mathematics; Statistics","score_opus":0.02903526542098471,"score_gpt":0.309085004368017,"score_spread":0.2800497389470323,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3204653578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0030907516,0.00012131829,0.9954516,0.000026348678,0.000020155814,0.00002260871,0.000020596364,0.0007784277,0.000468057],"genre_scores_gemma":[0.1977563,0.00046144257,0.7967014,0.00013736256,0.000071156544,0.00012123081,0.00042246678,0.00023047884,0.0040982594],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925655,0.00007298009,0.00003652575,0.00036695806,0.00020748936,0.000059385042],"domain_scores_gemma":[0.9993376,0.00020858312,0.00008772499,0.000097711454,0.00023835711,0.00003005653],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009972783,0.0008166342,0.0010722092,0.0013840662,0.0004189081,0.0008761086,0.0013514657,0.00084779557,0.0015974635],"category_scores_gemma":[0.0024569784,0.00041897717,0.00078360265,0.0010012916,0.0004570026,0.0011736824,0.000991027,0.0012337321,0.000826703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016487435,0.00007543022,0.0016677766,0.00008645016,0.00007783554,0.00007532087,0.00007325366,0.142601,0.030211449,0.00616459,0.0025149905,0.81628704],"study_design_scores_gemma":[0.000016208343,0.000045173794,0.0005359283,0.000008529508,0.000014695872,0.00012413066,0.000007721011,0.98784,0.008149046,0.0015289576,0.0017147358,0.00001491654],"about_ca_topic_score_codex":0.006072009,"about_ca_topic_score_gemma":0.0047620195,"teacher_disagreement_score":0.006072009,"about_ca_system_score_codex":0.0006324587,"about_ca_system_score_gemma":0.0009597354,"threshold_uncertainty_score":0.012073338},"labels":[],"label_agreement":null},{"id":"W3205447837","doi":"10.1108/ijius-07-2021-0061","title":"EGMM video surveillance for monitoring urban traffic scenario","year":2021,"lang":"en","type":"article","venue":"International Journal of Intelligent Unmanned Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lab_Bell (Canada)","funders":"","keywords":"Background subtraction; Mixture model; Computer science; Gaussian; Gaussian network model; Artificial intelligence; Computer vision; Real-time computing; Simulation; Pixel","score_opus":0.03900943426745274,"score_gpt":0.3236402122576833,"score_spread":0.28463077799023057,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3205447837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.37169752,0.0020693794,0.61045945,0.00030852412,0.00018265299,0.00021611189,0.0017588474,0.0031839134,0.010123601],"genre_scores_gemma":[0.8870832,0.0006721335,0.10853199,0.000082405604,0.00005682617,0.000059014128,0.0011581772,0.00003892855,0.0023174668],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997185,0.000057004512,0.000010312033,0.00007932367,0.000102085534,0.00003266782],"domain_scores_gemma":[0.99980515,0.000028858683,0.000034316745,0.00002007976,0.00009445781,0.000017124263],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003681447,0.00051182276,0.00029856584,0.0011888292,0.00019350134,0.0004063278,0.00043062193,0.00038785217,0.00072563713],"category_scores_gemma":[0.0006320176,0.00010997625,0.00024828597,0.0005827008,0.00012296655,0.00041475298,0.00031299933,0.00031094917,0.00037174195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005703005,0.00024876,0.038843866,0.0004872896,0.00013871498,0.00051833485,0.0002881179,0.063897125,0.1314876,0.003206052,0.011431394,0.7488825],"study_design_scores_gemma":[0.000022773507,0.0003527373,0.053531148,0.00007895825,0.00009348649,0.00079562166,0.00023137775,0.87701756,0.056296397,0.0015608297,0.009970594,0.000048630576],"about_ca_topic_score_codex":0.005536653,"about_ca_topic_score_gemma":0.0060077375,"teacher_disagreement_score":0.005536653,"about_ca_system_score_codex":0.00036600934,"about_ca_system_score_gemma":0.00028748217,"threshold_uncertainty_score":0.011008859},"labels":[],"label_agreement":null},{"id":"W3207740618","doi":"10.1109/crv60082.2023.00031","title":"Multi-Object Tracking and Segmentation with a Space-Time Memory Network","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Segmentation; Computer vision; Video tracking; Association (psychology); Metric (unit); Object (grammar); Tracking (education); Object detection; Optical flow; Pattern recognition (psychology); Image (mathematics)","score_opus":0.052888574562659905,"score_gpt":0.3089768751565303,"score_spread":0.2560883005938704,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3207740618","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":[0.010451754,0.00013074838,0.98798233,0.000058961752,0.000029562763,0.00002284784,0.00003093775,0.0006359604,0.00065689423],"genre_scores_gemma":[0.32634097,0.00021385927,0.667758,0.0001396841,0.00009652078,0.00015871465,0.00023311218,0.00015044841,0.004908749],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992768,0.00010714643,0.00003486732,0.0003064369,0.00019213292,0.00008260611],"domain_scores_gemma":[0.99920964,0.00020817113,0.000137458,0.00025550066,0.00012751279,0.00006167609],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008197008,0.0006315109,0.0007820544,0.0010291591,0.00056876394,0.0012054398,0.0018748634,0.0012623082,0.0013231075],"category_scores_gemma":[0.0019099413,0.0004506417,0.00054332544,0.0011378527,0.0006253118,0.0022191901,0.0016621083,0.0010715363,0.0006212377],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006420351,0.00018183135,0.0024775732,0.0001174052,0.00012988556,0.00023111567,0.00031449567,0.20659845,0.054055445,0.025780873,0.0029866365,0.7064843],"study_design_scores_gemma":[0.000015528596,0.00009500596,0.0005591335,0.000008287559,0.000019700914,0.00012624245,0.000018753173,0.97345656,0.016984118,0.005515295,0.0031839695,0.000017365877],"about_ca_topic_score_codex":0.0029529848,"about_ca_topic_score_gemma":0.0030202514,"teacher_disagreement_score":0.0029529848,"about_ca_system_score_codex":0.0009108719,"about_ca_system_score_gemma":0.0007876536,"threshold_uncertainty_score":0.0066089034},"labels":[],"label_agreement":null},{"id":"W3208050295","doi":"10.48550/arxiv.2111.01606","title":"PolyTrack: Tracking with Bounding Polygons","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Minimum bounding box; Computer science; Bounding overwatch; Polygon (computer graphics); Computer vision; Artificial intelligence; Segmentation; Tracking (education); Offset (computer science); Frame (networking); Kalman filter; Video tracking; Object (grammar); Image (mathematics)","score_opus":0.1037332671745549,"score_gpt":0.21515355083888066,"score_spread":0.11142028366432576,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3208050295","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0027856876,0.00020615189,0.9890844,0.00004946711,0.000069443326,0.000064116495,0.00035992623,0.0064447545,0.00093590864],"genre_scores_gemma":[0.08161469,0.00042282973,0.90937746,0.00016158044,0.00008032316,0.00030552928,0.0030918943,0.0019410764,0.0030046008],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980525,0.0002344798,0.00009547707,0.0007841983,0.00068114663,0.00015230851],"domain_scores_gemma":[0.99776185,0.0007353571,0.00027292056,0.0007434884,0.00036000248,0.00012629956],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011663178,0.0017600257,0.0016157844,0.0024722903,0.0009114844,0.0029947076,0.003356505,0.0014623377,0.004304208],"category_scores_gemma":[0.005158238,0.0013646919,0.0015191986,0.0025875682,0.0011057415,0.0027817634,0.003498212,0.0017132522,0.0033986],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005420494,0.00013612169,0.003883994,0.00041004,0.00018794059,0.00031591405,0.0004202956,0.18837258,0.021715276,0.01882344,0.023527214,0.74166507],"study_design_scores_gemma":[0.000031705462,0.000046098805,0.0005493484,0.000045400386,0.000023747516,0.00019002895,0.000042490734,0.9622453,0.0143208075,0.00667862,0.015788745,0.00003770607],"about_ca_topic_score_codex":0.009205351,"about_ca_topic_score_gemma":0.007618361,"teacher_disagreement_score":0.009205351,"about_ca_system_score_codex":0.0009127011,"about_ca_system_score_gemma":0.0012695151,"threshold_uncertainty_score":0.018303514},"labels":[],"label_agreement":null},{"id":"W3209993199","doi":"10.1109/iccvw54120.2021.00305","title":"The Ninth Visual Object Tracking VOT2021 Challenge Results","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Engineering and Physical Sciences Research Council","keywords":"Computer science; Ninth; Computer vision; Artificial intelligence; Object (grammar); Video tracking; Eye tracking","score_opus":0.038224399817515915,"score_gpt":0.3268587849193556,"score_spread":0.2886343851018397,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3209993199","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.076127656,0.02299982,0.2022729,0.010985914,0.039086327,0.007458095,0.4449427,0.086476296,0.10965031],"genre_scores_gemma":[0.035341095,0.0010448159,0.056539986,0.001580284,0.00094827125,0.0019291894,0.8522129,0.003102251,0.047301244],"study_design_codex":"not_applicable","study_design_gemma":"observational","domain_scores_codex":[0.9844493,0.0030149522,0.00093053764,0.003640981,0.006086602,0.0018775322],"domain_scores_gemma":[0.98679185,0.001751756,0.00030498925,0.0031644725,0.006124891,0.0018619775],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.013466921,0.0062099053,0.0051061343,0.0026964417,0.003727537,0.0060085463,0.0055151815,0.005908338,0.01743884],"category_scores_gemma":[0.017698193,0.0010211758,0.0032870814,0.0029148464,0.0010910716,0.004593207,0.0074944613,0.0044937697,0.025656983],"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":[0.00079237856,0.00052261044,0.0010923307,0.0008328877,0.00020808635,0.00019791762,0.00009143404,0.0048052794,0.0035838524,0.0013904336,0.87885505,0.107627794],"study_design_scores_gemma":[0.00096904085,0.0019139847,0.016427824,0.0007833459,0.00033159877,0.0013991365,0.0005797106,0.09676985,0.02864443,0.011614602,0.8402686,0.00029794875],"about_ca_topic_score_codex":0.037201818,"about_ca_topic_score_gemma":0.050439175,"teacher_disagreement_score":0.037201818,"about_ca_system_score_codex":0.00413847,"about_ca_system_score_gemma":0.004536573,"threshold_uncertainty_score":0.073970556},"labels":[],"label_agreement":null},{"id":"W3210339021","doi":"10.1109/ccece53047.2021.9569159","title":"GroupNet: Detecting the Social Distancing Violation using Object Tracking in Crowdscene","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Social distance; Computer science; Artificial intelligence; Object (grammar); Computer vision; Object detection; Euclidean distance; Distance matrix; False positive paradox; Group (periodic table); Pattern recognition (psychology); Algorithm","score_opus":0.05370404446308074,"score_gpt":0.34463089689267534,"score_spread":0.2909268524295946,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210339021","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35527885,0.0027199453,0.55375004,0.0010100229,0.0010829162,0.0017786917,0.012245492,0.042552974,0.02958111],"genre_scores_gemma":[0.70879084,0.0003988775,0.26303026,0.00044196242,0.00012517051,0.00053957786,0.015525917,0.0007319118,0.010415584],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985677,0.00017032956,0.00005376336,0.00059135724,0.0004354191,0.00018127821],"domain_scores_gemma":[0.9992853,0.0001543316,0.000100694015,0.00017824804,0.00017602324,0.00010544131],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010817602,0.0015379391,0.0014295514,0.0027248855,0.0009864471,0.0013549608,0.0022387635,0.0013985396,0.0021813647],"category_scores_gemma":[0.002502469,0.00045524127,0.00066801504,0.001349424,0.0005005794,0.0012388787,0.0032064756,0.0010345348,0.0018286016],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013014042,0.0010295341,0.060519706,0.0008052208,0.00050024386,0.0024821146,0.0017196583,0.0740775,0.035682015,0.0059071523,0.07595278,0.74002266],"study_design_scores_gemma":[0.00009258978,0.00022743906,0.019202584,0.000115443,0.000062830906,0.0005643969,0.0010890235,0.92733127,0.012533437,0.010554418,0.028141808,0.00008479694],"about_ca_topic_score_codex":0.023591455,"about_ca_topic_score_gemma":0.03763927,"teacher_disagreement_score":0.023591455,"about_ca_system_score_codex":0.0010311184,"about_ca_system_score_gemma":0.0012129627,"threshold_uncertainty_score":0.04690826},"labels":[],"label_agreement":null},{"id":"W3210921698","doi":"10.1007/s42979-021-00932-x","title":"A Systematic Survey on Human Behavior Recognition Methods","year":2021,"lang":"en","type":"article","venue":"SN Computer Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Abbotsford Veterinary Clinic","funders":"Natural Science Foundation of Shandong Province","keywords":"Computer science; Reinforcement learning; Artificial intelligence; Deep learning; Human–computer interaction; Human behavior; Variety (cybernetics); RGB color model; Machine learning","score_opus":0.15871718194035328,"score_gpt":0.4266109894935579,"score_spread":0.26789380755320463,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3210921698","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":[0.016057309,0.6951311,0.27095363,0.0016343013,0.0012496401,0.0006659795,0.0023347924,0.0013776871,0.0105955405],"genre_scores_gemma":[0.10516412,0.6266287,0.24369098,0.002123814,0.0019301013,0.0011041671,0.008051144,0.00048710816,0.01081996],"study_design_codex":"design_other","study_design_gemma":"systematic_review","domain_scores_codex":[0.9934469,0.0017344509,0.0009495654,0.0017343417,0.0019575318,0.00017720339],"domain_scores_gemma":[0.97515374,0.017506877,0.0010984925,0.0017135651,0.004327127,0.00020017974],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064453566,0.0017347224,0.0023613237,0.008487895,0.0006326151,0.002328849,0.0021887084,0.0013245959,0.0035226436],"category_scores_gemma":[0.021682454,0.0007731347,0.0016577787,0.006009862,0.0007422097,0.0038972283,0.0011069039,0.0011024866,0.0023009935],"study_design_candidate":"systematic_review","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010223874,0.00011156242,0.0055106054,0.007263865,0.00020409086,0.000029587974,0.00008850025,0.00084682094,0.0015344991,0.0017827539,0.007987566,0.9745378],"study_design_scores_gemma":[0.0001634142,0.0024046542,0.0908734,0.030822445,0.003878536,0.004653622,0.0023646024,0.10231946,0.042341467,0.028780984,0.69071704,0.00068033097],"about_ca_topic_score_codex":0.004187261,"about_ca_topic_score_gemma":0.005241347,"teacher_disagreement_score":0.008487895,"about_ca_system_score_codex":0.00068836985,"about_ca_system_score_gemma":0.0025641469,"threshold_uncertainty_score":0.034086764},"labels":[],"label_agreement":null},{"id":"W3211571646","doi":"10.1155/2021/3515512","title":"Traffic Foreground Detection at Complex Urban Intersections Using a Novel Background Dictionary Learning Model","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation of Hunan Province; Education Department of Hunan Province","keywords":"Computer science; Artificial intelligence; Dictionary learning; Computer vision; Natural language processing; Pattern recognition (psychology); Sparse approximation","score_opus":0.05919482883379214,"score_gpt":0.3122454324832336,"score_spread":0.2530506036494415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3211571646","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.044212673,0.00012266489,0.95425093,0.000068176414,0.00002598546,0.000026939144,0.000047308535,0.00041315827,0.00083223527],"genre_scores_gemma":[0.60053134,0.00054678315,0.39548838,0.00015476851,0.00007706702,0.00007573131,0.0005407552,0.000104785155,0.0024804361],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996487,0.000053278596,0.000014481374,0.000093425995,0.00012905383,0.000060978397],"domain_scores_gemma":[0.9996406,0.00010887481,0.00004684514,0.000043649317,0.00013203385,0.000028011005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045179832,0.00055572356,0.00066814304,0.0009111302,0.00028528992,0.00069115474,0.00081831636,0.00059694325,0.00050139],"category_scores_gemma":[0.001198295,0.00028787903,0.00052554475,0.0008240914,0.00037069744,0.0010730145,0.0007593922,0.00072337734,0.0003296844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00041561478,0.00020557475,0.0057574157,0.00016732568,0.000111873574,0.00022485551,0.00024619358,0.31968713,0.07217011,0.008468964,0.0026873995,0.58985746],"study_design_scores_gemma":[0.0000065362133,0.00002881809,0.0005944934,0.0000034347709,0.000012038835,0.000058694233,0.000013577489,0.99260485,0.0056705815,0.00050592184,0.0004936186,0.0000074777495],"about_ca_topic_score_codex":0.004082971,"about_ca_topic_score_gemma":0.0035674165,"teacher_disagreement_score":0.004082971,"about_ca_system_score_codex":0.00040307632,"about_ca_system_score_gemma":0.00058730564,"threshold_uncertainty_score":0.008118451},"labels":[],"label_agreement":null},{"id":"W3214009070","doi":"10.1109/iotdi54339.2022.00010","title":"DeepScale: Online Frame Size Adaptation for Multi-object Tracking on Smart Cameras and Edge Servers","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","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":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Server; Artificial intelligence; Video tracking; Edge computing; Convolutional neural network; Computation; Frame (networking); BitTorrent tracker; Real-time computing; Enhanced Data Rates for GSM Evolution; Testbed; Computer vision; Benchmark (surveying); Frame rate; Eye tracking; Video processing; Computer network; Algorithm","score_opus":0.11461599508796252,"score_gpt":0.36087453413249637,"score_spread":0.24625853904453385,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3214009070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.053856473,0.00096173404,0.8763619,0.0002189531,0.00027519028,0.00018741611,0.0012508949,0.062824786,0.004062589],"genre_scores_gemma":[0.50567216,0.0004644042,0.48087928,0.00048373317,0.000088287125,0.00024185782,0.0044990503,0.001651859,0.006019249],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995945,0.000028598735,0.000015773958,0.00017679605,0.00013158053,0.000052840955],"domain_scores_gemma":[0.99959093,0.000092864546,0.000039034225,0.00014302673,0.0000876081,0.000046527377],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007579615,0.0012264883,0.00078118074,0.00063654385,0.0003879557,0.0008731306,0.0022210693,0.000781715,0.0036923757],"category_scores_gemma":[0.0026708967,0.000515997,0.00043503882,0.0007080393,0.00031805836,0.0016021294,0.0015859567,0.0013199949,0.0017134144],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006808418,0.0002849332,0.004008804,0.00016328027,0.00015300106,0.0001832355,0.00013295245,0.13233663,0.03849417,0.0043087937,0.042407155,0.7768462],"study_design_scores_gemma":[0.000047518057,0.000068174246,0.0010663875,0.000012294001,0.000015176245,0.00007521471,0.000013213219,0.9812479,0.0112488065,0.0022557369,0.0039309184,0.000018599396],"about_ca_topic_score_codex":0.011872119,"about_ca_topic_score_gemma":0.019160893,"teacher_disagreement_score":0.011872119,"about_ca_system_score_codex":0.00095538684,"about_ca_system_score_gemma":0.0013434439,"threshold_uncertainty_score":0.023606002},"labels":[],"label_agreement":null},{"id":"W3215163807","doi":"10.24963/ijcai.2022/180","title":"SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BC Research (Canada)","funders":"Nanyang Technological University","keywords":"Skeleton (computer programming); Computer science; Artificial intelligence; Discriminative model; Sequence (biology); Feature learning; Pattern recognition (psychology); Consistency (knowledge bases); Similarity (geometry); Representation (politics); Identification (biology); Deep learning; Natural language processing; Image (mathematics)","score_opus":0.10771857738968935,"score_gpt":0.3326002944640817,"score_spread":0.22488171707439233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215163807","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":[0.017770924,0.0003865549,0.97654516,0.00010076476,0.00008088464,0.00009155279,0.00021598653,0.0034327079,0.001375511],"genre_scores_gemma":[0.29066238,0.00042886776,0.69608366,0.0004035359,0.00012333851,0.0002317503,0.0022570547,0.0006401162,0.009169308],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991373,0.00013467085,0.000025837993,0.00039061767,0.0002184319,0.00009316801],"domain_scores_gemma":[0.99908245,0.00021983888,0.00008708086,0.0003482849,0.0001895585,0.00007271702],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010589482,0.0011792015,0.0010944918,0.0010648977,0.0004142409,0.0006230013,0.0027469716,0.0012983641,0.0036457516],"category_scores_gemma":[0.0028319077,0.0005742167,0.0010740729,0.00084000843,0.00086877076,0.0014529333,0.001957057,0.0017498003,0.002636478],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037651265,0.00026968776,0.0023398122,0.00013011706,0.00015162646,0.00015723339,0.00013420734,0.12659997,0.027145261,0.005812268,0.009791343,0.8270919],"study_design_scores_gemma":[0.00001489617,0.000112358524,0.00073003245,0.000013168309,0.000019009767,0.00016342675,0.000015945861,0.98323935,0.008895691,0.003983922,0.002793629,0.00001856914],"about_ca_topic_score_codex":0.0044864123,"about_ca_topic_score_gemma":0.008806055,"teacher_disagreement_score":0.0044864123,"about_ca_system_score_codex":0.00067157636,"about_ca_system_score_gemma":0.001007833,"threshold_uncertainty_score":0.012196243},"labels":[],"label_agreement":null},{"id":"W3215494300","doi":"10.3390/technologies9040093","title":"Critical Overview of Visual Tracking with Kernel Correlation Filter","year":2021,"lang":"en","type":"article","venue":"Technologies","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Eye tracking; Circulant matrix; Kernel (algebra); Filter (signal processing); Artificial intelligence; Representation (politics); Algorithm; Computer vision; Mathematics","score_opus":0.06279360045868777,"score_gpt":0.357297924479525,"score_spread":0.29450432402083726,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3215494300","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0016874634,0.011340894,0.9780552,0.0007419538,0.00037009487,0.00006230208,0.00019881282,0.0025510974,0.004992311],"genre_scores_gemma":[0.11023721,0.03239788,0.835746,0.001296177,0.001639958,0.0003620324,0.0025783188,0.0013686742,0.0143737905],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9979627,0.00036004354,0.00014898044,0.0005031395,0.000876585,0.00014857351],"domain_scores_gemma":[0.99755645,0.00083251705,0.00013762008,0.0004477897,0.0009179202,0.00010773489],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0028189605,0.0011490816,0.0011742573,0.002689787,0.00075926346,0.0031487949,0.0020937417,0.0020154514,0.0043622265],"category_scores_gemma":[0.010513271,0.00085629424,0.0011011354,0.0038619307,0.00087159773,0.0038326562,0.0015539245,0.002820214,0.003299785],"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":[0.00018949696,0.0000834269,0.0015848994,0.00062402966,0.00014950924,0.00011318187,0.00015039044,0.06759973,0.005769694,0.06266928,0.030121014,0.8309454],"study_design_scores_gemma":[0.000037791076,0.00020204499,0.0017951258,0.00033224226,0.00007487794,0.00043334352,0.00006828978,0.779801,0.013125595,0.062209286,0.14178105,0.00013942161],"about_ca_topic_score_codex":0.015526881,"about_ca_topic_score_gemma":0.008020829,"teacher_disagreement_score":0.015526881,"about_ca_system_score_codex":0.0021325366,"about_ca_system_score_gemma":0.0025896034,"threshold_uncertainty_score":0.030873},"labels":[],"label_agreement":null},{"id":"W3217800669","doi":"10.18280/ts.380515","title":"Challenges and Limitations in Human Action Recognition on Unmanned Aerial Vehicles: A Comprehensive Survey","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Drone; Action (physics); Benchmark (surveying); Computer science; Artificial intelligence; Action recognition; Human–computer interaction; Computer security; Geography; Cartography","score_opus":0.3238141404210039,"score_gpt":0.3512683337557135,"score_spread":0.027454193334709553,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W3217800669","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":[0.07090087,0.75298464,0.14148606,0.004954151,0.0011282086,0.00028991973,0.0016260031,0.0008388577,0.025791282],"genre_scores_gemma":[0.37916297,0.5439807,0.061080102,0.0017816182,0.00208563,0.00032097902,0.0051210313,0.00018087507,0.0062860497],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9950358,0.0011042538,0.00048345173,0.001236195,0.0019493273,0.00019090141],"domain_scores_gemma":[0.98523104,0.010152887,0.0007613386,0.0009723656,0.0026546633,0.00022769369],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004190054,0.0009781721,0.0013955183,0.0036757472,0.0005819657,0.0025600255,0.0023646476,0.0013092179,0.0013474071],"category_scores_gemma":[0.011904444,0.00055100146,0.000981063,0.003941876,0.0009915356,0.0045711077,0.0011467275,0.0009776927,0.0010696413],"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":[0.00006755387,0.00009691422,0.009484254,0.0040830867,0.00011010364,0.00009787368,0.00028378709,0.006788502,0.0016735051,0.003516628,0.010183267,0.9636146],"study_design_scores_gemma":[0.000028913426,0.00094855606,0.10740966,0.008388171,0.00057712256,0.0035458761,0.0073594637,0.19184174,0.021322034,0.03137587,0.62680644,0.00039624597],"about_ca_topic_score_codex":0.007055877,"about_ca_topic_score_gemma":0.004661557,"teacher_disagreement_score":0.007055877,"about_ca_system_score_codex":0.00080745324,"about_ca_system_score_gemma":0.0011297028,"threshold_uncertainty_score":0.022159398},"labels":[],"label_agreement":null},{"id":"W350359974","doi":"10.1007/978-3-319-07488-7_24","title":"Cooperative Targeting: Detection and Tracking of Small Objects with a Dual Camera System","year":2014,"lang":"en","type":"book-chapter","venue":"Springer tracts in advanced robotics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Dual (grammatical number); Computer vision; Tracking (education); Artificial intelligence; Computer science; Tracking system; Computer graphics (images); Psychology; Art","score_opus":0.019171206691102855,"score_gpt":0.24065819697439267,"score_spread":0.2214869902832898,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W350359974","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019726533,0.00092670904,0.97500956,0.00007386504,0.00009026887,0.000034114793,0.000035777033,0.0005936067,0.0035095569],"genre_scores_gemma":[0.42007565,0.0017412056,0.5595315,0.00018116909,0.00016677716,0.00016572599,0.0002569066,0.00016553233,0.017715482],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995425,0.000054156055,0.000013049742,0.00016759038,0.00017242761,0.0000502985],"domain_scores_gemma":[0.999705,0.000095515934,0.000035479898,0.000058622507,0.0000713243,0.000033986747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003705692,0.00081132224,0.0008215671,0.0007472026,0.00027465518,0.0011178363,0.0011909202,0.0014000776,0.0017541602],"category_scores_gemma":[0.0007942523,0.00065555016,0.00041929283,0.0008589444,0.0004912377,0.0010344958,0.0015832493,0.00081281725,0.0012116168],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064461783,0.00018412308,0.0011469182,0.00027720872,0.00009239106,0.00028929868,0.00029687647,0.04807134,0.30411518,0.008543526,0.0057312944,0.63060725],"study_design_scores_gemma":[0.000046629022,0.00046630602,0.002422166,0.00003408629,0.000078954494,0.0012495752,0.000068934125,0.895692,0.07833068,0.0073821964,0.014169114,0.000059378028],"about_ca_topic_score_codex":0.0010729587,"about_ca_topic_score_gemma":0.0012029295,"teacher_disagreement_score":0.0017541602,"about_ca_system_score_codex":0.00033638757,"about_ca_system_score_gemma":0.0003997754,"threshold_uncertainty_score":0.005868256},"labels":[],"label_agreement":null},{"id":"W4200234902","doi":"10.1155/2021/5038832","title":"Online Discrete Anchor Graph Hashing for Mobile Person Re-Identification","year":2021,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Natural Science Foundation of Hubei Province; Foundation of Equipment Pre-research Area; National Natural Science Foundation of China","keywords":"Computer science; Hash function; Mobile device; Identification (biology); Graph; Mobile computing; Theoretical computer science; Computer network; Computer security; World Wide Web","score_opus":0.02966542760407528,"score_gpt":0.32873131461269717,"score_spread":0.2990658870086219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200234902","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028345348,0.0002687582,0.96902925,0.00009972021,0.00009211826,0.000053435513,0.00011204159,0.0011140316,0.00088534114],"genre_scores_gemma":[0.7236056,0.00032464002,0.27175885,0.00014436497,0.0001079175,0.00008924783,0.00067949964,0.0001145272,0.0031754463],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993406,0.00016604486,0.000029989962,0.00020700556,0.00018617614,0.00007005863],"domain_scores_gemma":[0.99897623,0.00031078255,0.0001321622,0.0003449172,0.00016844406,0.00006755563],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047630156,0.00056376134,0.0008961837,0.0006802716,0.0003447232,0.00045314708,0.0011681909,0.0005829586,0.002072117],"category_scores_gemma":[0.0028443146,0.0002327847,0.00043523416,0.0009535213,0.0005624592,0.001645433,0.001218928,0.0007588595,0.0008649885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00059706107,0.00020379179,0.0028638795,0.00022728415,0.00009551409,0.0002206185,0.00020788613,0.23191178,0.022716172,0.022714218,0.009463195,0.70877856],"study_design_scores_gemma":[0.000022183312,0.00009350744,0.0006094852,0.000004570108,0.000013714699,0.00014676663,0.000043880886,0.9836398,0.004776847,0.009024181,0.0016032977,0.0000217743],"about_ca_topic_score_codex":0.0020395226,"about_ca_topic_score_gemma":0.0017480662,"teacher_disagreement_score":0.002072117,"about_ca_system_score_codex":0.00042139692,"about_ca_system_score_gemma":0.0005595208,"threshold_uncertainty_score":0.0069319606},"labels":[],"label_agreement":null},{"id":"W4200590247","doi":"10.3390/app12010211","title":"Attitudes toward Applying Facial Recognition Technology for Red-Light Running by E-Bikers: A Case Study in Fuzhou, China","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"License; China; Test (biology); Psychology; Applied psychology; Political science","score_opus":0.0683850425111635,"score_gpt":0.34631027111209667,"score_spread":0.27792522860093316,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200590247","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":[0.9996213,0.000020580597,0.00005312949,0.000076843484,0.0000012364466,0.000010089211,0.0000027952995,5.4821066e-7,0.0002135932],"genre_scores_gemma":[0.9992091,0.00010771308,0.0001314642,0.00006538745,0.0000027526467,0.000011578955,0.00000730169,8.7756626e-7,0.00046389314],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9991543,0.00026337666,0.0000444121,0.00009963414,0.00018851494,0.00024971124],"domain_scores_gemma":[0.99914324,0.00025848683,0.00021528495,0.000049993538,0.0001376194,0.0001954025],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013095102,0.00045171328,0.0002979204,0.00085920555,0.0031730265,0.0008645584,0.00063618453,0.0010930315,0.001380475],"category_scores_gemma":[0.0019340382,0.00032720444,0.00037456467,0.0007282846,0.0010507316,0.0007926041,0.00069014315,0.00074591633,0.00011656235],"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":[0.00014448057,0.0021588078,0.7392787,0.00020434416,0.0000754196,0.029506056,0.18103582,0.00051922386,0.0074201487,0.0011313432,0.00097642804,0.037549205],"study_design_scores_gemma":[0.000022320639,0.0010161321,0.7384307,0.000097442346,0.00011573398,0.008182159,0.2421478,0.004087213,0.0024148629,0.0002544623,0.0031443262,0.00008684887],"about_ca_topic_score_codex":0.06556911,"about_ca_topic_score_gemma":0.100090794,"teacher_disagreement_score":0.06556911,"about_ca_system_score_codex":0.0027079994,"about_ca_system_score_gemma":0.001769486,"threshold_uncertainty_score":0.13037491},"labels":[],"label_agreement":null},{"id":"W4200602317","doi":"10.1016/j.neucom.2021.12.027","title":"Towards more effective PRM-based crowd counting via a multi-resolution fusion and attention network","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture","keywords":"Computer science; Benchmark (surveying); Margin (machine learning); Artificial intelligence; Feature (linguistics); Task (project management); Pattern recognition (psychology); Machine learning; Data mining","score_opus":0.018100697087170745,"score_gpt":0.28713232116300114,"score_spread":0.26903162407583037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4200602317","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014376764,0.00043908687,0.9822099,0.00015283187,0.00011668802,0.000063732645,0.00008310196,0.00093622116,0.0016217076],"genre_scores_gemma":[0.40589684,0.00065391307,0.5854835,0.00047687133,0.0003752971,0.00016266,0.00050599396,0.00023456429,0.0062103076],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9986052,0.0002731077,0.000063670595,0.0004805827,0.00037129116,0.00020614485],"domain_scores_gemma":[0.9987733,0.00037075792,0.00013022865,0.0001628596,0.00047531727,0.00008753894],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001597857,0.0017794118,0.0021835526,0.002237954,0.0007954611,0.0014139856,0.0023127748,0.0018014584,0.0019612536],"category_scores_gemma":[0.003864733,0.00088370783,0.0011071486,0.0017328885,0.00058401586,0.002579028,0.003304308,0.0015242007,0.0015263299],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00054914976,0.0004273179,0.0022892398,0.00021217513,0.0002254779,0.00024825573,0.00021339941,0.12914564,0.053182956,0.0051661334,0.0057228673,0.8026173],"study_design_scores_gemma":[0.000006454075,0.000040184885,0.00058146374,0.000009089326,0.000027638456,0.00006859509,0.000023931547,0.9923024,0.00459162,0.0017207619,0.00061473023,0.000013154703],"about_ca_topic_score_codex":0.006047399,"about_ca_topic_score_gemma":0.0058341194,"teacher_disagreement_score":0.006047399,"about_ca_system_score_codex":0.00061837886,"about_ca_system_score_gemma":0.0010309726,"threshold_uncertainty_score":0.012024403},"labels":[],"label_agreement":null},{"id":"W4205113522","doi":"10.1007/s00521-021-06794-x","title":"Accelerated duality-aware correlation filters for visual tracking","year":2022,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"BitTorrent tracker; Computer science; Tracking (education); Regularization (linguistics); Frame (networking); Artificial intelligence; Eye tracking; Filter (signal processing); Correlation; Computer vision; Mathematics","score_opus":0.058722925585429624,"score_gpt":0.361824480110995,"score_spread":0.30310155452556536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205113522","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":[0.0043711704,0.00016623686,0.99428964,0.000055863056,0.000049736736,0.000011545972,0.00003537052,0.00023657165,0.0007839529],"genre_scores_gemma":[0.23132248,0.0005644485,0.7598909,0.00021834893,0.00016128302,0.00012026652,0.000373494,0.00024005718,0.0071086967],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99921227,0.00017825217,0.000030054132,0.00014335784,0.000327122,0.00010900048],"domain_scores_gemma":[0.9987067,0.0004948977,0.00011269353,0.00021688837,0.00038620716,0.000082501516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001010867,0.00069594744,0.0010886099,0.0007295485,0.00041807748,0.0012084639,0.001167935,0.0011146921,0.003450789],"category_scores_gemma":[0.0042917253,0.0005485265,0.0007157046,0.0013092109,0.00051422504,0.0013806524,0.001790219,0.002024569,0.0014485366],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008568081,0.00031001316,0.0012038705,0.00017314364,0.00012497844,0.00011072805,0.00012363058,0.25447857,0.046854172,0.07013448,0.009892688,0.61573696],"study_design_scores_gemma":[0.000014151171,0.00004276268,0.00018168098,0.000008449607,0.000010241386,0.00005109751,0.0000067860256,0.9872279,0.0044820975,0.006390681,0.001573939,0.000010198638],"about_ca_topic_score_codex":0.0037407135,"about_ca_topic_score_gemma":0.0056706015,"teacher_disagreement_score":0.0037407135,"about_ca_system_score_codex":0.0007078643,"about_ca_system_score_gemma":0.0019675817,"threshold_uncertainty_score":0.011543989},"labels":[],"label_agreement":null},{"id":"W4205240360","doi":"10.1109/tits.2021.3129506","title":"A Robust Environment-Aware Driver Profiling Framework Using Ensemble Supervised Learning","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; United Arab Emirates University","keywords":"Profiling (computer programming); Computer science; Ensemble learning; Artificial intelligence; Machine learning","score_opus":0.07293845911837098,"score_gpt":0.28399595170861713,"score_spread":0.21105749259024614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205240360","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06242639,0.00026477157,0.93203217,0.00016625316,0.000059479175,0.000084635205,0.00027972815,0.0035388768,0.0011477426],"genre_scores_gemma":[0.83026105,0.00017693778,0.16539255,0.00014023413,0.000089966474,0.00015191032,0.0012601034,0.00013632252,0.002390887],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99949884,0.000078475045,0.000022689226,0.00018408084,0.00013763345,0.000078328194],"domain_scores_gemma":[0.9995492,0.0000854958,0.00006470631,0.00007871751,0.00017289577,0.000048928952],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063932396,0.0010212677,0.00096822594,0.00065801194,0.0004115351,0.000593825,0.0017109067,0.00056956935,0.00063351233],"category_scores_gemma":[0.0012055489,0.0003653038,0.0007915213,0.0004745484,0.00021388345,0.0010616594,0.0011905193,0.0010588797,0.00038747813],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021108822,0.0006494637,0.014292462,0.00006173575,0.000292229,0.00021346672,0.00018858771,0.6011318,0.008066671,0.0026537457,0.005722969,0.36651585],"study_design_scores_gemma":[0.000003481961,0.000022883884,0.0006489034,0.0000017538279,0.000008777874,0.000013974264,0.0000074811255,0.9977976,0.0004930384,0.00074801414,0.00024872378,0.00000532435],"about_ca_topic_score_codex":0.014721087,"about_ca_topic_score_gemma":0.018548613,"teacher_disagreement_score":0.014721087,"about_ca_system_score_codex":0.0005492048,"about_ca_system_score_gemma":0.0012160236,"threshold_uncertainty_score":0.029270828},"labels":[],"label_agreement":null},{"id":"W4205349071","doi":"10.1155/2022/5006347","title":"Target Tracking and 3D Trajectory Reconstruction Based on Multicamera Calibration","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Trajectory; Computer vision; Computer science; Artificial intelligence; Tracking (education); Camera resectioning; Matching (statistics); Vehicle tracking system; Metric (unit); Mathematics; Segmentation; Engineering","score_opus":0.014366268186840066,"score_gpt":0.2632315152263881,"score_spread":0.24886524703954804,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4205349071","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":[0.019808266,0.00011785083,0.9778503,0.00004928441,0.000026748949,0.000028073538,0.00004525698,0.0007484689,0.0013258061],"genre_scores_gemma":[0.44565263,0.00035800494,0.54948884,0.00009031054,0.00003569564,0.000102791244,0.0004892302,0.00024064729,0.0035417995],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989478,0.00011744622,0.000037753838,0.00043261424,0.000364341,0.000100026315],"domain_scores_gemma":[0.99943906,0.0000775398,0.00009850453,0.00020456057,0.0001460826,0.00003423098],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00041449428,0.0008337184,0.0007922699,0.00089113734,0.0005265609,0.00085837825,0.00087069714,0.0008373736,0.0015749437],"category_scores_gemma":[0.0019379717,0.0006208728,0.0008591626,0.001647918,0.0005622531,0.0015272384,0.0015169652,0.0012224021,0.0010737746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031718222,0.00013760479,0.0050202436,0.0001359534,0.00013497725,0.0003475409,0.0005777328,0.28597185,0.0871026,0.01012622,0.002267959,0.6078602],"study_design_scores_gemma":[0.00001096124,0.00007529833,0.0017948886,0.000010909743,0.00002057015,0.00048322437,0.00008557159,0.9646469,0.027474208,0.002209793,0.00314681,0.00004091581],"about_ca_topic_score_codex":0.0057127667,"about_ca_topic_score_gemma":0.0043216217,"teacher_disagreement_score":0.0057127667,"about_ca_system_score_codex":0.00055582233,"about_ca_system_score_gemma":0.0012328846,"threshold_uncertainty_score":0.011359036},"labels":[],"label_agreement":null},{"id":"W4206077751","doi":"10.1016/j.cviu.2021.103352","title":"Cross-modal distillation for RGB-depth person re-identification","year":2022,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Artificial intelligence; Computer science; RGB color model; Computer vision; Pattern recognition (psychology); Identification (biology); Modality (human–computer interaction); Deep learning; Modal; Feature (linguistics); Distillation; Machine learning","score_opus":0.09468000872548839,"score_gpt":0.3613157230118719,"score_spread":0.26663571428638355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206077751","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03502154,0.0010079873,0.9545423,0.00020599223,0.00027956223,0.00010488914,0.00086295215,0.0037480984,0.004226724],"genre_scores_gemma":[0.40414643,0.0010397882,0.5722945,0.0005068311,0.0001901115,0.000185947,0.0033241664,0.00046931376,0.017842948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991652,0.0001131697,0.000029924537,0.00026052387,0.0002597303,0.00017151903],"domain_scores_gemma":[0.9994642,0.000095768264,0.000037396352,0.00018058371,0.00019060983,0.000031362943],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007400397,0.0012641991,0.0011942646,0.0011410944,0.00061625254,0.00079376536,0.0013512013,0.0011044908,0.0067237983],"category_scores_gemma":[0.0016701897,0.00046259168,0.000923694,0.0014478583,0.00042203593,0.0018074198,0.0026323388,0.0015097577,0.004354174],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00062018464,0.00029411478,0.001074278,0.00017225009,0.00010183449,0.00010669181,0.000095618685,0.020578796,0.06925009,0.0033498816,0.009021434,0.8953348],"study_design_scores_gemma":[0.000025777314,0.00018911468,0.004076967,0.00004469826,0.00007048284,0.00038880194,0.000113611895,0.90263367,0.07557058,0.0062526786,0.010572437,0.000061231476],"about_ca_topic_score_codex":0.0057250625,"about_ca_topic_score_gemma":0.013655538,"teacher_disagreement_score":0.0067237983,"about_ca_system_score_codex":0.00039527664,"about_ca_system_score_gemma":0.0010523936,"threshold_uncertainty_score":0.022493303},"labels":[],"label_agreement":null},{"id":"W4206567695","doi":"10.1007/s11042-021-11654-w","title":"Location-aware hazardous litter management for smart emergency governance in urban eco-cyber-physical systems","year":2022,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; York University","funders":"","keywords":"Computer science; Hazardous waste; Geospatial analysis; Emergency management; Smart city; Waste collection; Computer security; Municipal solid waste; Internet of Things; Waste management; Remote sensing","score_opus":0.02231829094286662,"score_gpt":0.28285454139278804,"score_spread":0.2605362504499214,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206567695","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2361461,0.00080487115,0.75355107,0.0003524398,0.00019366035,0.00010149162,0.00045527462,0.0019823958,0.0064128074],"genre_scores_gemma":[0.9635083,0.00019129018,0.03417484,0.000053434534,0.00002914487,0.000032405293,0.00020947217,0.000033714816,0.0017673619],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997943,0.000038102968,0.000009601158,0.000071637645,0.000040319203,0.00004601323],"domain_scores_gemma":[0.9996871,0.000067896,0.000068111505,0.00004507734,0.00009140702,0.00004044535],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025086585,0.0005243312,0.00074638845,0.00064525,0.00041637957,0.000953266,0.0009347172,0.00062598474,0.0016830795],"category_scores_gemma":[0.000627772,0.00022306049,0.00037197914,0.00051370426,0.00025115663,0.0012242268,0.0010170795,0.00037976413,0.0005134321],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006592495,0.00060916995,0.022486899,0.00033883803,0.000121121266,0.00045819674,0.00036360248,0.54253584,0.060339037,0.0058598416,0.0062792893,0.35994893],"study_design_scores_gemma":[0.000012873616,0.00011252543,0.004657718,0.00001810792,0.000041357038,0.0000774085,0.00033344777,0.98175836,0.00823139,0.002933656,0.0017977868,0.000025432431],"about_ca_topic_score_codex":0.002690455,"about_ca_topic_score_gemma":0.0048539336,"teacher_disagreement_score":0.002690455,"about_ca_system_score_codex":0.0003791125,"about_ca_system_score_gemma":0.000548514,"threshold_uncertainty_score":0.005630493},"labels":[],"label_agreement":null},{"id":"W4206662593","doi":"10.1049/cmu2.12322","title":"Coordinated 3D spectrum utilization for B5G indoor HetNets: A collaborated crowdsensing approach","year":2021,"lang":"en","type":"article","venue":"IET Communications","topic":"Video Surveillance and Tracking Methods","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":"Western University","funders":"National Natural Science Foundation of China","keywords":"Crowdsensing; Computer science; Computer network; Heterogeneous network; Wireless; Telecommunications; Wireless network; Data science","score_opus":0.1262847010364642,"score_gpt":0.3619478327010616,"score_spread":0.23566313166459743,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4206662593","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028825175,0.00026699028,0.9655263,0.00023390983,0.000064572836,0.000055698612,0.000048223683,0.00015807219,0.004821164],"genre_scores_gemma":[0.93106604,0.0002068319,0.06668293,0.00009296533,0.000054073545,0.00007935398,0.000050171606,0.000029366858,0.001738295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99929893,0.00024210317,0.000019545187,0.00015012872,0.00017122967,0.00011805526],"domain_scores_gemma":[0.99911374,0.0004907036,0.00009329456,0.00008486534,0.00012572715,0.00009163285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009916878,0.000749229,0.00095709646,0.0007859291,0.00078874564,0.0014009738,0.001318634,0.0010277296,0.0009982773],"category_scores_gemma":[0.0016609306,0.00034322162,0.0008989795,0.00072437106,0.0009575708,0.00083954225,0.002120519,0.00061023363,0.00015116383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00010394642,0.000068438625,0.00090878713,0.00008066818,0.000055019103,0.00030836515,0.00020315731,0.93597716,0.0063233785,0.01994131,0.0010439528,0.03498588],"study_design_scores_gemma":[0.0000035219778,0.000015627438,0.00007795462,0.0000029854912,0.000005447522,0.000019262594,0.0000428906,0.99522614,0.00043664692,0.0038277935,0.00033537822,0.0000064331143],"about_ca_topic_score_codex":0.0060490756,"about_ca_topic_score_gemma":0.004088332,"teacher_disagreement_score":0.0060490756,"about_ca_system_score_codex":0.00096908375,"about_ca_system_score_gemma":0.00090828544,"threshold_uncertainty_score":0.0120277405},"labels":[],"label_agreement":null},{"id":"W4207024264","doi":"10.1109/ssci50451.2021.9660071","title":"Towards Explainable Person Re- Identification","year":2021,"lang":"en","type":"article","venue":"2021 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Identification (biology); Perspective (graphical); Task (project management); Biometrics; Artificial intelligence; Machine learning; Range (aeronautics); Face (sociological concept); Selection (genetic algorithm); Engineering","score_opus":0.04768453344090044,"score_gpt":0.3099459879599282,"score_spread":0.26226145451902777,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4207024264","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16067173,0.0008642583,0.82842106,0.0013218574,0.00016055453,0.00016997315,0.00089412736,0.003994608,0.0035019289],"genre_scores_gemma":[0.76432616,0.00042411688,0.22358209,0.00062999915,0.00013002615,0.00012759588,0.0035838208,0.0003761696,0.006820018],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978963,0.0007394996,0.00007391403,0.0007953581,0.00033770688,0.00015716336],"domain_scores_gemma":[0.995271,0.0020697569,0.00039232775,0.0015219453,0.0006188102,0.00012609329],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033527552,0.0013032239,0.0010623812,0.0014147696,0.00062885415,0.0015706747,0.0023804712,0.0019842405,0.0018544394],"category_scores_gemma":[0.008216359,0.0005096112,0.0017605587,0.00078667095,0.000907567,0.0035047606,0.0020104186,0.0029428576,0.0015260384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00097194477,0.00075576256,0.024755713,0.00030850142,0.0005215903,0.0004676415,0.0017683211,0.43223652,0.013731813,0.02455972,0.014674322,0.48524815],"study_design_scores_gemma":[0.000011104603,0.000056688346,0.0019909644,0.000018452427,0.00003168754,0.00014241245,0.00010229839,0.9797999,0.0027696046,0.012482771,0.0025692894,0.000024938245],"about_ca_topic_score_codex":0.008506532,"about_ca_topic_score_gemma":0.008527692,"teacher_disagreement_score":0.008506532,"about_ca_system_score_codex":0.0014497463,"about_ca_system_score_gemma":0.00092010055,"threshold_uncertainty_score":0.017731309},"labels":[],"label_agreement":null},{"id":"W4210638231","doi":"10.1109/jac-ecc54461.2021.9691441","title":"Vulnerable Road Users Detection and Tracking using YOLOv4 and Deep SORT","year":2021,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"General Motors (Canada)","funders":"","keywords":"sort; Computer science; Artificial intelligence; Tracking (education); Object detection; Computer vision; Video tracking; Metric (unit); Vehicle tracking system; Frame (networking); Data association; Object (grammar); Real-time computing; Kalman filter; Pattern recognition (psychology); Engineering; Filter (signal processing)","score_opus":0.038275367147722526,"score_gpt":0.29590382366228646,"score_spread":0.25762845651456395,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4210638231","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.55424935,0.0033793545,0.4009047,0.00078885024,0.00058263075,0.0005399107,0.0044960687,0.026745627,0.008313493],"genre_scores_gemma":[0.7665532,0.00055153074,0.21482544,0.00031430772,0.00009580569,0.00017183923,0.010159029,0.0003096512,0.0070192586],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993185,0.00007387467,0.000033487653,0.00022252696,0.0001981838,0.00015344808],"domain_scores_gemma":[0.9995727,0.00006609329,0.000050096944,0.000090636284,0.0001519871,0.00006842322],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00076125615,0.0012210831,0.0016255248,0.0024975024,0.00059710967,0.0010350094,0.0018476468,0.0009722057,0.0017663764],"category_scores_gemma":[0.0014444394,0.00041139743,0.0009973685,0.0014236775,0.0003607006,0.0009447077,0.0014657596,0.0008397776,0.0009256124],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010986084,0.0006155565,0.01669243,0.00021979868,0.00043107194,0.0001799263,0.00014540918,0.05439494,0.016447242,0.0014992177,0.015543534,0.8927324],"study_design_scores_gemma":[0.00005987938,0.00034489637,0.007296629,0.00001913924,0.00008302709,0.00017897304,0.00009911904,0.9762368,0.010629976,0.001495731,0.0035193237,0.000036451616],"about_ca_topic_score_codex":0.02977536,"about_ca_topic_score_gemma":0.038680207,"teacher_disagreement_score":0.02977536,"about_ca_system_score_codex":0.0009720238,"about_ca_system_score_gemma":0.0013317671,"threshold_uncertainty_score":0.0592041},"labels":[],"label_agreement":null},{"id":"W4212863562","doi":"10.9781/ijimai.2022.01.002","title":"A Novel Technique to Detect and Track Multiple Objects in Dynamic Video Surveillance Systems.","year":2022,"lang":"en","type":"article","venue":"International Journal of Interactive Multimedia and Artificial Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Computer science; Track (disk drive); Computer vision; Real-time computing; Artificial intelligence; Human–computer interaction; Operating system","score_opus":0.031085799394504357,"score_gpt":0.3294405627721237,"score_spread":0.29835476337761935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212863562","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01741519,0.000912848,0.97713816,0.00014505802,0.00009311351,0.000083718645,0.00012855252,0.0011267245,0.0029566842],"genre_scores_gemma":[0.48218134,0.0013959171,0.50762206,0.00033051142,0.00012964399,0.00015185984,0.0005620414,0.00010834485,0.0075182463],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99946374,0.00005475522,0.00002708443,0.00015450541,0.0002343572,0.000065605265],"domain_scores_gemma":[0.9995314,0.00010621827,0.000085214415,0.00008077539,0.00016714257,0.0000292594],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053431786,0.00062341447,0.0004757459,0.0010180775,0.00037360878,0.0004822044,0.0008810104,0.00057202595,0.0011365316],"category_scores_gemma":[0.0012072081,0.0003038131,0.0003618078,0.00087854126,0.00045520806,0.0011531772,0.0007907908,0.0007201315,0.00058937166],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025710286,0.00012488189,0.0038734633,0.00021285102,0.0001017025,0.0003128606,0.00019987933,0.03655643,0.112448685,0.007264601,0.0058680275,0.8327795],"study_design_scores_gemma":[0.000018774856,0.00020570855,0.0060088392,0.00005261422,0.00006833284,0.0010474804,0.000055915432,0.91567045,0.05490315,0.0036575657,0.018266616,0.00004456241],"about_ca_topic_score_codex":0.005619994,"about_ca_topic_score_gemma":0.0076706894,"teacher_disagreement_score":0.005619994,"about_ca_system_score_codex":0.0006450419,"about_ca_system_score_gemma":0.00074271596,"threshold_uncertainty_score":0.01117456},"labels":[],"label_agreement":null},{"id":"W4212931582","doi":"10.1109/iotm.001.2100088","title":"Unmanned Aerial Multi-Object Dynamic Frame Detection and Skipping Using Deep Learning on the Internet of Drones","year":2021,"lang":"en","type":"article","venue":"IEEE Internet of Things Magazine","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Drone; Computer science; Frame (networking); Software deployment; Real-time computing; Deep learning; Artificial intelligence; The Internet; Process (computing); Object detection; Computer vision; Telecommunications; World Wide Web; Pattern recognition (psychology)","score_opus":0.02346996202038674,"score_gpt":0.283216060584466,"score_spread":0.25974609856407926,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4212931582","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.28219378,0.0008696652,0.71043974,0.00037326643,0.0001134844,0.00006714501,0.00031284455,0.0017072639,0.003922825],"genre_scores_gemma":[0.90013796,0.0003462375,0.094958946,0.00013654257,0.000032003583,0.000034676144,0.00051252247,0.000041184,0.0037999747],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999874,0.000013052128,0.000004781066,0.0000423917,0.00003394387,0.000031826316],"domain_scores_gemma":[0.99987733,0.00003544769,0.000016061796,0.00001913748,0.000038499395,0.000013450115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023928842,0.0005527191,0.0003851615,0.00049126416,0.00018961432,0.00033918617,0.0006918708,0.0003989622,0.00066257745],"category_scores_gemma":[0.0005711894,0.00023661292,0.0003414744,0.0003602284,0.00025068055,0.0005956443,0.0004981143,0.00066920376,0.00017665938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020443875,0.00016625572,0.003086621,0.000054754255,0.000077445526,0.00024556034,0.000093962706,0.51894116,0.021288859,0.0034696055,0.0028674367,0.44950384],"study_design_scores_gemma":[0.0000020261512,0.00001527742,0.00030512415,0.0000026376092,0.0000028841114,0.000010588588,0.0000057803954,0.9975527,0.001476389,0.00039579987,0.0002285113,0.0000022560105],"about_ca_topic_score_codex":0.017410748,"about_ca_topic_score_gemma":0.017956756,"teacher_disagreement_score":0.017410748,"about_ca_system_score_codex":0.0005163045,"about_ca_system_score_gemma":0.0004913714,"threshold_uncertainty_score":0.034618855},"labels":[],"label_agreement":null},{"id":"W4213060227","doi":"10.1109/tnnls.2022.3149332","title":"Filter Pruning by Switching to Neighboring CNNs With Good Attributes","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":62,"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":"Australian Research Council; Canadian Institute for Advanced Research","keywords":"Pruning; Filter (signal processing); Artificial intelligence; Computer science; Pattern recognition (psychology); Mathematics; Computer vision; Biology; Horticulture","score_opus":0.015295117027075722,"score_gpt":0.23315120914275286,"score_spread":0.21785609211567714,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213060227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18224569,0.0006041889,0.80918115,0.00028880977,0.00019609138,0.00012871003,0.00014382025,0.0019842905,0.0052272016],"genre_scores_gemma":[0.82173246,0.00019749251,0.1740175,0.00023773145,0.00008226379,0.00011450501,0.00032174267,0.00019242958,0.0031039028],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991696,0.000085975385,0.0000804589,0.00023275892,0.00027780206,0.00015350277],"domain_scores_gemma":[0.99800986,0.00063232746,0.00019403611,0.00052978547,0.00050575344,0.00012829016],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012976225,0.0011363205,0.0011254353,0.001083787,0.0006497205,0.0012001924,0.0016677283,0.0010973968,0.0026520807],"category_scores_gemma":[0.0057432964,0.0004566377,0.00097245735,0.00067276886,0.000707406,0.0023370285,0.0013367204,0.0013481529,0.0005572057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008147505,0.0005213726,0.012541015,0.00021108374,0.0002821608,0.0006394574,0.00031144905,0.28771818,0.081386,0.019872911,0.005982634,0.58971894],"study_design_scores_gemma":[0.000023382927,0.00015585311,0.0019690022,0.000024557497,0.00009956018,0.00016468183,0.000043840188,0.960404,0.026286056,0.008355533,0.0024536303,0.000019852385],"about_ca_topic_score_codex":0.004391348,"about_ca_topic_score_gemma":0.0073168743,"teacher_disagreement_score":0.004391348,"about_ca_system_score_codex":0.0010277153,"about_ca_system_score_gemma":0.0010977293,"threshold_uncertainty_score":0.008872092},"labels":[],"label_agreement":null},{"id":"W4213427671","doi":"10.1155/2022/5867524","title":"A Novel Traffic Surveillance System Using an Uncalibrated Camera","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Track (disk drive); Computer vision; Artificial intelligence; Tracking (education); Tracking system; Image processing; Real-time computing; Image (mathematics); Kalman filter","score_opus":0.028532929798946918,"score_gpt":0.28735205161473665,"score_spread":0.25881912181578975,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4213427671","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07874488,0.0011012758,0.90259814,0.00039204376,0.0004234226,0.00027528458,0.00025648263,0.0049627344,0.011245753],"genre_scores_gemma":[0.41479585,0.0009912797,0.56694025,0.0005045354,0.00035494572,0.00030801503,0.0005524605,0.00012693669,0.015425776],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996043,0.00005107095,0.000015741127,0.00013906401,0.00014684824,0.00004303116],"domain_scores_gemma":[0.9996915,0.000038828555,0.00003156336,0.000046795372,0.00014299815,0.000048279526],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024614623,0.0004985587,0.0005900928,0.0006498645,0.00047256314,0.0007767136,0.0010531041,0.0008924787,0.0027835364],"category_scores_gemma":[0.00043623126,0.0003853659,0.00032746565,0.00036080237,0.00026378955,0.0011228261,0.00064197724,0.0008009166,0.0012250487],"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":[0.0004743514,0.00024583214,0.0027659282,0.0003357103,0.00008974983,0.0007801105,0.00029810154,0.008255512,0.507892,0.0054471493,0.009882178,0.46353343],"study_design_scores_gemma":[0.00022657024,0.0019564282,0.011102582,0.00012273966,0.00029813353,0.006305474,0.00015913167,0.5598912,0.31481847,0.0016196847,0.103182346,0.00031717418],"about_ca_topic_score_codex":0.0015941786,"about_ca_topic_score_gemma":0.0018555095,"teacher_disagreement_score":0.0027835364,"about_ca_system_score_codex":0.00036612767,"about_ca_system_score_gemma":0.00058761716,"threshold_uncertainty_score":0.009311855},"labels":[],"label_agreement":null},{"id":"W4214837610","doi":"10.1109/mmul.2022.3156032","title":"Efficient Multimedia Frame-Skipping Architecture Using Deep Learning in Vehicular Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Multimedia","topic":"Video Surveillance and Tracking Methods","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":"Brandon University","funders":"","keywords":"Computer science; Frame (networking); Software deployment; Process (computing); Deep learning; Multimedia; Object detection; Artificial intelligence; Real-time computing; Computer vision; Computer network; Pattern recognition (psychology)","score_opus":0.020023622941645097,"score_gpt":0.27429807867304673,"score_spread":0.2542744557314016,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4214837610","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08235219,0.0016339873,0.908796,0.00035045823,0.00016802066,0.000055719705,0.0002182197,0.0019286375,0.0044967355],"genre_scores_gemma":[0.9090245,0.00059588754,0.0840002,0.00016959665,0.000052409177,0.00006571791,0.0005045699,0.00006152743,0.0055255657],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998287,0.00002497067,0.000008435727,0.000041500174,0.00004314125,0.00005319096],"domain_scores_gemma":[0.99986947,0.000032626172,0.000012153967,0.000015247719,0.000056101224,0.00001433],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003657779,0.00068811444,0.00057253975,0.0004540246,0.00030991627,0.00051264255,0.0014198073,0.00056396984,0.0015339616],"category_scores_gemma":[0.00063445646,0.0002938406,0.00035171496,0.00042488865,0.00030379425,0.0008094797,0.00068340433,0.0008988994,0.00043118428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025438087,0.00014089598,0.0011028409,0.00007469204,0.000055884862,0.00010778151,0.000055994704,0.62502253,0.010550157,0.008027077,0.0042443033,0.35036343],"study_design_scores_gemma":[0.000002699951,0.000024608984,0.00006191934,0.0000029211728,0.000004787145,0.0000072486055,0.000004281824,0.997168,0.001293871,0.0011157163,0.00031107766,0.0000029295284],"about_ca_topic_score_codex":0.016391205,"about_ca_topic_score_gemma":0.019672,"teacher_disagreement_score":0.016391205,"about_ca_system_score_codex":0.0009392251,"about_ca_system_score_gemma":0.0009781818,"threshold_uncertainty_score":0.03259164},"labels":[],"label_agreement":null},{"id":"W4220913778","doi":"10.1145/3501404","title":"Clustering Matters: Sphere Feature for Fully Unsupervised Person Re-identification","year":2022,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Artificial intelligence; Cluster analysis; Unsupervised learning; Pattern recognition (psychology); Computer science; Feature (linguistics); Feature learning; Feature vector; Complete-linkage clustering; Artificial neural network; Machine learning; Correlation clustering; Canopy clustering algorithm","score_opus":0.05064696583485039,"score_gpt":0.3158366727462122,"score_spread":0.2651897069113618,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4220913778","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01952454,0.00036930796,0.96910346,0.00021389688,0.00012363945,0.00010183611,0.00091276114,0.0070809787,0.0025696135],"genre_scores_gemma":[0.43536386,0.0005519821,0.5438232,0.00050392566,0.00022603224,0.0002446821,0.008323478,0.0016559337,0.009306982],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985488,0.00025740202,0.000073973926,0.00050828204,0.00041445208,0.00019708405],"domain_scores_gemma":[0.99852765,0.00017517952,0.000109915774,0.0005986122,0.00049295416,0.00009575858],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010232832,0.0014691387,0.0014767107,0.001988393,0.0006973739,0.0010223001,0.0022608538,0.0012383998,0.003136365],"category_scores_gemma":[0.0034493776,0.00046248033,0.001314421,0.002187932,0.0007115317,0.0020470337,0.0022578333,0.0013993131,0.004651106],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007949864,0.0002516553,0.004805056,0.00016920218,0.0002119031,0.0002432692,0.0002854515,0.07282802,0.02308063,0.009622905,0.04383495,0.8438721],"study_design_scores_gemma":[0.000021514354,0.00006864839,0.0034118833,0.000019279089,0.00003058515,0.00028752277,0.00009070874,0.95884687,0.016317291,0.010685055,0.010159256,0.000061280334],"about_ca_topic_score_codex":0.008977992,"about_ca_topic_score_gemma":0.012491358,"teacher_disagreement_score":0.008977992,"about_ca_system_score_codex":0.0008873195,"about_ca_system_score_gemma":0.0011674738,"threshold_uncertainty_score":0.017851472},"labels":[],"label_agreement":null},{"id":"W4221041219","doi":"10.1155/2022/9189600","title":"Vehicle Detection for Vision-Based Intelligent Transportation Systems Using Convolutional Neural Network Algorithm","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":48,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Intelligent transportation system; Convolutional neural network; Computer science; Histogram; Pedestrian detection; Traffic flow (computer networking); Artificial neural network; Algorithm; Key (lock); Advanced Traffic Management System; Object detection; Artificial intelligence; Real-time computing; Advanced driver assistance systems; Histogram of oriented gradients; Pattern recognition (psychology); Pedestrian; Engineering; Image (mathematics)","score_opus":0.023202742668510645,"score_gpt":0.30009903171554375,"score_spread":0.2768962890470331,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221041219","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10117348,0.00089505105,0.89010173,0.00026615118,0.00011826481,0.00009905228,0.00017560877,0.0024679059,0.004702756],"genre_scores_gemma":[0.81678814,0.00048157573,0.17570236,0.0001237027,0.000033791228,0.00009598541,0.0005871246,0.0000577326,0.0061295363],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99982065,0.000018332139,0.000011159117,0.000058085443,0.000052221712,0.0000396066],"domain_scores_gemma":[0.99985516,0.000031657888,0.000016132686,0.0000116648935,0.00007835911,0.0000070852307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00035688642,0.000571915,0.00036397937,0.0006565424,0.0002718784,0.0005059565,0.0007658263,0.0005222271,0.0013938408],"category_scores_gemma":[0.00066424377,0.00025071952,0.00046104673,0.0004533648,0.00018872815,0.00058547186,0.00032643983,0.000567363,0.00038953515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015264959,0.00013475325,0.002785542,0.00008371131,0.000090405585,0.00006726778,0.00003935353,0.5468744,0.015541579,0.0025518092,0.002973585,0.42870486],"study_design_scores_gemma":[0.00000159801,0.000011934617,0.00027311515,0.0000023425316,0.0000043193204,0.000006267253,0.0000025401614,0.9974825,0.0017844007,0.0002166628,0.00021224574,0.0000021372116],"about_ca_topic_score_codex":0.031621754,"about_ca_topic_score_gemma":0.026699238,"teacher_disagreement_score":0.031621754,"about_ca_system_score_codex":0.0012375667,"about_ca_system_score_gemma":0.0010684599,"threshold_uncertainty_score":0.06287539},"labels":[],"label_agreement":null},{"id":"W4221161046","doi":"10.1155/2022/2771085","title":"Roadside LiDAR Vehicle Detection and Tracking Using Range and Intensity Background Subtraction","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"New Jersey Department of Transportation","keywords":"Background subtraction; Lidar; Computer science; Point cloud; Computer vision; Artificial intelligence; Azimuth; Subtraction; Tracking (education); Range (aeronautics); Trajectory; Pattern recognition (psychology); Remote sensing; Mathematics; Geography; Optics; Pixel; Physics","score_opus":0.03412429716758047,"score_gpt":0.2950833098879471,"score_spread":0.26095901272036665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4221161046","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029942332,0.0000965308,0.9679585,0.000038355247,0.000024546654,0.000028362738,0.000038420705,0.00092261593,0.0009503985],"genre_scores_gemma":[0.45481142,0.00017259248,0.54221493,0.000080514415,0.000037406488,0.0000685344,0.00034201023,0.00009141297,0.002181199],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994485,0.000041911477,0.000018255892,0.00017768359,0.00024237644,0.00007131613],"domain_scores_gemma":[0.9996519,0.00006115436,0.000034378238,0.000055247514,0.0001772825,0.000020100246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004027706,0.0005473574,0.00063742325,0.0011071786,0.00031301277,0.00064910034,0.0010806554,0.00047736012,0.0006191954],"category_scores_gemma":[0.00089086307,0.00039988745,0.00058898306,0.0007548886,0.00023996469,0.001053523,0.00090267794,0.00051955687,0.0005801019],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013214625,0.00017142012,0.0056548663,0.00007611845,0.00008713585,0.00011855616,0.0001260643,0.100931734,0.06599222,0.0046838727,0.0012464094,0.82077944],"study_design_scores_gemma":[0.000007839097,0.000054638964,0.0017690023,0.0000061231876,0.00001944255,0.00013526977,0.00002987719,0.9728457,0.022312379,0.001293514,0.0015090562,0.000017235467],"about_ca_topic_score_codex":0.003846028,"about_ca_topic_score_gemma":0.0040751933,"teacher_disagreement_score":0.003846028,"about_ca_system_score_codex":0.00040748162,"about_ca_system_score_gemma":0.00074252946,"threshold_uncertainty_score":0.007647276},"labels":[],"label_agreement":null},{"id":"W4224943773","doi":"10.1007/s42991-021-00215-1","title":"Similarity learning networks for animal individual re-identification: an ecological perspective","year":2022,"lang":"en","type":"article","venue":"Mammalian Biology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Vector Institute","funders":"","keywords":"Generality; Identification (biology); Artificial intelligence; Similarity (geometry); Animal ecology; Machine learning; Deep learning; Population; Biology; Perspective (graphical); Computer science; Ecology; Image (mathematics)","score_opus":0.06329520285680046,"score_gpt":0.34652808219008996,"score_spread":0.2832328793332895,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4224943773","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025449445,0.00276121,0.967872,0.0013836657,0.00007184955,0.00003984191,0.00012154985,0.00017865359,0.0021216935],"genre_scores_gemma":[0.7652265,0.0041700923,0.2170377,0.00045759886,0.00061992055,0.00018219791,0.0005436256,0.00014466954,0.011617721],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904484,0.00037671995,0.000050113198,0.000300911,0.00014917854,0.00007815163],"domain_scores_gemma":[0.9920427,0.00522752,0.0007828906,0.00078350905,0.00088708306,0.00027635403],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027013535,0.0007416126,0.0018136255,0.0023742577,0.00068933755,0.0019434801,0.0031180957,0.0029508616,0.0023023568],"category_scores_gemma":[0.013807554,0.00063098903,0.00084283104,0.0024495695,0.0018551657,0.0047811316,0.0019736737,0.002268387,0.0004885221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011506608,0.00019515841,0.004478354,0.00022123226,0.00018562781,0.000110328874,0.0003007281,0.657834,0.0021764925,0.16461313,0.0031117613,0.16665816],"study_design_scores_gemma":[0.0000033756646,0.000022592048,0.00048996805,0.000016801461,0.000011180505,0.000035013745,0.000030032617,0.9265957,0.00029901057,0.07167875,0.0008052356,0.000012316467],"about_ca_topic_score_codex":0.0073778355,"about_ca_topic_score_gemma":0.004841482,"teacher_disagreement_score":0.0073778355,"about_ca_system_score_codex":0.001689064,"about_ca_system_score_gemma":0.00077943667,"threshold_uncertainty_score":0.014669776},"labels":[],"label_agreement":null},{"id":"W4225146327","doi":"10.1155/2022/4110246","title":"Pedestrian Fall Event Detection in Complex Scenes Based on Attention-Guided Neural Network","year":2022,"lang":"en","type":"article","venue":"Mathematical Problems in Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"Graduate Research and Innovation Projects of Jiangsu Province; Nanjing Institute of Technology; Government of Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Pedestrian detection; Artificial intelligence; Bounding overwatch; Sliding window protocol; Convolutional neural network; Support vector machine; Pedestrian; Event (particle physics); Classifier (UML); Computer vision; Minimum bounding box; Feature (linguistics); Pattern recognition (psychology); Window (computing); Engineering; Image (mathematics)","score_opus":0.04145410089879711,"score_gpt":0.2771028298524296,"score_spread":0.2356487289536325,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4225146327","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.31726152,0.0012729472,0.6708713,0.0003080039,0.00019575452,0.00012363734,0.00054742536,0.004891176,0.004528204],"genre_scores_gemma":[0.9161863,0.0003434052,0.078883365,0.00017784256,0.000060699414,0.00004500378,0.0009005943,0.00007436034,0.0033285369],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997236,0.0000282561,0.0000108543945,0.00010520816,0.00006319431,0.00006895069],"domain_scores_gemma":[0.9997693,0.000059806753,0.000039761693,0.000023352077,0.00007466731,0.000033165095],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040925544,0.0010112431,0.00079135277,0.0010469831,0.00032318835,0.0004396505,0.00086767995,0.00057092536,0.0010591737],"category_scores_gemma":[0.0007461812,0.0003276402,0.00050888525,0.00065319927,0.00029455777,0.00063324126,0.0006440504,0.0005387918,0.0003161206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014345705,0.0006460106,0.016687224,0.00014169482,0.00019985411,0.00051529566,0.00015772521,0.16003293,0.04969697,0.0015520683,0.009383019,0.7595526],"study_design_scores_gemma":[0.00001179804,0.00007264019,0.004042986,0.000007826429,0.00002270175,0.00008154852,0.000012787369,0.9898825,0.0047231065,0.00067017414,0.00046329128,0.000008558409],"about_ca_topic_score_codex":0.016852196,"about_ca_topic_score_gemma":0.019402312,"teacher_disagreement_score":0.016852196,"about_ca_system_score_codex":0.00077749864,"about_ca_system_score_gemma":0.0005573996,"threshold_uncertainty_score":0.03350824},"labels":[],"label_agreement":null},{"id":"W4226049128","doi":"10.1109/tip.2022.3162961","title":"Universal Background Subtraction Based on Arithmetic Distribution Neural Network","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Histogram; Computer science; Artificial neural network; Subtraction; Artificial intelligence; Convolutional neural network; Algorithm; Probability distribution; Pattern recognition (psychology); Arithmetic; Mathematics; Image (mathematics); Statistics","score_opus":0.024862131119723848,"score_gpt":0.2792896840390449,"score_spread":0.25442755291932106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226049128","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0098029515,0.00028972854,0.9866463,0.00008663007,0.000041426618,0.00002170046,0.000048014856,0.0013063155,0.0017569137],"genre_scores_gemma":[0.5082953,0.00084474485,0.48269412,0.00039480728,0.0000977602,0.00007307482,0.0004897053,0.00030968737,0.006800837],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963236,0.000037028003,0.000015019177,0.00010772121,0.00013951305,0.00006833249],"domain_scores_gemma":[0.9997813,0.00005363618,0.000025086107,0.000030397008,0.000091146816,0.000018418863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005448415,0.00095614727,0.00082044356,0.00095829676,0.00036254255,0.0009684312,0.0016406603,0.0006857566,0.0018345113],"category_scores_gemma":[0.0009892654,0.00040862273,0.0006913432,0.0008980702,0.00055012613,0.0017065883,0.0010990467,0.0011183876,0.0006022924],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026239085,0.00009350517,0.0013692804,0.000118020034,0.000107174055,0.00014809935,0.00008843627,0.23279859,0.038161803,0.016043223,0.0027137697,0.7080957],"study_design_scores_gemma":[0.000005335211,0.00001861053,0.00025268606,0.0000066950392,0.000016940976,0.000051355928,0.000006272007,0.9837624,0.011935966,0.0026708336,0.0012634081,0.000009547156],"about_ca_topic_score_codex":0.008575673,"about_ca_topic_score_gemma":0.00914965,"teacher_disagreement_score":0.008575673,"about_ca_system_score_codex":0.0009816122,"about_ca_system_score_gemma":0.0010067386,"threshold_uncertainty_score":0.017051518},"labels":[],"label_agreement":null},{"id":"W4226049389","doi":"10.1109/access.2022.3170481","title":"Extracting Unambiguous Drone Signature Using High-Speed Camera","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; National Research Council Canada","funders":"","keywords":"Drone; Computer science; Computer vision; Propeller; Rotation (mathematics); Artificial intelligence; Signature (topology); Frame (networking); Frame rate; Kinematics; Mathematics; Engineering; Marine engineering","score_opus":0.06241831130606304,"score_gpt":0.3500379445145664,"score_spread":0.28761963320850337,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226049389","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.40123075,0.0011312693,0.58459646,0.0001670477,0.00017688367,0.00028910063,0.00044380047,0.0026722613,0.009292513],"genre_scores_gemma":[0.7008253,0.0010939419,0.2927226,0.00010221744,0.000063124775,0.00007418387,0.00085789256,0.000087076645,0.004173563],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99975914,0.000018147797,0.000009169348,0.000041429743,0.00014563535,0.0000265005],"domain_scores_gemma":[0.99981326,0.000030199759,0.000031305604,0.00002508093,0.00008386334,0.000016251355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00013504452,0.00049277005,0.0003851721,0.0010761895,0.00013196733,0.0005117774,0.00031678163,0.00042588296,0.0011570972],"category_scores_gemma":[0.0004878794,0.00014978298,0.00020947766,0.0005129346,0.00012738912,0.0006092434,0.00031742555,0.00032238488,0.0009305137],"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":[0.00023533356,0.00009400917,0.0026370327,0.00026290954,0.00004188794,0.00052960176,0.00011344819,0.006789711,0.53422743,0.0005638463,0.0015249193,0.45297983],"study_design_scores_gemma":[0.00008040972,0.00049448654,0.029383799,0.00007711468,0.00008337274,0.0027801052,0.0003277124,0.4234717,0.5307496,0.00089160027,0.011557956,0.000102078935],"about_ca_topic_score_codex":0.001205944,"about_ca_topic_score_gemma":0.0017219285,"teacher_disagreement_score":0.001205944,"about_ca_system_score_codex":0.00014450272,"about_ca_system_score_gemma":0.00021672169,"threshold_uncertainty_score":0.0038709044},"labels":[],"label_agreement":null},{"id":"W4226294111","doi":"10.1109/tmm.2022.3163847","title":"Spatial-Channel Enhanced Transformer for Visible-Infrared Person Re-Identification","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":110,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Discriminative model; Artificial intelligence; Feature learning; Pattern recognition (psychology); Embedding; Transformer; Feature extraction; Feature (linguistics); Feature vector; Computer vision; Engineering","score_opus":0.03848727033689459,"score_gpt":0.2986878252806538,"score_spread":0.2602005549437592,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226294111","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048948042,0.0006660052,0.94385624,0.00013179216,0.00013772109,0.00006628809,0.00028810935,0.0030038694,0.0029019006],"genre_scores_gemma":[0.7599541,0.00072155456,0.22158395,0.00038261185,0.00012799572,0.00011090659,0.0018035708,0.00026240034,0.015052902],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995158,0.000073401825,0.000015624426,0.00017599823,0.00013281178,0.00008637104],"domain_scores_gemma":[0.99957687,0.00007640397,0.000052255553,0.00016021093,0.00010899742,0.000025212785],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006579759,0.0008422583,0.0010321828,0.00069302233,0.0003139042,0.0004295476,0.0019407803,0.0008006884,0.0028872108],"category_scores_gemma":[0.001630066,0.00030670824,0.0009265676,0.0008837609,0.0004462184,0.0018420061,0.0011988218,0.0010996158,0.0020624334],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005619679,0.00020938851,0.0024571405,0.00014032351,0.0001319143,0.00029250424,0.00010331662,0.06756301,0.03175579,0.0042278827,0.01040387,0.8821529],"study_design_scores_gemma":[0.000023342805,0.00022150841,0.002184511,0.000015413036,0.000084292995,0.0007543739,0.00006943798,0.9462327,0.038725816,0.0059454716,0.0057019996,0.000041047133],"about_ca_topic_score_codex":0.0026181617,"about_ca_topic_score_gemma":0.0038644043,"teacher_disagreement_score":0.0028872108,"about_ca_system_score_codex":0.00037402005,"about_ca_system_score_gemma":0.0005532554,"threshold_uncertainty_score":0.009658694},"labels":[],"label_agreement":null},{"id":"W4226343310","doi":"10.22215/etd/2021-14848","title":"Quadcopter Behaviour Identification","year":2021,"lang":"en","type":"dissertation","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Quadcopter; Drone; Computer science; Artificial intelligence; Identification (biology); Artificial neural network; Computer security; Context (archaeology); Payload (computing); Deep learning; Machine learning; Engineering; Network packet; Geography","score_opus":0.020667578030735035,"score_gpt":0.3267299076095919,"score_spread":0.3060623295788569,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226343310","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":[0.90813184,0.00090487115,0.06897459,0.000224667,0.00017211138,0.0002403941,0.0042852717,0.002204707,0.014861526],"genre_scores_gemma":[0.97490066,0.00022648423,0.01591668,0.00007492542,0.000020513166,0.00005100254,0.0034176013,0.000067688256,0.00532435],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9998216,0.000014714885,0.00000820223,0.00007322401,0.000044838616,0.000037388643],"domain_scores_gemma":[0.9996921,0.00004496855,0.0000607506,0.00004642765,0.00010898108,0.00004679326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00010344887,0.00058119884,0.0004009406,0.0009410317,0.00019827171,0.00027940614,0.00040356058,0.00039724814,0.003174834],"category_scores_gemma":[0.0006431527,0.0001364362,0.00027837657,0.00035480756,0.0001294558,0.0003436317,0.0005236604,0.00029657118,0.0017799939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011353054,0.00032958438,0.26009008,0.0006662157,0.00020090546,0.0016346299,0.0006947064,0.03286184,0.12670484,0.0010654181,0.021120714,0.5534958],"study_design_scores_gemma":[0.00004536118,0.0005808039,0.41121805,0.00013059394,0.00006867884,0.00203452,0.0008925454,0.53278923,0.03636466,0.0009935739,0.0148169035,0.00006510181],"about_ca_topic_score_codex":0.0054702526,"about_ca_topic_score_gemma":0.010687609,"teacher_disagreement_score":0.0054702526,"about_ca_system_score_codex":0.00023401529,"about_ca_system_score_gemma":0.00020790848,"threshold_uncertainty_score":0.010876834},"labels":[],"label_agreement":null},{"id":"W4226369513","doi":"10.1016/j.patcog.2023.109830","title":"CrowdMLP: Weakly-supervised crowd counting via multi-granularity MLP","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph; Memorial University of Newfoundland","funders":"Zhejiang Sci-Tech University; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Granularity; Computer science; Cardinality (data modeling); Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Data mining; Spatial analysis; Machine learning; Mathematics; Statistics","score_opus":0.07720168428992544,"score_gpt":0.3076822966566913,"score_spread":0.23048061236676584,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226369513","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009216333,0.00022882258,0.9795544,0.00016449046,0.00011207384,0.00008676102,0.0004099573,0.0090217665,0.0012053913],"genre_scores_gemma":[0.3304535,0.00023950369,0.6539593,0.00044331772,0.00029103947,0.0004839529,0.002905554,0.0012868559,0.009936923],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99809855,0.00042981812,0.00009419061,0.0006524085,0.00045875803,0.00026625322],"domain_scores_gemma":[0.9979377,0.00079410523,0.00015829143,0.00052946026,0.00040968574,0.0001707435],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020819837,0.0019299642,0.0025627727,0.0019961635,0.0010175592,0.0017757516,0.0042019286,0.0028634123,0.0053098523],"category_scores_gemma":[0.0061776685,0.0012668902,0.0013372571,0.0016195963,0.0009401548,0.002582611,0.0054994207,0.0026496637,0.0037063882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00066117407,0.00032597603,0.0020440177,0.0002479605,0.00027899706,0.00026352474,0.00015571872,0.22261982,0.014393724,0.005414711,0.01884222,0.7347522],"study_design_scores_gemma":[0.000009292327,0.000021062215,0.00021061924,0.000008539456,0.000009778236,0.000022655278,0.000010784286,0.99428046,0.001777161,0.0027812389,0.0008607031,0.000007743812],"about_ca_topic_score_codex":0.0073451106,"about_ca_topic_score_gemma":0.008988278,"teacher_disagreement_score":0.0073451106,"about_ca_system_score_codex":0.0009864451,"about_ca_system_score_gemma":0.0015245767,"threshold_uncertainty_score":0.017763197},"labels":[],"label_agreement":null},{"id":"W4226446052","doi":"10.1155/2022/7111248","title":"Generative Adversarial Networks for Unmanned Aerial Vehicle Object Detection with Fusion Technology","year":2022,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Autopilot; Drone; Lidar; Artificial intelligence; Object detection; Obstacle; Modular design; Computer vision; Controller (irrigation); Real-time computing; Benchmark (surveying); Object (grammar); Orientation (vector space); Engineering; Control engineering; Pattern recognition (psychology)","score_opus":0.009259382376221675,"score_gpt":0.2564099738657909,"score_spread":0.24715059148956922,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4226446052","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008015705,0.0004660051,0.98900807,0.00026748097,0.00004384546,0.000027251619,0.00004237636,0.00035014085,0.0017792934],"genre_scores_gemma":[0.8570885,0.00084598764,0.13458437,0.00041001232,0.00012603526,0.00017290362,0.00028147045,0.00009675991,0.0063939057],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994978,0.000149084,0.000019369234,0.00013441299,0.00013461927,0.00006467831],"domain_scores_gemma":[0.99904233,0.0005913822,0.000121231955,0.00008124347,0.00012093784,0.000042888023],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010341874,0.00078569364,0.0006506345,0.0005397309,0.00029314973,0.0006510085,0.001205239,0.0009270068,0.0017416617],"category_scores_gemma":[0.002563052,0.00038380479,0.00072856224,0.0004918824,0.00096300314,0.0010206263,0.001539704,0.001885331,0.00037402267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000048758706,0.000021622393,0.0004193404,0.000036120335,0.000036451635,0.000065661654,0.000048776426,0.94043344,0.0016706086,0.014057609,0.0010618877,0.042099725],"study_design_scores_gemma":[0.0000011502634,0.000008437144,0.000052789368,0.0000029776356,0.0000026962696,0.000008047278,0.0000028342786,0.9952701,0.00030880887,0.0041011195,0.00023832334,0.0000027250076],"about_ca_topic_score_codex":0.0035591386,"about_ca_topic_score_gemma":0.0021619452,"teacher_disagreement_score":0.0035591386,"about_ca_system_score_codex":0.0010589128,"about_ca_system_score_gemma":0.0004873354,"threshold_uncertainty_score":0.0076829195},"labels":[],"label_agreement":null},{"id":"W4230054345","doi":"10.32920/ryerson.14646711","title":"Obstacle detection using Microsoft Kinect","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"RANSAC; Computer science; Computer vision; Artificial intelligence; Segmentation; Obstacle; Point cloud; Software; Point (geometry); MATLAB; Image segmentation; Computer graphics (images); Image (mathematics); Mathematics","score_opus":0.05824360816384602,"score_gpt":0.322409238116959,"score_spread":0.264165629953113,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230054345","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":[0.03156804,0.0014654975,0.9425967,0.00018202807,0.0003361029,0.00036108404,0.0022731302,0.007662693,0.013554781],"genre_scores_gemma":[0.23217934,0.0026999514,0.7392138,0.00023072764,0.00007017568,0.00070678646,0.004116344,0.0007695715,0.020013358],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99887496,0.000060055525,0.000052730502,0.00023289252,0.00070455944,0.00007478922],"domain_scores_gemma":[0.9996854,0.000052058822,0.00004979036,0.000032837033,0.000144886,0.000034955905],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042141517,0.0011728901,0.0010486376,0.0019171186,0.000400893,0.0012377322,0.001365394,0.0010921698,0.006901164],"category_scores_gemma":[0.00089853024,0.00079214654,0.0009822302,0.0009781249,0.00029660793,0.0013315819,0.0014631937,0.0008561086,0.0034346713],"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":[0.00064899435,0.00020104034,0.0036616796,0.0018791916,0.00013624234,0.0006931472,0.0006022339,0.049001962,0.2574908,0.0088801775,0.015772985,0.66103154],"study_design_scores_gemma":[0.00008406902,0.00044441692,0.020147651,0.00076093554,0.000114398325,0.0016887664,0.00045850038,0.5910023,0.28627303,0.0057681412,0.09288612,0.00037172885],"about_ca_topic_score_codex":0.0021814436,"about_ca_topic_score_gemma":0.0031750845,"teacher_disagreement_score":0.006901164,"about_ca_system_score_codex":0.00042250848,"about_ca_system_score_gemma":0.0010898375,"threshold_uncertainty_score":0.023086727},"labels":[],"label_agreement":null},{"id":"W4230124334","doi":"10.1109/wvs.1998.646015","title":"Tracking a person with pre-recorded image database and a pan, tilt, and zoom camera","year":2002,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada); University of Toronto","funders":"","keywords":"Computer vision; Artificial intelligence; Computer science; Zoom; Tracking (education); Tilt (camera); Segmentation; Image segmentation; Set (abstract data type); Image (mathematics); Video tracking; Object (grammar); Mathematics; Engineering","score_opus":0.03637787054081854,"score_gpt":0.26566623548474666,"score_spread":0.22928836494392812,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4230124334","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061275862,0.0003030813,0.9351598,0.00006657597,0.000032832075,0.00008191468,0.00012681737,0.0013435781,0.0016094232],"genre_scores_gemma":[0.4973723,0.0005594822,0.49828932,0.00013141913,0.00007888642,0.00014206857,0.0006294544,0.00009180679,0.0027052609],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99954396,0.000046100962,0.000026336289,0.00017965057,0.00016691799,0.00003700874],"domain_scores_gemma":[0.99961156,0.000074747855,0.000064416534,0.00012621556,0.0000854149,0.000037625538],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004940717,0.00047970426,0.0010274677,0.00061679573,0.00040298852,0.0006789277,0.0011313725,0.0005664218,0.00082457135],"category_scores_gemma":[0.001150683,0.00045452427,0.0003719334,0.00048610131,0.00026112358,0.0013380954,0.0007429913,0.00048426734,0.0005592331],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00089192716,0.0003363895,0.009292712,0.00023112632,0.00017038693,0.00047474183,0.00040592183,0.024328459,0.19512802,0.003808742,0.0031427753,0.7617888],"study_design_scores_gemma":[0.00015447092,0.0010718695,0.028703518,0.00006182288,0.00031609045,0.0045368397,0.000350339,0.745762,0.2010232,0.003293424,0.014580412,0.00014607004],"about_ca_topic_score_codex":0.0032248148,"about_ca_topic_score_gemma":0.004414927,"teacher_disagreement_score":0.0032248148,"about_ca_system_score_codex":0.00032236706,"about_ca_system_score_gemma":0.00051028735,"threshold_uncertainty_score":0.006412089},"labels":[],"label_agreement":null},{"id":"W4231869404","doi":"10.1007/978-3-642-21827-9_52","title":"Negotiating Privacy Preferences in Video Surveillance Systems","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Privacy protection; Negotiation; Information privacy; Feature (linguistics); Internet privacy; Privacy software; Computer security","score_opus":0.0426056042202764,"score_gpt":0.27646402656661734,"score_spread":0.23385842234634094,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4231869404","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17763643,0.00086325017,0.8072977,0.0010467876,0.00006007364,0.00013368859,0.00010124776,0.00020755164,0.012653237],"genre_scores_gemma":[0.9527956,0.00028888808,0.043679062,0.000085933534,0.000041717183,0.00006072976,0.0000881533,0.00003865287,0.0029212276],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.9947184,0.0030642122,0.00022811381,0.00053631834,0.0009189301,0.0005340708],"domain_scores_gemma":[0.98565173,0.011760136,0.00065203564,0.0010019284,0.00056211656,0.0003721275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0064613954,0.0005087982,0.00085036433,0.00042698643,0.00080689706,0.0032606914,0.0012926396,0.0014851756,0.0027221607],"category_scores_gemma":[0.022193871,0.00067389116,0.0005322701,0.00076688227,0.0011819848,0.0065365396,0.00214025,0.0023840738,0.0003325082],"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":[0.0025384084,0.0004821752,0.007680847,0.00041062295,0.00023630711,0.00078735425,0.0037362592,0.27436122,0.01681648,0.4001743,0.0048210328,0.28795496],"study_design_scores_gemma":[0.00005530049,0.00021778043,0.001070888,0.000038026865,0.00004532547,0.0002433363,0.0007976971,0.7950781,0.0077381767,0.19162357,0.0030466728,0.000045220335],"about_ca_topic_score_codex":0.0009383181,"about_ca_topic_score_gemma":0.00081436743,"teacher_disagreement_score":0.0064613954,"about_ca_system_score_codex":0.000956293,"about_ca_system_score_gemma":0.0005976708,"threshold_uncertainty_score":0.03417152},"labels":[],"label_agreement":null},{"id":"W4233476686","doi":"10.32920/ryerson.14663142","title":"Real-time data mining for multimedia streaming","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Video tracking; Artificial intelligence; Cluster analysis; C4.5 algorithm; Decision tree; Classifier (UML); Computer vision; Support vector machine; Data mining; Object (grammar); Machine learning","score_opus":0.10119059330128098,"score_gpt":0.3649671927395983,"score_spread":0.2637765994383173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4233476686","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11900048,0.0027131934,0.8593083,0.0008691585,0.00023536928,0.00064514834,0.006277457,0.007902231,0.003048654],"genre_scores_gemma":[0.59979266,0.0017947606,0.3845576,0.0001612465,0.00020897794,0.0006718658,0.010097223,0.00017232273,0.0025433546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988146,0.00014892883,0.00017034193,0.0003478218,0.0004389824,0.00007929686],"domain_scores_gemma":[0.99815327,0.0007523454,0.00022247828,0.00030088853,0.00048082587,0.00009012867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014338434,0.0008599984,0.0008116706,0.0025367276,0.00045738023,0.0013549803,0.001235682,0.0007237735,0.001352769],"category_scores_gemma":[0.0051686503,0.00028806983,0.0006302146,0.0025704755,0.00024471647,0.0014465917,0.000592802,0.0008035662,0.0009851645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071645493,0.0006191209,0.020101726,0.0007105839,0.00021879101,0.0007554099,0.00021134866,0.09098096,0.027494794,0.006084155,0.0113738375,0.84073275],"study_design_scores_gemma":[0.000020341002,0.00012953416,0.0063862437,0.000037165923,0.00002798553,0.00033357303,0.00016104836,0.9650927,0.015242526,0.0066244276,0.0059229946,0.000021470187],"about_ca_topic_score_codex":0.0020879405,"about_ca_topic_score_gemma":0.0020097012,"teacher_disagreement_score":0.0025367276,"about_ca_system_score_codex":0.0005922884,"about_ca_system_score_gemma":0.00060031115,"threshold_uncertainty_score":0.007583022},"labels":[],"label_agreement":null},{"id":"W4236114321","doi":"10.22215/etd/2017-12079","title":"Techniques for Enhancing the Computational Speed of Multiple Object Tracking","year":2017,"lang":"en","type":"dissertation","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Video tracking; Benchmark (surveying); Focus (optics); Overhead (engineering); Artificial intelligence; Speedup; Set (abstract data type); Real-time computing; Object (grammar); Computer vision; Data mining; Parallel computing","score_opus":0.042961802398742574,"score_gpt":0.3597910808866947,"score_spread":0.3168292784879521,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236114321","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":[0.013151514,0.0011982854,0.980646,0.00027894654,0.00021482157,0.00007614645,0.00008098561,0.001859798,0.0024934195],"genre_scores_gemma":[0.13488851,0.0014688977,0.85975754,0.00014760377,0.00019173839,0.0002186937,0.0004399286,0.00030703197,0.0025800448],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886596,0.00015891368,0.000076275326,0.00027977652,0.0005043543,0.00011461875],"domain_scores_gemma":[0.9979558,0.0007832487,0.00016850245,0.0005009092,0.0005203007,0.00007117723],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013685764,0.0011253646,0.0009251551,0.0017529048,0.00096802023,0.001301995,0.0020408256,0.00075916544,0.0024250655],"category_scores_gemma":[0.0050137937,0.00053461664,0.0012467994,0.002869372,0.00059613015,0.0023587605,0.001688163,0.0018488256,0.0012886899],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003049629,0.00025797068,0.00205381,0.00043281098,0.00018643057,0.00012685992,0.00031026916,0.08330185,0.056823708,0.022135828,0.008904298,0.8251612],"study_design_scores_gemma":[0.00008593956,0.00016448776,0.0015761171,0.00004716124,0.000093846094,0.0004347754,0.00012434128,0.90633017,0.042273194,0.025231864,0.023584025,0.00005406362],"about_ca_topic_score_codex":0.0045161764,"about_ca_topic_score_gemma":0.005110232,"teacher_disagreement_score":0.0045161764,"about_ca_system_score_codex":0.0007365405,"about_ca_system_score_gemma":0.0018604854,"threshold_uncertainty_score":0.008979797},"labels":[],"label_agreement":null},{"id":"W4236629533","doi":"10.32920/ryerson.14653467","title":"An enhanced system for augmenting urban search and rescue canines","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Federal Emergency Management Agency","keywords":"Urban search and rescue; Emergency rescue; Situation awareness; Search and rescue; Computer science; Wearable computer; Computer security; Plan (archaeology); Medical emergency; Engineering; Medicine; Robot; Embedded system; Artificial intelligence; Geography","score_opus":0.04623012676997174,"score_gpt":0.3417590874704427,"score_spread":0.29552896070047097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236629533","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.5109329,0.00081234955,0.4333606,0.0005158994,0.0004093191,0.0008355749,0.0016982445,0.028825898,0.022609126],"genre_scores_gemma":[0.77582914,0.00030107066,0.2065332,0.00030129097,0.000118973425,0.0002772559,0.0014963832,0.00018935197,0.014953405],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997943,0.000031506963,0.000011397831,0.000063142936,0.00007415242,0.000025454867],"domain_scores_gemma":[0.99965346,0.000077836434,0.000026489082,0.00008954752,0.00010984992,0.000042848707],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031625567,0.00032590184,0.00037802386,0.00046336377,0.00024171238,0.0004844744,0.000605475,0.00064584403,0.005045641],"category_scores_gemma":[0.0006689704,0.00018494231,0.00019387162,0.00033803243,0.00018840973,0.00088286167,0.00087793707,0.00034057544,0.0012173321],"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":[0.001817962,0.00049062114,0.0086375065,0.00046882706,0.00008469354,0.0010871025,0.00096576835,0.010780763,0.5181032,0.0037238146,0.024451336,0.4293884],"study_design_scores_gemma":[0.0004407036,0.0042655817,0.035703037,0.00013733545,0.0003300198,0.0052192197,0.0006480215,0.57695884,0.19968148,0.0020759266,0.17433758,0.00020222855],"about_ca_topic_score_codex":0.0012542325,"about_ca_topic_score_gemma":0.0012449458,"teacher_disagreement_score":0.005045641,"about_ca_system_score_codex":0.00020314244,"about_ca_system_score_gemma":0.00030767004,"threshold_uncertainty_score":0.01687938},"labels":[],"label_agreement":null},{"id":"W4236649555","doi":"10.1177/0361198106198200125","title":"Development of Bicycle and Pedestrian Detection and Classification Algorithm for Active-Infrared Overhead Vehicle Imaging Sensors","year":2006,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Bombardier (Canada)","funders":"","keywords":"Overhead (engineering); Computer science; Pedestrian; Identification (biology); Field (mathematics); Algorithm; Artificial intelligence; Pedestrian detection; Intelligent transportation system; Computer vision; Engineering; Transport engineering","score_opus":0.08743499007239734,"score_gpt":0.38641565365263847,"score_spread":0.2989806635802411,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4236649555","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045490436,0.00010325481,0.95082545,0.000047264934,0.000039878574,0.00018395421,0.000052414398,0.0013832804,0.0018741536],"genre_scores_gemma":[0.20722419,0.00009513982,0.78917485,0.000056527388,0.000022036604,0.00029828935,0.0002027332,0.000039989078,0.0028862227],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99939096,0.000107484746,0.00004126656,0.0001486205,0.00026119678,0.0000505732],"domain_scores_gemma":[0.99870753,0.0002453463,0.00008962862,0.00007435607,0.0008404335,0.000042696323],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009630986,0.0004937097,0.0005931089,0.0014519838,0.00040348442,0.00063895085,0.00095159403,0.00071669597,0.0013480606],"category_scores_gemma":[0.001935331,0.00032681943,0.0003547893,0.0006603814,0.00027815372,0.00083358644,0.00031832533,0.0004560448,0.0006619498],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037869313,0.00037349924,0.011194991,0.00014856391,0.00009085489,0.00011227119,0.00017650698,0.05215169,0.058230422,0.005821904,0.0035316802,0.867789],"study_design_scores_gemma":[0.000038073278,0.00023465675,0.0047557126,0.00002126152,0.000035923753,0.00018167644,0.000057466023,0.9550652,0.03441519,0.0011542822,0.004004631,0.00003591529],"about_ca_topic_score_codex":0.0044391695,"about_ca_topic_score_gemma":0.0034188693,"teacher_disagreement_score":0.0044391695,"about_ca_system_score_codex":0.0006449153,"about_ca_system_score_gemma":0.00096715416,"threshold_uncertainty_score":0.008826673},"labels":[],"label_agreement":null},{"id":"W4238263146","doi":"10.32920/ryerson.14652873.v1","title":"A real-time pedestrian detection system for safety applications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pedestrian; Computer science; Brake; Pedestrian detection; Warning system; Advanced driver assistance systems; Real-time computing; Artificial intelligence; Computer vision; Engineering; Transport engineering; Automotive engineering; Telecommunications","score_opus":0.02698125875405935,"score_gpt":0.30115140715149763,"score_spread":0.27417014839743825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4238263146","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10389124,0.00102616,0.85428256,0.00027344492,0.00049340166,0.0004696105,0.0008366912,0.031898346,0.0068284934],"genre_scores_gemma":[0.64756364,0.00046880028,0.33109486,0.000268152,0.00017976541,0.00032355118,0.00152911,0.00024341389,0.018328737],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997271,0.00003875698,0.000016440534,0.00007289855,0.00011352572,0.000031269126],"domain_scores_gemma":[0.99966645,0.00003078826,0.000020961417,0.000045645746,0.00019430334,0.00004195037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046220666,0.00055944186,0.00050950266,0.0007137851,0.00038336686,0.00047976803,0.0006957136,0.0008602033,0.005738337],"category_scores_gemma":[0.0005308254,0.00030938842,0.00026292604,0.00045854386,0.0001327687,0.00056142523,0.00035639835,0.0004398962,0.0032236804],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014250823,0.00043398046,0.0033818698,0.0003369448,0.00007235394,0.00047844867,0.00025690554,0.0066918377,0.4619344,0.002103392,0.026308564,0.4965763],"study_design_scores_gemma":[0.00022082019,0.0020390654,0.018677406,0.000076475044,0.00019517417,0.0020409739,0.00011983371,0.5565107,0.34708,0.0012930149,0.071574785,0.00017181963],"about_ca_topic_score_codex":0.0014917218,"about_ca_topic_score_gemma":0.001305343,"teacher_disagreement_score":0.005738337,"about_ca_system_score_codex":0.00029802407,"about_ca_system_score_gemma":0.00046906076,"threshold_uncertainty_score":0.01919669},"labels":[],"label_agreement":null},{"id":"W4240606069","doi":"10.32920/ryerson.14652873","title":"A real-time pedestrian detection system for safety applications","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pedestrian; Brake; Pedestrian detection; Computer science; Warning system; Real-time computing; Advanced driver assistance systems; Artificial intelligence; Computer vision; Engineering; Automotive engineering; Transport engineering; Telecommunications","score_opus":0.02698125875405935,"score_gpt":0.30115140715149763,"score_spread":0.27417014839743825,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4240606069","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10389124,0.00102616,0.85428256,0.00027344492,0.00049340166,0.0004696105,0.0008366912,0.031898346,0.0068284934],"genre_scores_gemma":[0.64756364,0.00046880028,0.33109486,0.000268152,0.00017976541,0.00032355118,0.00152911,0.00024341389,0.018328737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997271,0.00003875698,0.000016440534,0.00007289855,0.00011352572,0.000031269126],"domain_scores_gemma":[0.99966645,0.00003078826,0.000020961417,0.000045645746,0.00019430334,0.00004195037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046220666,0.00055944186,0.00050950266,0.0007137851,0.00038336686,0.00047976803,0.0006957136,0.0008602033,0.005738337],"category_scores_gemma":[0.0005308254,0.00030938842,0.00026292604,0.00045854386,0.0001327687,0.00056142523,0.00035639835,0.0004398962,0.0032236804],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014250823,0.00043398046,0.0033818698,0.0003369448,0.00007235394,0.00047844867,0.00025690554,0.0066918377,0.4619344,0.002103392,0.026308564,0.4965763],"study_design_scores_gemma":[0.00022082019,0.0020390654,0.018677406,0.000076475044,0.00019517417,0.0020409739,0.00011983371,0.5565107,0.34708,0.0012930149,0.071574785,0.00017181963],"about_ca_topic_score_codex":0.0014917218,"about_ca_topic_score_gemma":0.001305343,"teacher_disagreement_score":0.005738337,"about_ca_system_score_codex":0.00029802407,"about_ca_system_score_gemma":0.00046906076,"threshold_uncertainty_score":0.01919669},"labels":[],"label_agreement":null},{"id":"W4242606816","doi":"10.1007/s001380050122","title":"Tracking a person with pre-recorded image database and a pan, tilt, and zoom camera","year":2000,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"IBM (Canada); University of Toronto; York University","funders":"","keywords":"Computer vision; Artificial intelligence; Zoom; Tracking (education); Computer science; Tilt (camera); Video tracking; Segmentation; Image (mathematics); Computer graphics (images); Object (grammar); Mathematics; Engineering","score_opus":0.01240692098803086,"score_gpt":0.29565117662177054,"score_spread":0.28324425563373967,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4242606816","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":[0.58195716,0.0009934558,0.4007604,0.0003165659,0.00024159343,0.00043764964,0.003773294,0.004780689,0.0067392318],"genre_scores_gemma":[0.7966298,0.00081463694,0.18993257,0.00016985869,0.00011535039,0.00013393328,0.0036166797,0.00008347293,0.008503698],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99973565,0.000013652721,0.0000120580235,0.00012752615,0.000079528945,0.000031570235],"domain_scores_gemma":[0.99978346,0.000026938327,0.000024297467,0.000062397754,0.000068660185,0.000034282115],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002588755,0.0005321895,0.0010788065,0.0010927734,0.00049312954,0.00051527773,0.0007287265,0.0009104971,0.0022634345],"category_scores_gemma":[0.00061196875,0.00029330768,0.00035336177,0.0009927918,0.00015691815,0.0006032285,0.00057427934,0.00044809622,0.0015587036],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0026704255,0.0010842007,0.04192554,0.00040156613,0.0003701352,0.0017189371,0.00050011853,0.012144782,0.22556932,0.0012930526,0.0121554425,0.7001666],"study_design_scores_gemma":[0.0001688611,0.0017382716,0.23927185,0.00010143577,0.00054569443,0.011436784,0.0008320311,0.48832983,0.2396706,0.002268481,0.015483772,0.00015229954],"about_ca_topic_score_codex":0.008547328,"about_ca_topic_score_gemma":0.013568001,"teacher_disagreement_score":0.008547328,"about_ca_system_score_codex":0.00027375037,"about_ca_system_score_gemma":0.00044498063,"threshold_uncertainty_score":0.016995192},"labels":[],"label_agreement":null},{"id":"W4246510892","doi":"10.22215/etd/2015-10876","title":"A Robust Approach for Road Users Classification Using Motion Cues","year":2015,"lang":"en","type":"dissertation","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Computer science; Motion (physics); Artificial intelligence; Computer vision; Fuzzy logic; Machine learning","score_opus":0.21935616623888693,"score_gpt":0.37593576026751935,"score_spread":0.15657959402863242,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4246510892","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034370925,0.0004308706,0.96331877,0.000075746415,0.00004093034,0.00008085002,0.00011200297,0.00048904907,0.0010808496],"genre_scores_gemma":[0.58709407,0.0004970566,0.40740034,0.00010469894,0.00013244228,0.00018186272,0.0006504189,0.00008939897,0.0038497772],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990709,0.00011328277,0.00005417161,0.0003033478,0.0003361339,0.00012221541],"domain_scores_gemma":[0.9994778,0.0000969176,0.000081035934,0.00006983057,0.0002461517,0.000028311453],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008525431,0.00070986425,0.0010041087,0.0021532134,0.00042421787,0.0008611415,0.0011317256,0.00100555,0.0009085096],"category_scores_gemma":[0.0018957794,0.00029567987,0.0009712457,0.0009917241,0.00038864737,0.0009974479,0.0007676577,0.00090288307,0.00053543976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032249413,0.00023390823,0.0053154626,0.0001546533,0.0001544611,0.000169549,0.00018800702,0.122117825,0.062612586,0.006319677,0.001932198,0.8004791],"study_design_scores_gemma":[0.000007109828,0.00015048326,0.0044479636,0.000023889745,0.000057973328,0.00010230983,0.000086437605,0.97628003,0.014279409,0.0024177404,0.0021102591,0.00003642133],"about_ca_topic_score_codex":0.0045346334,"about_ca_topic_score_gemma":0.0043394244,"teacher_disagreement_score":0.0045346334,"about_ca_system_score_codex":0.00065592385,"about_ca_system_score_gemma":0.0008109887,"threshold_uncertainty_score":0.009016454},"labels":[],"label_agreement":null},{"id":"W4248928049","doi":"10.32920/ryerson.14663142.v1","title":"Real-time data mining for multimedia streaming","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Video tracking; C4.5 algorithm; Cluster analysis; Artificial intelligence; Decision tree; Classifier (UML); Support vector machine; Data mining; Computer vision; Object (grammar); Machine learning","score_opus":0.10119059330128098,"score_gpt":0.3649671927395983,"score_spread":0.2637765994383173,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4248928049","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11900048,0.0027131934,0.8593083,0.0008691585,0.00023536928,0.00064514834,0.006277457,0.007902231,0.003048654],"genre_scores_gemma":[0.59979266,0.0017947606,0.3845576,0.0001612465,0.00020897794,0.0006718658,0.010097223,0.00017232273,0.0025433546],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988146,0.00014892883,0.00017034193,0.0003478218,0.0004389824,0.00007929686],"domain_scores_gemma":[0.99815327,0.0007523454,0.00022247828,0.00030088853,0.00048082587,0.00009012867],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014338434,0.0008599984,0.0008116706,0.0025367276,0.00045738023,0.0013549803,0.001235682,0.0007237735,0.001352769],"category_scores_gemma":[0.0051686503,0.00028806983,0.0006302146,0.0025704755,0.00024471647,0.0014465917,0.000592802,0.0008035662,0.0009851645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00071645493,0.0006191209,0.020101726,0.0007105839,0.00021879101,0.0007554099,0.00021134866,0.09098096,0.027494794,0.006084155,0.0113738375,0.84073275],"study_design_scores_gemma":[0.000020341002,0.00012953416,0.0063862437,0.000037165923,0.00002798553,0.00033357303,0.00016104836,0.9650927,0.015242526,0.0066244276,0.0059229946,0.000021470187],"about_ca_topic_score_codex":0.0020879405,"about_ca_topic_score_gemma":0.0020097012,"teacher_disagreement_score":0.0025367276,"about_ca_system_score_codex":0.0005922884,"about_ca_system_score_gemma":0.00060031115,"threshold_uncertainty_score":0.007583022},"labels":[],"label_agreement":null},{"id":"W4251300606","doi":"10.22360/springsim.2019.cns.002","title":"Scalable Pattern Recognition and Real Time Tracking of Moving Objects","year":2019,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Artificial intelligence; Computer science; Dynamic time warping; Computer vision; Pattern recognition (psychology); Cluster analysis; Tracking (education); Naive Bayes classifier; Video tracking; Mean-shift; Match moving; Cognitive neuroscience of visual object recognition; Motion (physics); Object (grammar); Support vector machine","score_opus":0.028430690492031178,"score_gpt":0.26066769563909653,"score_spread":0.23223700514706536,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4251300606","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010540248,0.00044595968,0.9869086,0.000065529006,0.000067571294,0.00004623024,0.000068296635,0.0011012112,0.0007562218],"genre_scores_gemma":[0.24515365,0.00078151695,0.7495402,0.000114377566,0.000097322365,0.00013037388,0.0006045292,0.00010630742,0.0034717612],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99901307,0.00008883506,0.00007400903,0.00038693863,0.00037653555,0.000060574646],"domain_scores_gemma":[0.99933136,0.00015150083,0.000083952604,0.00016529397,0.00023727064,0.000030596875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007997661,0.00053754455,0.00091644685,0.0013753813,0.00034866843,0.0008911751,0.0009946186,0.0006798328,0.0007905252],"category_scores_gemma":[0.0017995259,0.00034981823,0.0006797393,0.0013793436,0.00047640328,0.0013237888,0.0005283937,0.00060203124,0.0005078827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012917862,0.00009149118,0.0023413948,0.00018055215,0.0001119277,0.000246004,0.00015446242,0.060704984,0.092615865,0.008936621,0.002973112,0.8315145],"study_design_scores_gemma":[0.000021470949,0.00013870363,0.0053102886,0.000027060383,0.000059280257,0.00045854758,0.00007567135,0.9376643,0.035733562,0.0104121035,0.01005903,0.000039980336],"about_ca_topic_score_codex":0.005210359,"about_ca_topic_score_gemma":0.0048064305,"teacher_disagreement_score":0.005210359,"about_ca_system_score_codex":0.0005716752,"about_ca_system_score_gemma":0.0005715649,"threshold_uncertainty_score":0.010360062},"labels":[],"label_agreement":null},{"id":"W4252233008","doi":"10.1109/icpr.2004.1333859","title":"Robust KLT tracking with Gaussian and Laplacian of Gaussian weighting functions","year":2004,"lang":"en","type":"article","venue":"Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004.","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Weighting; Artificial intelligence; Tracking (education); Noise (video); Blob detection; Computer science; Feature (linguistics); Gaussian; Laplace operator; Function (biology); Object (grammar); Computer vision; Gaussian noise; Video tracking; Pattern recognition (psychology); Object detection; Image (mathematics); Feature extraction; Mathematics; Edge detection; Image processing","score_opus":0.062028494618783174,"score_gpt":0.27069540757479993,"score_spread":0.20866691295601675,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4252233008","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":[0.0071495334,0.00009375107,0.99165,0.000051468785,0.00002801145,0.00001619108,0.000018340039,0.0004768026,0.000515927],"genre_scores_gemma":[0.4099123,0.00026231565,0.5850945,0.0002197269,0.000075050695,0.00014379992,0.00026014762,0.0003384846,0.003693609],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975224,0.00041577223,0.0001729178,0.0004841319,0.00121548,0.00018919502],"domain_scores_gemma":[0.9968124,0.00088531704,0.00046149615,0.00066270767,0.0010481884,0.00012986407],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003226538,0.0008745018,0.0010718527,0.0010750409,0.00041257267,0.001945203,0.0015161183,0.0018521812,0.0010340741],"category_scores_gemma":[0.013169495,0.0005098313,0.0007964741,0.0014380292,0.0008861198,0.002998828,0.0020932397,0.0011150877,0.0009897918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045243304,0.00020472248,0.0021612714,0.00019544277,0.00013152715,0.00016562322,0.00021071968,0.37866533,0.066813976,0.028562577,0.0033480797,0.51908827],"study_design_scores_gemma":[0.00002062318,0.00007851321,0.0004127446,0.000007820737,0.000015591455,0.00009759606,0.000011789504,0.97802275,0.0143392505,0.005840056,0.0011173321,0.000035949226],"about_ca_topic_score_codex":0.0026926128,"about_ca_topic_score_gemma":0.002639238,"teacher_disagreement_score":0.003226538,"about_ca_system_score_codex":0.0009264263,"about_ca_system_score_gemma":0.0014262294,"threshold_uncertainty_score":0.017063737},"labels":[],"label_agreement":null},{"id":"W4254562804","doi":"10.32920/ryerson.14665893","title":"SoC for real - time object tracking in 3D space","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Alertness; Object (grammar); Computer science; Tracking (education); Video tracking; Warning system; Space (punctuation); Hazardous waste; Work (physics); Simulation; Human–computer interaction; Computer vision; Computer security; Real-time computing; Aeronautics; Artificial intelligence; Engineering; Psychology","score_opus":0.04381131404775677,"score_gpt":0.33987635932931465,"score_spread":0.29606504528155786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4254562804","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024906944,0.00043764335,0.95030516,0.000097590826,0.00021620911,0.00018142622,0.00030950562,0.01537682,0.008168701],"genre_scores_gemma":[0.6081305,0.000656187,0.3730026,0.00039941506,0.00009454879,0.0005221115,0.0009077424,0.000507954,0.015779037],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99954814,0.000043905955,0.000023527873,0.00006760352,0.00025767757,0.00005911044],"domain_scores_gemma":[0.99950254,0.000107872926,0.0000601992,0.00013459579,0.00015504796,0.00003974927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040935766,0.0006770939,0.00044547292,0.00051522243,0.00020603118,0.0009320909,0.0010227594,0.0007865379,0.0076804343],"category_scores_gemma":[0.0009913802,0.0002716776,0.0005063776,0.0003256009,0.00025615122,0.00070577743,0.000821166,0.00045685645,0.0023203923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008940298,0.00032913018,0.0029586568,0.00096428086,0.00024225435,0.0009544829,0.00045779604,0.076200105,0.29444337,0.014498882,0.021118209,0.58693874],"study_design_scores_gemma":[0.00015722463,0.0012753621,0.0036587194,0.0001466371,0.00013512619,0.0012276404,0.00010120658,0.80000716,0.10816391,0.006412214,0.07859882,0.00011598014],"about_ca_topic_score_codex":0.001976582,"about_ca_topic_score_gemma":0.0023867812,"teacher_disagreement_score":0.0076804343,"about_ca_system_score_codex":0.0002779464,"about_ca_system_score_gemma":0.0005218417,"threshold_uncertainty_score":0.025693655},"labels":[],"label_agreement":null},{"id":"W4255802862","doi":"10.1007/978-3-540-24671-8_37","title":"Adaptive Probabilistic Visual Tracking with Incremental Subspace Update","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Probabilistic logic; Computer vision; Subspace topology; Tracking (education); A priori and a posteriori; Prior probability; Affine transformation; Video tracking; Object (grammar); Bayesian probability; Mathematics","score_opus":0.024209143394415305,"score_gpt":0.277435982169378,"score_spread":0.25322683877496266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4255802862","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0018473162,0.00011956728,0.9969313,0.000022797823,0.00002632257,0.000011341815,0.000017619617,0.00038421762,0.0006394924],"genre_scores_gemma":[0.16982992,0.00047971084,0.82287127,0.00012350497,0.00009496169,0.000118278134,0.00030802903,0.00019388892,0.005980392],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99923754,0.000123779,0.000029050885,0.00018650379,0.00037318142,0.000049956172],"domain_scores_gemma":[0.9990551,0.00035212407,0.000070217655,0.00023731726,0.00025419836,0.000031085998],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00083516707,0.00072052586,0.0009746213,0.0008043779,0.0003063704,0.0008460153,0.0017167332,0.0008495087,0.002275275],"category_scores_gemma":[0.0033149803,0.0007720238,0.000683207,0.0014376047,0.0005219648,0.0015585895,0.0016774804,0.0010791492,0.0013299551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015675562,0.00007274561,0.00044335605,0.00009671627,0.00009108565,0.00004720574,0.000061024133,0.14189565,0.0192651,0.00854251,0.0036656708,0.8256622],"study_design_scores_gemma":[0.000010983451,0.000036060468,0.00031570165,0.0000054146517,0.000021454898,0.00011092151,0.0000053066724,0.9862499,0.005668165,0.0055469093,0.0020135876,0.000015521799],"about_ca_topic_score_codex":0.002924669,"about_ca_topic_score_gemma":0.0035886972,"teacher_disagreement_score":0.002924669,"about_ca_system_score_codex":0.0003439111,"about_ca_system_score_gemma":0.0004958121,"threshold_uncertainty_score":0.0076115727},"labels":[],"label_agreement":null},{"id":"W4256706427","doi":"10.32920/14637105.v1","title":"Multi-View Human Activity Recognition in Distributed Camera Sensor Networks","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Activity recognition; Smart camera; Artificial intelligence; Computer vision; Wireless sensor network; Distributed computing; Real-time computing; Computer network","score_opus":0.08975735004040966,"score_gpt":0.3459077300458746,"score_spread":0.256150380005465,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4256706427","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07106405,0.000635754,0.9262664,0.0002345572,0.00006080479,0.000058035,0.00010878284,0.0006579072,0.0009137305],"genre_scores_gemma":[0.88213044,0.00040649297,0.114962146,0.000101108286,0.0001132263,0.00010135552,0.0004360844,0.00003871999,0.0017104212],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991345,0.0002502439,0.00003235996,0.0003138866,0.00018326697,0.00008572937],"domain_scores_gemma":[0.99909747,0.0003827549,0.00015347384,0.00013042743,0.00015674162,0.00007916168],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010322724,0.0007388671,0.0011507571,0.00067735626,0.00028947173,0.0005731084,0.0012308517,0.0006878935,0.00049027824],"category_scores_gemma":[0.0023552845,0.00039441374,0.0004844915,0.000811623,0.00054144184,0.0010700455,0.0006857452,0.00086246687,0.00021408667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039387424,0.00021786304,0.002443427,0.00008127991,0.000087194305,0.00017341286,0.00011431902,0.78051287,0.008609233,0.002928925,0.0020974705,0.20234014],"study_design_scores_gemma":[0.000005895306,0.000015492727,0.00044265916,0.0000014885568,0.000002213117,0.000019494657,0.000012960481,0.9976407,0.00063498254,0.0010947256,0.00012686376,0.0000025475326],"about_ca_topic_score_codex":0.006577042,"about_ca_topic_score_gemma":0.0048152334,"teacher_disagreement_score":0.006577042,"about_ca_system_score_codex":0.0005963299,"about_ca_system_score_gemma":0.00043235876,"threshold_uncertainty_score":0.0130774975},"labels":[],"label_agreement":null},{"id":"W4283456084","doi":"10.1145/3530190.3534828","title":"Note: Image-based Prediction of House Attributes with Deep Learning","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Convolutional neural network; Deep learning; Artificial intelligence; Task (project management); Scale (ratio); Mean squared error; Image (mathematics); Artificial neural network; Machine learning; Pattern recognition (psychology); Cartography; Statistics; Geography; Engineering; Mathematics","score_opus":0.019105259143806612,"score_gpt":0.25408157672922693,"score_spread":0.2349763175854203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283456084","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.25758177,0.0028165502,0.43210757,0.0030764176,0.0018195093,0.0006277014,0.21107744,0.06140408,0.029488888],"genre_scores_gemma":[0.56898993,0.0006915439,0.20296757,0.0005587266,0.00020321642,0.00024746027,0.20245595,0.0007900067,0.023095649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980813,0.000012480025,0.0000054718507,0.000082249564,0.000051965602,0.00003969428],"domain_scores_gemma":[0.99976784,0.00003349914,0.000013397514,0.000078801655,0.00008352279,0.000022957207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022190629,0.0012656081,0.00041479032,0.0005215973,0.00028017303,0.00054590014,0.0011596108,0.00065338484,0.008301316],"category_scores_gemma":[0.0009953092,0.0002791875,0.00049261295,0.0006192308,0.00023020714,0.00063086726,0.00056601403,0.0010635575,0.0037519492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008182442,0.00056114735,0.017212061,0.00025635495,0.00025068963,0.00025110474,0.000051852163,0.12776351,0.022636486,0.0028925515,0.40754083,0.4197651],"study_design_scores_gemma":[0.000079338744,0.00010499665,0.007697021,0.000024283201,0.00003278487,0.000116112795,0.000030966472,0.94503725,0.021047687,0.0028720056,0.022925954,0.00003164916],"about_ca_topic_score_codex":0.064826876,"about_ca_topic_score_gemma":0.12892729,"teacher_disagreement_score":0.064826876,"about_ca_system_score_codex":0.0007816465,"about_ca_system_score_gemma":0.00093752856,"threshold_uncertainty_score":0.1288991},"labels":[],"label_agreement":null},{"id":"W4283729703","doi":"10.1109/sera54885.2022.9806761","title":"Predicting Episodic Video Memorability using Deep Features Fusion Strategy","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Episodic memory; Artificial intelligence; Histogram; Fuse (electrical); Set (abstract data type); Pattern recognition (psychology); Term (time); Image (mathematics); Cognition","score_opus":0.038341833654658285,"score_gpt":0.3061822953502328,"score_spread":0.2678404616955745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283729703","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":[0.70983875,0.0021356905,0.27985388,0.00040556124,0.00014716657,0.00014345822,0.001764892,0.002563726,0.0031468857],"genre_scores_gemma":[0.9745155,0.00028468744,0.021150628,0.000067726774,0.00004823446,0.000042874562,0.0014750338,0.000029107403,0.0023863176],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997745,0.000020074434,0.000013676174,0.00008932293,0.00005259715,0.00004981708],"domain_scores_gemma":[0.9996074,0.00013442952,0.00007177285,0.000044831573,0.000103920895,0.000037738795],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003804365,0.00080212177,0.00060911826,0.001199445,0.00017244079,0.0005218964,0.0007816491,0.0005581644,0.0012867998],"category_scores_gemma":[0.0016439267,0.00018760038,0.0006111664,0.00058843597,0.00019898248,0.0010413374,0.0006428444,0.00073164486,0.00035703107],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00085521344,0.00052632776,0.022952743,0.00013655516,0.0002310463,0.00034056327,0.00010631084,0.15295368,0.027506014,0.001115591,0.0061601126,0.7871158],"study_design_scores_gemma":[0.00001087049,0.0001751561,0.005828565,0.0000105323525,0.000050127197,0.00007003856,0.000023894389,0.9850166,0.0074264994,0.0008939075,0.00048267603,0.0000111808895],"about_ca_topic_score_codex":0.006019209,"about_ca_topic_score_gemma":0.006406771,"teacher_disagreement_score":0.006019209,"about_ca_system_score_codex":0.00052310433,"about_ca_system_score_gemma":0.00036700058,"threshold_uncertainty_score":0.011968315},"labels":[],"label_agreement":null},{"id":"W4283754581","doi":"10.1016/j.eswa.2022.117947","title":"ISAIR: Deep inpainted semantic aware image representation for background subtraction","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Representation (politics); Artificial intelligence; Background subtraction; Image (mathematics); Subtraction; Computer vision; Pattern recognition (psychology); Natural language processing; Mathematics; Pixel; Arithmetic","score_opus":0.037492488970715644,"score_gpt":0.33546993028753264,"score_spread":0.29797744131681697,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283754581","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":[0.008654096,0.000515973,0.97479475,0.00011764076,0.00014717596,0.00007791807,0.0008712643,0.013169658,0.0016514632],"genre_scores_gemma":[0.10439258,0.0006089687,0.87952363,0.00037835733,0.00012599216,0.00013798723,0.004318608,0.0010370433,0.009476835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967504,0.00003211319,0.000010076513,0.00008400407,0.00013682581,0.000061958264],"domain_scores_gemma":[0.99982244,0.000032462693,0.0000151822005,0.000051415464,0.000057379053,0.000021286307],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045394388,0.0014986685,0.0011379838,0.001134559,0.0003218625,0.00093504676,0.0019745163,0.0010246802,0.006638971],"category_scores_gemma":[0.000649032,0.00050691684,0.0009912079,0.00095282606,0.00026430713,0.0009039763,0.0012285191,0.001523045,0.0033786881],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055776915,0.0002962178,0.00039112088,0.00017112965,0.00013335003,0.00012578252,0.00005043534,0.025447296,0.08275933,0.0035982386,0.026889797,0.85957956],"study_design_scores_gemma":[0.000051597453,0.00019393208,0.00088010036,0.000025187728,0.00005972318,0.00025081504,0.000028699305,0.9124938,0.0674977,0.0048247925,0.013665259,0.000028356324],"about_ca_topic_score_codex":0.004488427,"about_ca_topic_score_gemma":0.008175919,"teacher_disagreement_score":0.006638971,"about_ca_system_score_codex":0.00046636906,"about_ca_system_score_gemma":0.00079815934,"threshold_uncertainty_score":0.022209585},"labels":[],"label_agreement":null},{"id":"W4283820844","doi":"10.1609/aaai.v36i1.20001","title":"Towards Versatile Pedestrian Detector with Multisensory-Matching and Multispectral Recalling Memory","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Defense Acquisition Program Administration","keywords":"Multispectral image; Pedestrian detection; Computer science; Modality (human–computer interaction); Detector; Computer vision; Artificial intelligence; Modal; Modalities; Pedestrian; Engineering; Telecommunications","score_opus":0.0715672072654312,"score_gpt":0.2957103954905862,"score_spread":0.224143188225155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4283820844","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":[0.042587098,0.00047969553,0.9540205,0.00011005983,0.00008773103,0.000067361114,0.00007707758,0.0013765624,0.0011938055],"genre_scores_gemma":[0.39570528,0.0006175167,0.5971595,0.0005451292,0.000121689875,0.00013360125,0.00041475712,0.00015239781,0.0051501486],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994618,0.000079116566,0.00002891441,0.00016648776,0.00018965075,0.00007397734],"domain_scores_gemma":[0.9993241,0.00011671642,0.00009753333,0.00018395337,0.00020377652,0.00007384226],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010324921,0.0009112917,0.0011873381,0.000978605,0.00033390976,0.0009110369,0.0018634533,0.0011421987,0.0015099023],"category_scores_gemma":[0.001708535,0.00047867355,0.00088435906,0.0008210662,0.0005596429,0.0020241358,0.0018602588,0.0009193163,0.0011420192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009573738,0.00031265998,0.0023826158,0.00025190157,0.00014684632,0.0002690069,0.00013861188,0.023739913,0.3412193,0.007974547,0.0037926522,0.6188145],"study_design_scores_gemma":[0.000050136674,0.0005243819,0.0019941586,0.00002966157,0.00013816841,0.0011182796,0.00005277827,0.7264596,0.25741217,0.004956268,0.007178615,0.00008580356],"about_ca_topic_score_codex":0.00076860195,"about_ca_topic_score_gemma":0.0009017551,"teacher_disagreement_score":0.0018634533,"about_ca_system_score_codex":0.00042714927,"about_ca_system_score_gemma":0.0005522494,"threshold_uncertainty_score":0.0054603815},"labels":[],"label_agreement":null},{"id":"W4285040075","doi":"10.1049/itr2.12236","title":"Non‐instinct detection of cellphone usage from lane‐keeping performance based on eXtreme gradient boosting and optimal sliding windows","year":2022,"lang":"en","type":"article","venue":"IET Intelligent Transport Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ministry of Education and Child Care","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Instinct; Boosting (machine learning); Computer science; Artificial intelligence; Gradient boosting; Real-time computing; Computer vision; Random forest; Biology","score_opus":0.04015665081695539,"score_gpt":0.23675140900130165,"score_spread":0.19659475818434627,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4285040075","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":[0.6220435,0.00028937816,0.3755591,0.00008053049,0.00006549081,0.00006153706,0.00007007989,0.00072603585,0.0011044019],"genre_scores_gemma":[0.98055786,0.000036134057,0.018863093,0.000019166637,0.000010431648,0.000017083861,0.000047757672,0.000011746392,0.00043670496],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.999589,0.000088673936,0.000020135127,0.00010367587,0.00011332367,0.00008515799],"domain_scores_gemma":[0.9994374,0.00021453352,0.00007404555,0.000044414053,0.00017775602,0.000051844236],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007800984,0.0004296606,0.00061552844,0.0006752808,0.0001780306,0.0003764882,0.00051877083,0.00030166112,0.00039910962],"category_scores_gemma":[0.0013875457,0.00017794372,0.0003739216,0.00032167503,0.00020252704,0.00032372988,0.00029211774,0.00036398857,0.00013765504],"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":[0.0012490351,0.00087722886,0.05338154,0.00015049825,0.00030067534,0.0002713742,0.00020603441,0.30261368,0.087681964,0.0015414667,0.002126011,0.5496005],"study_design_scores_gemma":[0.000006251355,0.00011641755,0.010003408,0.000004055609,0.000018459023,0.000037012927,0.000013411345,0.9833709,0.00603327,0.00020261588,0.00018403927,0.0000102537415],"about_ca_topic_score_codex":0.0024369922,"about_ca_topic_score_gemma":0.0015894762,"teacher_disagreement_score":0.0024369922,"about_ca_system_score_codex":0.00026586957,"about_ca_system_score_gemma":0.00033448866,"threshold_uncertainty_score":0.004845619},"labels":[],"label_agreement":null},{"id":"W4286580841","doi":"10.1109/lra.2022.3193246","title":"Reliable, Robust, Accurate and Real-Time 2D LiDAR Human Tracking in Cluttered Environment: A Social Dynamic Filtering Approach","year":2022,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Prince Edward Island","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Lidar; Computer vision; Artificial intelligence; Robustness (evolution); Video tracking; Tracking (education); Usability; Object (grammar); Human–computer interaction; Geography","score_opus":0.0260939825446983,"score_gpt":0.2631230884860992,"score_spread":0.23702910594140092,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286580841","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025687497,0.00019422939,0.9722578,0.0001290085,0.00003540872,0.00003760446,0.000042225674,0.000428644,0.0011875825],"genre_scores_gemma":[0.6168534,0.00044826197,0.37593997,0.00025043078,0.00022466575,0.00013450257,0.00043709177,0.00015338075,0.005558374],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9976063,0.00033085974,0.000115930234,0.00070615794,0.0009565011,0.00028428258],"domain_scores_gemma":[0.99805367,0.0006070008,0.00024722234,0.0002644504,0.0006919069,0.00013576387],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001818743,0.00091701857,0.0013618201,0.0028922434,0.0012536305,0.0014506793,0.0019103879,0.0012826353,0.0008954159],"category_scores_gemma":[0.0038760318,0.00045028812,0.0013654962,0.0017752816,0.00079736643,0.0018414906,0.0015253273,0.00076318684,0.00052326766],"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":[0.00033179717,0.00033083884,0.010120826,0.00017200036,0.00024178387,0.00038525576,0.00078066875,0.19235887,0.03205234,0.014373142,0.003361985,0.7454905],"study_design_scores_gemma":[0.0000057403854,0.000037942664,0.0016917601,0.000005546993,0.000027120355,0.00007368597,0.000048203296,0.991577,0.003294041,0.0019909195,0.0012278466,0.000020249929],"about_ca_topic_score_codex":0.020404523,"about_ca_topic_score_gemma":0.01648997,"teacher_disagreement_score":0.020404523,"about_ca_system_score_codex":0.0015161666,"about_ca_system_score_gemma":0.0013882555,"threshold_uncertainty_score":0.04057151},"labels":[],"label_agreement":null},{"id":"W4286750520","doi":"10.11159/mvml22.108","title":"LPYOLO: Low Precision YOLO for Face Detection on FPGA","year":2022,"lang":"en","type":"article","venue":"Proceedings of the World Congress on Electrical Engineering and Computer Systems and Science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Field-programmable gate array; Multithreading; Convolutional neural network; Embedded system; Edge device; Face detection; Deep learning; Object detection; Computer hardware; Facial recognition system; Artificial intelligence; Feature extraction; Pattern recognition (psychology); Operating system","score_opus":0.010277420926190885,"score_gpt":0.23218355576275418,"score_spread":0.2219061348365633,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286750520","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08490096,0.0051521338,0.61021817,0.0006453112,0.0007967215,0.00094261684,0.005895644,0.16349372,0.12795469],"genre_scores_gemma":[0.572034,0.0015383454,0.29575586,0.00112101,0.00011909959,0.0010951036,0.010074122,0.004262195,0.114000246],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997869,0.000019062383,0.000013179771,0.000051023024,0.000085013846,0.00004481662],"domain_scores_gemma":[0.99987054,0.000026037396,0.000016384647,0.000026398315,0.000049765116,0.000010818906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002176546,0.00085321715,0.0003146155,0.00061124243,0.00022486894,0.0006738391,0.001367721,0.0003491995,0.04460272],"category_scores_gemma":[0.0006082298,0.00032728742,0.00024238393,0.00034229946,0.00016269885,0.0006351858,0.0005003862,0.00053802074,0.008674309],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0019234966,0.00018509186,0.003619904,0.0015131148,0.00011826598,0.00070607726,0.000365128,0.01469716,0.1102504,0.015544022,0.19390042,0.6571769],"study_design_scores_gemma":[0.0006139419,0.0012496747,0.0050674323,0.00040973828,0.00012447296,0.0015222653,0.00016520573,0.2400508,0.257564,0.004484073,0.48857278,0.00017560735],"about_ca_topic_score_codex":0.0029691532,"about_ca_topic_score_gemma":0.0041097263,"teacher_disagreement_score":0.04460272,"about_ca_system_score_codex":0.0005927998,"about_ca_system_score_gemma":0.0005383229,"threshold_uncertainty_score":0.14921099},"labels":[],"label_agreement":null},{"id":"W4286897481","doi":"10.48550/arxiv.2110.11284","title":"Multi-Object Tracking and Segmentation with a Space-Time Memory Network","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Segmentation; Association (psychology); Computer vision; Object (grammar); Video tracking; Metric (unit); Tracking (education); Identification (biology); Sequence (biology); Pattern recognition (psychology)","score_opus":0.06557596250858469,"score_gpt":0.21212192952845016,"score_spread":0.14654596701986547,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286897481","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010356445,0.00012491137,0.9881342,0.00006535179,0.000028360071,0.000021987755,0.0000304204,0.0005600286,0.0006783323],"genre_scores_gemma":[0.34261596,0.00021705386,0.6511474,0.00015756226,0.000103904094,0.00016802111,0.00024063612,0.00014782826,0.0052016303],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992931,0.00010501102,0.00003235979,0.00030555154,0.00018273789,0.00008114414],"domain_scores_gemma":[0.9992506,0.00020132428,0.00013294508,0.00023201825,0.00012252449,0.0000604988],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008401918,0.0006355418,0.0007983518,0.0009921078,0.0005835181,0.0011804276,0.0020057198,0.0013009945,0.001303744],"category_scores_gemma":[0.0019471378,0.0004652807,0.00056159485,0.0011702807,0.0006574512,0.002223463,0.0017318907,0.0010818297,0.0005842967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005947852,0.00018292217,0.0025489437,0.00011437963,0.00013088252,0.00022833,0.00031756202,0.24414414,0.046315745,0.029388884,0.003030952,0.6730025],"study_design_scores_gemma":[0.00001318967,0.00007870945,0.00044824847,0.000007044386,0.000017473209,0.00009956204,0.000015470723,0.9795066,0.011931267,0.0053459913,0.0025219412,0.000014605428],"about_ca_topic_score_codex":0.003224096,"about_ca_topic_score_gemma":0.0032522175,"teacher_disagreement_score":0.003224096,"about_ca_system_score_codex":0.0009360642,"about_ca_system_score_gemma":0.00080818444,"threshold_uncertainty_score":0.0067916512},"labels":[],"label_agreement":null},{"id":"W4286984751","doi":"10.48550/arxiv.2109.08672","title":"Monitoring Indoor Activity of Daily Living Using Thermal Imaging: A Case\\n Study","year":2021,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Activities of daily living; Computer science; Assisted living; Identification (biology); Internet of Things; Real-time computing; Dependency (UML); Field (mathematics); Artificial intelligence; Human–computer interaction; Computer vision; Internet privacy; Psychology; Gerontology; Mathematics; Ecology; Medicine","score_opus":0.16006235112078765,"score_gpt":0.2657904172904256,"score_spread":0.10572806616963792,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4286984751","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":[0.98081595,0.001709072,0.011643361,0.0005217991,0.00006310058,0.00022821162,0.00033362574,0.000048622845,0.0046362323],"genre_scores_gemma":[0.9884211,0.0017230355,0.007391474,0.0001548459,0.00008292075,0.00010720084,0.00021129812,0.000012629749,0.0018954619],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9993761,0.00022327786,0.00004833648,0.0001201174,0.00014707718,0.00008500071],"domain_scores_gemma":[0.99881685,0.000547107,0.0001382887,0.00014316001,0.00021072057,0.00014391192],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006282805,0.00043869767,0.00039170092,0.0008689247,0.0005813069,0.0008795798,0.00056925236,0.0012508252,0.001462424],"category_scores_gemma":[0.0020251628,0.00018838925,0.00057685276,0.0008225306,0.0006357545,0.0005248164,0.0005947387,0.0005274573,0.00034221204],"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":[0.002404472,0.010429823,0.55908453,0.0019110132,0.000621583,0.120752595,0.008649431,0.015159287,0.02577002,0.0062577836,0.010843071,0.23811635],"study_design_scores_gemma":[0.00041719832,0.0092337895,0.57061905,0.00069546385,0.0008750971,0.17507927,0.026189324,0.12845911,0.034795195,0.004655147,0.048595246,0.00038623414],"about_ca_topic_score_codex":0.007095616,"about_ca_topic_score_gemma":0.008228514,"teacher_disagreement_score":0.007095616,"about_ca_system_score_codex":0.0005103576,"about_ca_system_score_gemma":0.00030988702,"threshold_uncertainty_score":0.014108598},"labels":[],"label_agreement":null},{"id":"W4287640148","doi":"10.48550/arxiv.2010.07881","title":"An Empirical Analysis of Visual Features for Multiple Object Tracking in\\n Urban Scenes","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Discriminative model; Artificial intelligence; Bhattacharyya distance; Computer science; Pattern recognition (psychology); Convolutional neural network; Histogram; Similarity (geometry); Detector; Computer vision; Identification (biology); Object (grammar); Bounding overwatch; Image (mathematics)","score_opus":0.13068161988085344,"score_gpt":0.30516213158353095,"score_spread":0.1744805117026775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4287640148","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":[0.96921813,0.0021985748,0.024699502,0.00042356155,0.00006987649,0.000053850676,0.0015875244,0.0003307139,0.0014182387],"genre_scores_gemma":[0.99464417,0.00017923955,0.0025024668,0.000025868367,0.000025222227,0.000014273151,0.0022084333,0.000028663671,0.00037160626],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.99822146,0.0005600676,0.000102213606,0.00053319585,0.00038231042,0.00020068222],"domain_scores_gemma":[0.9813368,0.013194188,0.0017306067,0.002005698,0.0012051868,0.0005276207],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046211104,0.00058486284,0.0005826864,0.0022286484,0.0006321679,0.0011474737,0.00088513206,0.000939008,0.0013412482],"category_scores_gemma":[0.02233176,0.00023406195,0.0006188759,0.0016570939,0.0011300397,0.0017153657,0.00073267927,0.0011847521,0.0004070928],"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":[0.0015349997,0.0005664605,0.658012,0.0004303043,0.00064623414,0.0005986352,0.0003010787,0.1208803,0.0037967595,0.0031684914,0.012856392,0.19720834],"study_design_scores_gemma":[0.00003546516,0.00030530762,0.26860204,0.000074998025,0.00011686858,0.0011112519,0.0003016251,0.7203527,0.0029276444,0.003996322,0.0021196492,0.000056096942],"about_ca_topic_score_codex":0.0052672992,"about_ca_topic_score_gemma":0.005894323,"teacher_disagreement_score":0.0052672992,"about_ca_system_score_codex":0.0010779481,"about_ca_system_score_gemma":0.0004431591,"threshold_uncertainty_score":0.024439037},"labels":[],"label_agreement":null},{"id":"W4288070419","doi":"10.18280/ts.390323","title":"Artificial Intelligence Based Social Distance Monitoring in Public Areas","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Social distance; Computer science; Scope (computer science); Perspective (graphical); Artificial intelligence; Coronavirus disease 2019 (COVID-19); Computer vision; Infectious disease (medical specialty); Disease","score_opus":0.09037103933049005,"score_gpt":0.3108529784534568,"score_spread":0.2204819391229667,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288070419","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":[0.85612744,0.0011535111,0.13115898,0.00045318768,0.00013580339,0.00017653163,0.0016304593,0.0012889818,0.007875005],"genre_scores_gemma":[0.96050614,0.00023814161,0.035750642,0.000056324912,0.000041555275,0.000056492052,0.0017320926,0.00001777042,0.0016008937],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941695,0.00011375273,0.00003712658,0.00020566238,0.00014350923,0.00008291638],"domain_scores_gemma":[0.99947673,0.00013116488,0.000112665504,0.00006181744,0.00016679139,0.000050791863],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003995373,0.0005764798,0.00046992901,0.0018030822,0.00042403588,0.00071136456,0.00072288426,0.00064169365,0.0005846014],"category_scores_gemma":[0.0011556157,0.00014817619,0.0003297699,0.0012306267,0.00022545781,0.0009057925,0.0006972207,0.00041479687,0.000298388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058710173,0.0010439933,0.07363302,0.00034510507,0.00021048625,0.0007782843,0.0006154405,0.16238235,0.021432273,0.0036014088,0.009972295,0.7253983],"study_design_scores_gemma":[0.000013713162,0.00018133002,0.04054921,0.000026529486,0.00003706062,0.00019413396,0.0004923746,0.94433427,0.0072189835,0.0025623974,0.0043683834,0.000021684187],"about_ca_topic_score_codex":0.008150889,"about_ca_topic_score_gemma":0.008319897,"teacher_disagreement_score":0.008150889,"about_ca_system_score_codex":0.00063203805,"about_ca_system_score_gemma":0.0003537295,"threshold_uncertainty_score":0.01620686},"labels":[],"label_agreement":null},{"id":"W4288075303","doi":"10.18280/ts.390318","title":"Embedded Implementation of Social Distancing Detector Based on One Stage Convolutional Neural Network Detector","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Detector; Task (project management); Artificial intelligence; Inference; Social distance; Artificial neural network; Deep learning; Machine learning; Real-time computing; Coronavirus disease 2019 (COVID-19); Engineering; Telecommunications","score_opus":0.030469731797315592,"score_gpt":0.29636118879477447,"score_spread":0.26589145699745886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288075303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2007047,0.00091651594,0.7750555,0.0005730987,0.00042234722,0.00022462932,0.0011232301,0.011060371,0.009919592],"genre_scores_gemma":[0.80226064,0.00027647146,0.18080014,0.00034968072,0.00005478841,0.00016613223,0.0019021351,0.00014493904,0.014045001],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973816,0.000019720206,0.000009046806,0.000107441105,0.00005862881,0.00006695288],"domain_scores_gemma":[0.99976164,0.000045804987,0.000019592448,0.000030630315,0.00011942823,0.000023003175],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043359617,0.00081161415,0.0005949824,0.0005305558,0.00033844277,0.0005219939,0.001453069,0.000730519,0.002305077],"category_scores_gemma":[0.0008837749,0.00028374835,0.00036672922,0.00029500495,0.00022717977,0.0006914895,0.00064634345,0.0007694062,0.0009411303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00068591593,0.00065763143,0.0128089925,0.00021083828,0.00028865004,0.00035298587,0.00013220197,0.2066536,0.061892338,0.0052426974,0.017063085,0.69401103],"study_design_scores_gemma":[0.000012353012,0.000049731818,0.0012394958,0.000007356222,0.000023784612,0.000047141213,0.000011383091,0.9849598,0.0118033625,0.0005774581,0.0012563374,0.000011745728],"about_ca_topic_score_codex":0.018343173,"about_ca_topic_score_gemma":0.028622655,"teacher_disagreement_score":0.018343173,"about_ca_system_score_codex":0.0011491205,"about_ca_system_score_gemma":0.0010882843,"threshold_uncertainty_score":0.036472797},"labels":[],"label_agreement":null},{"id":"W4288346518","doi":"10.48550/arxiv.1905.12759","title":"Distant Pedestrian Detection in the Wild using Single Shot Detector with\\n Deep Convolutional Generative Adversarial Networks","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Pedestrian detection; Detector; Computer science; Object detection; Artificial intelligence; Generative adversarial network; Single shot; Convolutional neural network; Deep learning; Generative grammar; Set (abstract data type); Training set; Computer vision; Object (grammar); One shot; Shot (pellet); Generative model; Pedestrian; Pattern recognition (psychology); Adversarial system; Engineering; Telecommunications; Optics","score_opus":0.12154489916371704,"score_gpt":0.22027363970049982,"score_spread":0.09872874053678278,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4288346518","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08382189,0.0005353895,0.90842843,0.0003177707,0.00018306932,0.000074947086,0.00022717712,0.002828812,0.0035825446],"genre_scores_gemma":[0.80814373,0.00027767025,0.18350439,0.00043630783,0.00008253412,0.000049033682,0.00087206956,0.00013679035,0.006497442],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99924576,0.00018118088,0.00001767997,0.0002966124,0.00013969536,0.00011910392],"domain_scores_gemma":[0.99935466,0.00028640052,0.000047293488,0.00016928908,0.00008673637,0.00005561396],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001533777,0.0012253765,0.0009808985,0.00074334006,0.00032811752,0.00080881314,0.0013604765,0.0009961749,0.0014542865],"category_scores_gemma":[0.0017363143,0.000546596,0.0008668289,0.0003917819,0.00073170214,0.0013518068,0.0016280736,0.0013436448,0.00080670643],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006248138,0.00036101197,0.0048334687,0.00011899707,0.000309393,0.0003807739,0.00014047984,0.64146507,0.028251942,0.008702031,0.0053816177,0.30943045],"study_design_scores_gemma":[0.000003918119,0.00003943526,0.00031089224,0.0000051567135,0.000008854417,0.000049689083,0.000007373314,0.9934412,0.004219872,0.001460529,0.00044704505,0.0000059451136],"about_ca_topic_score_codex":0.0044351714,"about_ca_topic_score_gemma":0.0062374836,"teacher_disagreement_score":0.0044351714,"about_ca_system_score_codex":0.00089040323,"about_ca_system_score_gemma":0.00066176907,"threshold_uncertainty_score":0.008818746},"labels":[],"label_agreement":null},{"id":"W4289709938","doi":"10.48550/arxiv.1808.01066","title":"Online Illumination Invariant Moving Object Detection by Generative\\n Neural Network","year":2018,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Invariant (physics); Benchmark (surveying); Computer vision; Representation (politics); Batch processing; Generative model; Pattern recognition (psychology); Image (mathematics); Generative grammar; Mathematics","score_opus":0.06756382013493388,"score_gpt":0.22017247818555813,"score_spread":0.15260865805062424,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4289709938","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.064188614,0.001262842,0.9258075,0.0002763689,0.00015274946,0.00007362197,0.0001590365,0.0050490513,0.0030303597],"genre_scores_gemma":[0.6660309,0.0006157267,0.3220947,0.00046908134,0.00014161186,0.00010014681,0.0009999779,0.00036578384,0.009182139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99970514,0.000027115811,0.000010183529,0.00013042937,0.000074156706,0.00005301899],"domain_scores_gemma":[0.9996132,0.00015379387,0.000058854737,0.000063100975,0.000082646606,0.000028324042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037825565,0.0010160577,0.0009809484,0.00064249965,0.0003255075,0.00060220173,0.0018757061,0.0009409645,0.0014624122],"category_scores_gemma":[0.0011271954,0.00057910534,0.00075857824,0.0006204004,0.0005329682,0.00089546846,0.00081254623,0.0013217,0.0005196369],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032987588,0.00020592964,0.0015537961,0.00009540947,0.0001437075,0.0001451809,0.00005588741,0.31050158,0.022673715,0.002522636,0.0049935575,0.65677875],"study_design_scores_gemma":[0.00000436235,0.000012706893,0.00022712482,0.0000019439933,0.000007877988,0.000017212853,0.0000021353967,0.9966336,0.0023241625,0.00053177733,0.00023337644,0.000003815894],"about_ca_topic_score_codex":0.012762017,"about_ca_topic_score_gemma":0.01697946,"teacher_disagreement_score":0.012762017,"about_ca_system_score_codex":0.0009480832,"about_ca_system_score_gemma":0.00086961803,"threshold_uncertainty_score":0.025375426},"labels":[],"label_agreement":null},{"id":"W4291910385","doi":"10.1109/tits.2022.3196854","title":"Pedestrian Detection Using MB-CSP Model and Boosted Identity Aware Non-Maximum Suppression","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Pedestrian detection; Pedestrian; Computer science; Artificial intelligence; Computer vision; Task (project management); Visibility; Process (computing); Interference (communication); Identity (music); Pattern recognition (psychology); Machine learning; Engineering; Geography; Transport engineering; Channel (broadcasting)","score_opus":0.049960735542289456,"score_gpt":0.3060570136145349,"score_spread":0.25609627807224544,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4291910385","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029448276,0.00040965085,0.9667495,0.00021702524,0.00007483509,0.000050042065,0.00017181654,0.001452299,0.0014265389],"genre_scores_gemma":[0.71236014,0.00053545187,0.2809747,0.00033237803,0.00020238668,0.0001087988,0.0009556283,0.0002309841,0.004299538],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937075,0.00010152323,0.000021360453,0.00017629823,0.00022653055,0.00010360214],"domain_scores_gemma":[0.9992052,0.00020926197,0.00007503459,0.00011771323,0.0003027527,0.000090023736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000928933,0.001307713,0.0013306545,0.00089229806,0.00043266342,0.00080395455,0.0018346494,0.0008398459,0.0017640927],"category_scores_gemma":[0.0022641562,0.0005684453,0.0010868633,0.0009128137,0.00059923995,0.0013708195,0.0013680389,0.0014408852,0.0009889147],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013132525,0.0003511687,0.0065551414,0.00020573095,0.0001520303,0.0005628218,0.0002550201,0.3633071,0.050327886,0.008234812,0.00817477,0.56056035],"study_design_scores_gemma":[0.0000065350464,0.000043147807,0.00040554474,0.0000039967413,0.00001129576,0.000060545422,0.000007726936,0.9937138,0.0037022554,0.0014970346,0.00054179766,0.0000063606976],"about_ca_topic_score_codex":0.009067248,"about_ca_topic_score_gemma":0.008071358,"teacher_disagreement_score":0.009067248,"about_ca_system_score_codex":0.00054454064,"about_ca_system_score_gemma":0.0012288059,"threshold_uncertainty_score":0.018028915},"labels":[],"label_agreement":null},{"id":"W4292120377","doi":"10.1007/s11760-022-02319-8","title":"Transnational image object detection datasets from nighttime driving","year":2022,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Object (grammar); Motion blur; Computer vision; Image (mathematics); Deep learning; Pattern recognition (psychology); Remote sensing; Geography","score_opus":0.01401972366640679,"score_gpt":0.27822011538139946,"score_spread":0.26420039171499266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292120377","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":[0.587577,0.00129966,0.011915059,0.0004161713,0.00052448723,0.00045426056,0.37859237,0.00789134,0.011329663],"genre_scores_gemma":[0.18674585,0.0003208893,0.008919983,0.00010673765,0.000066470064,0.00013217221,0.79567844,0.00020900846,0.007820418],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951684,0.000028456163,0.000038880316,0.0001453732,0.0001647706,0.00010569713],"domain_scores_gemma":[0.9993932,0.00003472854,0.00004326458,0.00014067288,0.00030834568,0.00007980936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031290474,0.0011083652,0.00069802144,0.0019140827,0.0006540287,0.00063334266,0.00088981626,0.0009335566,0.0032233265],"category_scores_gemma":[0.0006976005,0.00026686612,0.0007298312,0.0015691492,0.00027049155,0.00038913006,0.00078382407,0.00060210645,0.0050168186],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023170218,0.002298984,0.115384534,0.001767461,0.0010411981,0.0015181393,0.0007521558,0.013450662,0.06883067,0.0012515563,0.4530815,0.33830613],"study_design_scores_gemma":[0.00023938438,0.0007794381,0.5930895,0.00018007903,0.000461856,0.0024505802,0.0017827265,0.07339255,0.058036268,0.00078494335,0.26858288,0.00021975231],"about_ca_topic_score_codex":0.046016265,"about_ca_topic_score_gemma":0.09188241,"teacher_disagreement_score":0.046016265,"about_ca_system_score_codex":0.0004204712,"about_ca_system_score_gemma":0.0009501006,"threshold_uncertainty_score":0.091496885},"labels":[],"label_agreement":null},{"id":"W4292793930","doi":"10.1109/cvprw56347.2022.00042","title":"Multiple Object Detection and Tracking in the Thermal Spectrum","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; BitTorrent tracker; Tracking (education); Pipeline (software); Object detection; Video tracking; RGB color model; Object (grammar); Detector; Tracking system; Eye tracking; Pattern recognition (psychology); Kalman filter","score_opus":0.049919280423009665,"score_gpt":0.2913845363450546,"score_spread":0.24146525592204496,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4292793930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2280907,0.0038066811,0.6934807,0.00079131866,0.0008971408,0.0005850174,0.03222895,0.025673272,0.014446245],"genre_scores_gemma":[0.40439934,0.0014020302,0.45551246,0.0006058813,0.00024256382,0.00073690055,0.12212628,0.001073105,0.013901449],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99799156,0.00028766788,0.00010332654,0.0009356312,0.0004935257,0.00018822662],"domain_scores_gemma":[0.9988696,0.00022234004,0.0001039183,0.00043498576,0.00029672025,0.00007241602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014754729,0.0013289903,0.0012780258,0.0019778325,0.0009390031,0.0016729756,0.0020115613,0.0013456938,0.002897154],"category_scores_gemma":[0.0035692423,0.00048313334,0.0012779733,0.0020947224,0.0005260557,0.0018737465,0.0018937988,0.0012640215,0.003310199],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017106589,0.0011268109,0.018641224,0.0010257382,0.00050610007,0.0004911652,0.00031002137,0.13992558,0.070260175,0.007215291,0.106424704,0.6523625],"study_design_scores_gemma":[0.00011883007,0.00032905702,0.0201526,0.0001654336,0.0001264428,0.0012865582,0.00024468813,0.8521236,0.06607224,0.014456233,0.044787742,0.00013659985],"about_ca_topic_score_codex":0.0115672555,"about_ca_topic_score_gemma":0.019203227,"teacher_disagreement_score":0.0115672555,"about_ca_system_score_codex":0.0008665935,"about_ca_system_score_gemma":0.0010200026,"threshold_uncertainty_score":0.022999883},"labels":[],"label_agreement":null},{"id":"W4295832395","doi":"10.1109/tits.2022.3203411","title":"SMART: Vision-Based Method of Cooperative Surveillance and Tracking by Multiple UAVs in the Urban Environment","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Intelligent Transportation Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Process (computing); Real-time computing; Artificial intelligence","score_opus":0.024193775851770738,"score_gpt":0.28107426434625754,"score_spread":0.2568804884944868,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4295832395","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017057547,0.00014123991,0.9815694,0.000047301506,0.000029060933,0.000029994013,0.00000765362,0.0002158008,0.0009020469],"genre_scores_gemma":[0.7449106,0.0002536512,0.25201714,0.000098754936,0.000046518042,0.00013042665,0.000060446313,0.000028077728,0.0024544098],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996786,0.000045079665,0.000014358876,0.00009777407,0.0001277762,0.000036482055],"domain_scores_gemma":[0.999818,0.000041857536,0.00003749474,0.00003184873,0.000048319867,0.000022455293],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003856656,0.0006097566,0.0005102297,0.00035173626,0.00038796806,0.00034043763,0.00096342806,0.0005757586,0.0004669778],"category_scores_gemma":[0.0004936212,0.0002923746,0.000599481,0.00032592984,0.00046165075,0.0006191095,0.0009121829,0.00049825275,0.00012334858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00017344866,0.00010709899,0.002588578,0.00015367082,0.00010345246,0.0003317552,0.00037614448,0.6230157,0.07925464,0.013579087,0.0021373418,0.27817908],"study_design_scores_gemma":[0.000010203257,0.000086485525,0.00025811067,0.0000032779574,0.000010777013,0.00005622248,0.00001883063,0.9949432,0.0031270853,0.0007613078,0.00071490736,0.000009575726],"about_ca_topic_score_codex":0.0032016523,"about_ca_topic_score_gemma":0.0023730078,"teacher_disagreement_score":0.0032016523,"about_ca_system_score_codex":0.0003730934,"about_ca_system_score_gemma":0.00057948026,"threshold_uncertainty_score":0.006366074},"labels":[],"label_agreement":null},{"id":"W4297684245","doi":"10.1109/mipr54900.2022.00060","title":"License Plate Privacy in Collaborative Visual Analysis of Traffic Scenes","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"License; Computer science; Traffic analysis; Construct (python library); Task (project management); Intelligent transportation system; Computer vision; Computer security; Artificial intelligence; Human–computer interaction; Transport engineering; Engineering","score_opus":0.019948134265924986,"score_gpt":0.3230441508963608,"score_spread":0.3030960166304358,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4297684245","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":[0.23239261,0.0006625967,0.7614419,0.00036015894,0.000041893738,0.00008987099,0.0005655922,0.0014671555,0.0029782096],"genre_scores_gemma":[0.9146793,0.0003220521,0.08130859,0.00010526787,0.000064200256,0.000045894893,0.0010820134,0.0001126338,0.0022799408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976387,0.00063218153,0.00007458497,0.00072290446,0.000670489,0.0002611878],"domain_scores_gemma":[0.99730116,0.00079005637,0.00045603843,0.0010317671,0.00029093432,0.00013008014],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019839033,0.0007245664,0.0008763607,0.0012591088,0.0007066231,0.001973873,0.0010740872,0.0008301819,0.0007435668],"category_scores_gemma":[0.005252803,0.0005505269,0.00075740553,0.0012347919,0.0010757713,0.002128894,0.0026394664,0.0010361193,0.0006563397],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022498139,0.00062938593,0.015342069,0.000250823,0.00033578987,0.0009506935,0.0011322884,0.24083866,0.07988679,0.008372297,0.0059164604,0.644095],"study_design_scores_gemma":[0.000030437473,0.0001578531,0.008904789,0.000023845305,0.0000688106,0.00038602971,0.00049630343,0.9303597,0.03839315,0.017329693,0.003802577,0.00004680489],"about_ca_topic_score_codex":0.006049273,"about_ca_topic_score_gemma":0.009475248,"teacher_disagreement_score":0.006049273,"about_ca_system_score_codex":0.00075786357,"about_ca_system_score_gemma":0.0010949554,"threshold_uncertainty_score":0.012028158},"labels":[],"label_agreement":null},{"id":"W4298009684","doi":"10.18280/ts.390424","title":"Cast Shadow Angle Detection in Morphological Aerial Images Using Faster R-CNN","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Shadow (psychology); Artificial intelligence; Computer vision; Computer science; Pixel; Aerial image; Feature (linguistics); Block (permutation group theory); Object detection; Segmentation; Image (mathematics); Mathematics","score_opus":0.04436156848940429,"score_gpt":0.28403168495919334,"score_spread":0.23967011646978906,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298009684","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14916772,0.0012805994,0.8390356,0.00035657219,0.00016971641,0.00012591414,0.0004237492,0.0043671164,0.0050730575],"genre_scores_gemma":[0.70254755,0.00097643805,0.28790525,0.0002981534,0.00007781958,0.00007842761,0.0011004856,0.00018774053,0.0068281265],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981517,0.000012273229,0.00000885327,0.00006249812,0.00006191632,0.000039374238],"domain_scores_gemma":[0.9998312,0.000023515486,0.000028577831,0.000031771335,0.0000709102,0.000013980568],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026637933,0.0007268833,0.00051754026,0.00076350436,0.00017943676,0.0006412987,0.0011738184,0.0005236876,0.0021162592],"category_scores_gemma":[0.000621431,0.00038113949,0.0008070262,0.0005543185,0.00021511837,0.00076141505,0.000543013,0.0005716526,0.0007784572],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031385463,0.00013892897,0.003997209,0.00017457883,0.00022194478,0.0003334567,0.000098310506,0.18266974,0.08607234,0.0018940893,0.0040555894,0.72003],"study_design_scores_gemma":[0.000005861438,0.000053481013,0.0014245843,0.0000097049115,0.000025401336,0.00009015068,0.000022847307,0.9886841,0.008049604,0.00061524875,0.0010108812,0.0000080359305],"about_ca_topic_score_codex":0.012260501,"about_ca_topic_score_gemma":0.017825214,"teacher_disagreement_score":0.012260501,"about_ca_system_score_codex":0.0006257283,"about_ca_system_score_gemma":0.00050239393,"threshold_uncertainty_score":0.02437824},"labels":[],"label_agreement":null},{"id":"W4298009711","doi":"10.18280/ts.390431","title":"Automatic Recognition for IoT Supervision Images Based on Modal Decomposition","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Modal; Decomposition; Artificial intelligence; Feature extraction; Internet of Things; Feature (linguistics); Computer vision; Segmentation; Generalization; Computation; Pattern recognition (psychology); Real-time computing; Embedded system; Algorithm","score_opus":0.033689389539240215,"score_gpt":0.29952415802443433,"score_spread":0.2658347684851941,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4298009711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.029390575,0.00007452871,0.9690372,0.000044431374,0.000017504131,0.000026882097,0.00003277233,0.00032508658,0.0010509647],"genre_scores_gemma":[0.42119032,0.0002848626,0.57555,0.000068955946,0.00003348954,0.00006752974,0.00024992,0.00007580887,0.0024790994],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998394,0.000022188295,0.000008144546,0.000042649386,0.00006967094,0.000017872188],"domain_scores_gemma":[0.9998579,0.00003369492,0.000019927018,0.00002815732,0.000050748666,0.000009502783],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018895017,0.00026499951,0.00024147789,0.00053039956,0.0001641377,0.00029986582,0.0002636834,0.00028063473,0.0011093112],"category_scores_gemma":[0.00047497652,0.000111313966,0.00037476278,0.00039535444,0.0002436049,0.000500499,0.00034071246,0.00038170436,0.00037741064],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018501843,0.00007290834,0.0012657403,0.00009483989,0.0000234923,0.00008896037,0.00012153343,0.021908004,0.3934611,0.0053209066,0.0017687487,0.57568866],"study_design_scores_gemma":[0.000010826526,0.00010106086,0.0036630959,0.000014120438,0.000019280518,0.00035487104,0.00007320311,0.8957375,0.09333996,0.0030861148,0.0035767157,0.000023234534],"about_ca_topic_score_codex":0.00080000923,"about_ca_topic_score_gemma":0.0009755233,"teacher_disagreement_score":0.0011093112,"about_ca_system_score_codex":0.00016123094,"about_ca_system_score_gemma":0.0001956081,"threshold_uncertainty_score":0.0037109852},"labels":[],"label_agreement":null},{"id":"W4300345831","doi":"10.1109/jiot.2022.3195359","title":"Online Multiple-Pedestrian Tracking With Detection-Pair-Based Graph Convolutional Networks","year":2022,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Pedestrian; Graph; Benchmark (surveying); Association (psychology); Artificial intelligence; Convolutional neural network; Data association; Process (computing); Machine learning; Data mining; Theoretical computer science","score_opus":0.026193462556199948,"score_gpt":0.26133259376637585,"score_spread":0.2351391312101759,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4300345831","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1209518,0.0008932488,0.8630516,0.0002715366,0.0001608209,0.00008660146,0.00072591467,0.010453724,0.003404681],"genre_scores_gemma":[0.7910383,0.00028694878,0.19808912,0.00024191389,0.000065096516,0.000074561314,0.0025191242,0.00023587114,0.007449174],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994449,0.000059494992,0.000016911918,0.00025889947,0.00011547636,0.00010438142],"domain_scores_gemma":[0.9992914,0.00019430103,0.000105018655,0.00017370454,0.00016340689,0.00007202005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006026068,0.0013968905,0.000991393,0.0013343641,0.0006631173,0.0007622608,0.0025691616,0.0010909125,0.0015642949],"category_scores_gemma":[0.0015123959,0.0007731944,0.0008904184,0.0017373502,0.0004951469,0.0012931975,0.0015835561,0.0015830247,0.0010447527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000582737,0.00038532066,0.00896497,0.00012672653,0.00032536712,0.00023949324,0.00015312359,0.4151816,0.013960768,0.0056563145,0.011134485,0.54328907],"study_design_scores_gemma":[0.000006780585,0.00001926535,0.0006628781,0.0000036613658,0.000014748228,0.00003689975,0.0000055106425,0.99543685,0.0018547858,0.0013937819,0.0005589696,0.0000058284522],"about_ca_topic_score_codex":0.032273185,"about_ca_topic_score_gemma":0.057401825,"teacher_disagreement_score":0.032273185,"about_ca_system_score_codex":0.001795291,"about_ca_system_score_gemma":0.0011929611,"threshold_uncertainty_score":0.06417066},"labels":[],"label_agreement":null},{"id":"W4302288356","doi":"10.1007/s00530-022-00996-6","title":"DATaR: Depth Augmented Target Redetection using Kernelized Correlation Filter","year":2022,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Kernel (algebra); BitTorrent tracker; Filter (signal processing); Correlation; Computer vision; Eye tracking; Tracking (education); RGB color model; Set (abstract data type); Pattern recognition (psychology); Mathematics","score_opus":0.052745493556100226,"score_gpt":0.2949115613963617,"score_spread":0.24216606784026148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4302288356","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":[0.011844852,0.00025572427,0.9814556,0.000098301316,0.00014710001,0.00007473821,0.00031710605,0.00408303,0.0017235375],"genre_scores_gemma":[0.14247268,0.00039723248,0.8434605,0.00026608285,0.000085493986,0.00014678077,0.001393912,0.00046297285,0.011314385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953806,0.00005149048,0.00001445302,0.00010635359,0.00023230932,0.0000572933],"domain_scores_gemma":[0.9996668,0.000049641792,0.000024229706,0.00009809363,0.00013682208,0.000024411942],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049306534,0.0008475326,0.00076681393,0.00083352096,0.00028362582,0.00067951385,0.001011378,0.00075731165,0.0053705075],"category_scores_gemma":[0.00090542325,0.00037168135,0.0005635502,0.00084235135,0.00028878756,0.0010495001,0.0015997245,0.00085024076,0.0025815142],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005723164,0.00018450891,0.0012408269,0.0001575851,0.00009199927,0.00014868072,0.000091356305,0.021447605,0.10407617,0.005684085,0.015784837,0.8505201],"study_design_scores_gemma":[0.000063040236,0.00014149304,0.0019151048,0.000018560946,0.000043100543,0.00029914852,0.000035301917,0.90870464,0.07185951,0.001725591,0.01514524,0.000049239785],"about_ca_topic_score_codex":0.0048792064,"about_ca_topic_score_gemma":0.00675932,"teacher_disagreement_score":0.0053705075,"about_ca_system_score_codex":0.00043731855,"about_ca_system_score_gemma":0.0010527887,"threshold_uncertainty_score":0.017966092},"labels":[],"label_agreement":null},{"id":"W4304480610","doi":"10.1109/tnnls.2022.3209918","title":"CLRNet: A Cross Locality Relation Network for Crowd Counting in Videos","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Neural Networks and Learning Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor; Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; National Natural Science Foundation of China","keywords":"Locality; Computer science; Relation (database); Similarity (geometry); Artificial intelligence; Measure (data warehouse); Pixel; Feature (linguistics); Consistency (knowledge bases); Cosine similarity; Computer vision; Pattern recognition (psychology); Data mining; Image (mathematics)","score_opus":0.023149508389525772,"score_gpt":0.2824192690738697,"score_spread":0.25926976068434393,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4304480610","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":[0.022870624,0.0006612349,0.9696098,0.00023455347,0.00014344188,0.00015951681,0.00064090855,0.0028197572,0.002860219],"genre_scores_gemma":[0.53897965,0.0008516696,0.44764084,0.00045778934,0.00021828443,0.0005327963,0.0027764828,0.00041889728,0.008123693],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993482,0.00013780498,0.000029824365,0.00024648482,0.00016799735,0.00006973427],"domain_scores_gemma":[0.9993274,0.00022591473,0.000093591334,0.00008334146,0.00021478851,0.000054948927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010860225,0.0014066658,0.0010602691,0.0025299438,0.0007370748,0.00079308345,0.002163209,0.0011461338,0.0026130653],"category_scores_gemma":[0.0036199412,0.0005663756,0.00096377183,0.0017006245,0.00060356985,0.0022058086,0.0021963248,0.0009597551,0.00086492667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045465285,0.00025875366,0.004064234,0.00026771156,0.00019627016,0.0002994093,0.00030084612,0.42439634,0.012167076,0.012856914,0.014336328,0.5304015],"study_design_scores_gemma":[0.000010700175,0.000042028416,0.0005416512,0.000015499096,0.000023527335,0.000063265834,0.00003178595,0.9897335,0.002642534,0.0046107713,0.0022693917,0.000015263287],"about_ca_topic_score_codex":0.009613038,"about_ca_topic_score_gemma":0.009614254,"teacher_disagreement_score":0.009613038,"about_ca_system_score_codex":0.0013123932,"about_ca_system_score_gemma":0.0008373249,"threshold_uncertainty_score":0.019114137},"labels":[],"label_agreement":null},{"id":"W4309869330","doi":"10.48550/arxiv.2211.11925","title":"Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data","year":2022,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Modality (human–computer interaction); Computer science; Modalities; Artificial intelligence; Generalization; Task (project management); Machine learning; Exploit; Deep learning; Sensor fusion; RGB color model; Identification (biology); Mathematics; Computer security","score_opus":0.2768497228349971,"score_gpt":0.29534648962199195,"score_spread":0.018496766786994856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309869330","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18132262,0.0014265991,0.80419856,0.0008058529,0.0003547356,0.00014105214,0.0017962791,0.006379972,0.0035743038],"genre_scores_gemma":[0.8410634,0.00042890932,0.14818329,0.0004794956,0.000102528305,0.00013546635,0.0043636407,0.00022572551,0.005017493],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991473,0.00024644323,0.00003465119,0.00029033792,0.0001570222,0.00012429156],"domain_scores_gemma":[0.99867404,0.00032663913,0.00018333578,0.0005595974,0.0002083401,0.000048084068],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001958903,0.0013788871,0.0012603338,0.0007887501,0.00049044326,0.0006925546,0.0025404561,0.0015560004,0.0020167113],"category_scores_gemma":[0.0051058363,0.00048905256,0.001749782,0.00089924503,0.0009745391,0.0019106204,0.0025805924,0.0022323988,0.0014425967],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009548213,0.00047452078,0.011803276,0.00026309313,0.00040783355,0.00074682006,0.0004465397,0.5206056,0.018482514,0.0042780107,0.014373674,0.42716327],"study_design_scores_gemma":[0.000011506124,0.00017770249,0.002645943,0.00003371511,0.000053910768,0.00029984736,0.000111450965,0.9698935,0.01835451,0.0047631953,0.0036119656,0.000042670603],"about_ca_topic_score_codex":0.0041956482,"about_ca_topic_score_gemma":0.0045527047,"teacher_disagreement_score":0.0041956482,"about_ca_system_score_codex":0.00065070245,"about_ca_system_score_gemma":0.0007269862,"threshold_uncertainty_score":0.010359824},"labels":[],"label_agreement":null},{"id":"W4309918511","doi":"10.1109/avss56176.2022.9959543","title":"Dynamic Background Subtraction by Generative Neural Networks","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Background subtraction; Computer science; Artificial intelligence; Artificial neural network; Generative model; Subtraction; Frame (networking); Code (set theory); Computer vision; Entropy (arrow of time); Pattern recognition (psychology); Foreground detection; Pixel; Generative grammar; Mathematics","score_opus":0.02342458219514736,"score_gpt":0.2932536192686339,"score_spread":0.26982903707348654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4309918511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008007674,0.00019309005,0.98892105,0.000081099,0.00003099019,0.00002261844,0.00005365411,0.0014066054,0.0012832277],"genre_scores_gemma":[0.37564647,0.00048414417,0.6121152,0.00048429286,0.00010588814,0.00012242494,0.0010122367,0.0009885855,0.009040731],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99960583,0.00007858934,0.000011815939,0.00014169453,0.000111795816,0.000050345076],"domain_scores_gemma":[0.99957615,0.00019957183,0.000047732123,0.00006428684,0.00008843008,0.000023770692],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057654554,0.0011565677,0.00077998795,0.0008114699,0.0003585636,0.0008202325,0.0014949327,0.00088929804,0.002086825],"category_scores_gemma":[0.0013865437,0.0006862401,0.0011457786,0.00074258324,0.000636204,0.0007504298,0.0010954926,0.0015146047,0.00095758535],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011342927,0.000064944914,0.00080141565,0.000076493016,0.00012896539,0.00011708371,0.000076630145,0.666205,0.018463643,0.009171743,0.002736798,0.3020439],"study_design_scores_gemma":[0.0000030978797,0.000006611615,0.00012579367,0.000004053844,0.000007449301,0.000026435013,0.0000031719883,0.9941075,0.0027596732,0.0023842652,0.0005668765,0.000005050554],"about_ca_topic_score_codex":0.0065952744,"about_ca_topic_score_gemma":0.011033304,"teacher_disagreement_score":0.0065952744,"about_ca_system_score_codex":0.00091911136,"about_ca_system_score_gemma":0.00069560163,"threshold_uncertainty_score":0.013113797},"labels":[],"label_agreement":null},{"id":"W4310681647","doi":"10.20944/preprints202212.0049.v1","title":"Deep Learning Empowered Fast and Accurate Multiclass UAV Detection in Challenging Weather Conditions","year":2022,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Multirotor; Computer science; Artificial intelligence; Deep learning; Object detection; Real-time computing; Precision and recall; Single shot; Machine learning; Computer vision; Pattern recognition (psychology); Engineering; Aerospace engineering","score_opus":0.10189745760075761,"score_gpt":0.37088264805513477,"score_spread":0.2689851904543772,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310681647","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3339385,0.0013460483,0.65492684,0.00042646492,0.00022823462,0.000078810284,0.00048613639,0.0045789215,0.0039899726],"genre_scores_gemma":[0.9040003,0.00028662317,0.09082808,0.0001505286,0.00005223187,0.0000340421,0.0010299105,0.00006960726,0.0035486775],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959856,0.000054740045,0.000018693478,0.00015099316,0.00008709311,0.00008985089],"domain_scores_gemma":[0.99956137,0.000121190875,0.000052878626,0.00009283063,0.00013525596,0.000036409656],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006749955,0.00089348096,0.00065216137,0.00073771784,0.00028360257,0.0006729721,0.00090901786,0.00081098906,0.0006743942],"category_scores_gemma":[0.0014125266,0.00029956465,0.0004368003,0.00049973506,0.0002485709,0.0007663979,0.0008054828,0.0009308658,0.0005063714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028052105,0.00032990868,0.008209305,0.0000981482,0.00013159393,0.00013350049,0.000105458224,0.16882677,0.033850126,0.001257466,0.0064922282,0.78028506],"study_design_scores_gemma":[0.0000036541592,0.000023050263,0.0010464741,0.000005202709,0.000008494156,0.000023903536,0.000012481161,0.9926507,0.005309712,0.00051599694,0.00039647237,0.000003858615],"about_ca_topic_score_codex":0.007862857,"about_ca_topic_score_gemma":0.010493235,"teacher_disagreement_score":0.007862857,"about_ca_system_score_codex":0.0005319752,"about_ca_system_score_gemma":0.0005746128,"threshold_uncertainty_score":0.01563418},"labels":[],"label_agreement":null},{"id":"W4310881526","doi":"10.1145/3563357.3564054","title":"TEA-bot","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Ceiling (cloud); HVAC; Leak; Computer science; Robot; Point cloud; Artificial intelligence; Computer vision; Real-time computing; RGB color model; Unmanned ground vehicle; Convolutional neural network; Simulation; Engineering; Air conditioning; Mechanical engineering","score_opus":0.018126386307452974,"score_gpt":0.26283273793444717,"score_spread":0.2447063516269942,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4310881526","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03062807,0.00056918344,0.7686962,0.00059200794,0.0005032972,0.00045156546,0.0019674199,0.16566066,0.030931551],"genre_scores_gemma":[0.37306383,0.0007063289,0.56019634,0.0014814095,0.00009159271,0.0008093692,0.0076901293,0.006930029,0.04903098],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9997143,0.000030242882,0.000013379125,0.00009980242,0.00008617614,0.000056098186],"domain_scores_gemma":[0.99968374,0.00006738745,0.000029966346,0.000104528015,0.00007147537,0.00004300553],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00027516944,0.0008870977,0.00039353452,0.00033998597,0.0003549339,0.0006076568,0.0017633556,0.0008607731,0.0105064465],"category_scores_gemma":[0.001035841,0.00038167337,0.0006945285,0.0001973959,0.00040875285,0.0014549986,0.0015381207,0.0009893713,0.0061664316],"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":[0.0011929394,0.0006577735,0.0073904954,0.0013387857,0.0002148511,0.0011954564,0.0004958052,0.10035371,0.11389896,0.03308441,0.19266613,0.54751074],"study_design_scores_gemma":[0.00013397475,0.0005148768,0.0023799173,0.00009896568,0.00006326009,0.00090952386,0.00012951379,0.7188559,0.04304212,0.013660424,0.22011676,0.000094709234],"about_ca_topic_score_codex":0.0024117932,"about_ca_topic_score_gemma":0.0037920896,"teacher_disagreement_score":0.98949355,"about_ca_system_score_codex":0.00035160081,"about_ca_system_score_gemma":0.00053245964,"threshold_uncertainty_score":0},"labels":[],"label_agreement":null},{"id":"W4311164977","doi":"10.18280/ts.390519","title":"A Traffic Parameter Detection Algorithm Based on Double Coils","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Hefei Normal University","keywords":"Intersection (aeronautics); Computer science; Algorithm; Computer vision; Artificial intelligence; Image processing; Real-time computing; Image (mathematics); Engineering","score_opus":0.026483992213108848,"score_gpt":0.26274194198557627,"score_spread":0.23625794977246742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4311164977","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011522318,0.00011005971,0.9868146,0.000031176118,0.00004877893,0.00003796379,0.000022468295,0.0006636408,0.0007489922],"genre_scores_gemma":[0.31760204,0.00030522753,0.67866886,0.00010824491,0.00013104094,0.0001512729,0.00018991885,0.00012653592,0.0027168542],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99940693,0.00007245856,0.000036040594,0.0001999924,0.00022375627,0.00006078102],"domain_scores_gemma":[0.9994772,0.00013287293,0.00006221811,0.00005827385,0.00022338437,0.000046078414],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003882141,0.00082845124,0.00082521624,0.0017888092,0.00040013838,0.000761002,0.0007855698,0.00073142565,0.0013318381],"category_scores_gemma":[0.0011929242,0.0004890686,0.0004916035,0.00080157473,0.0004520628,0.0015393735,0.00072078,0.00054818025,0.00071490364],"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":[0.00038827755,0.000092479684,0.0027506377,0.0001838708,0.000069706075,0.0001670364,0.00020921987,0.027121702,0.19602467,0.0056674643,0.0034482537,0.76387674],"study_design_scores_gemma":[0.00006125253,0.00045837506,0.0042652506,0.000025400182,0.00009176261,0.001339765,0.00008355032,0.84551823,0.1299496,0.0036670947,0.014438035,0.00010167333],"about_ca_topic_score_codex":0.0008048333,"about_ca_topic_score_gemma":0.0006944097,"teacher_disagreement_score":0.0017888092,"about_ca_system_score_codex":0.00037598124,"about_ca_system_score_gemma":0.00045934948,"threshold_uncertainty_score":0.004455447},"labels":[],"label_agreement":null},{"id":"W4312177897","doi":"10.18280/ria.360510","title":"Modified Ant Colony Optimization for Human Recognition in Videos of Low Resolution","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Support vector machine; Computer vision; Histogram; Pattern recognition (psychology); Background subtraction; Local binary patterns; Low resolution; Classifier (UML); RGB color model; Pixel; Histogram of oriented gradients; High resolution; Image (mathematics); Geography","score_opus":0.08722286678289976,"score_gpt":0.3199621069514708,"score_spread":0.23273924016857106,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312177897","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11055547,0.00073894404,0.8821837,0.00029482832,0.00009879214,0.00011401255,0.000055471726,0.0006622064,0.00529658],"genre_scores_gemma":[0.85276836,0.00021197191,0.14315806,0.00010347842,0.00002196128,0.00016060406,0.0000999466,0.000045425873,0.003430109],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99978954,0.000047634436,0.000012557789,0.000051425788,0.00006481626,0.000034047338],"domain_scores_gemma":[0.9994655,0.00029212932,0.00007558043,0.000023850393,0.000114968985,0.000028026994],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00036451276,0.00064641575,0.000804402,0.00035933987,0.00024100681,0.0005331493,0.00074047135,0.0006268346,0.0010358948],"category_scores_gemma":[0.0012239412,0.00026816304,0.00042779246,0.00035256412,0.00032451103,0.00032991957,0.00037901528,0.0005699082,0.0001791487],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00008219795,0.00006719513,0.0009205001,0.00008544105,0.000036896137,0.000108769316,0.000054835542,0.92522776,0.0059297127,0.00094846264,0.0007240746,0.06581414],"study_design_scores_gemma":[0.0000025279571,0.000013086789,0.00007629327,0.0000011116337,0.0000018314304,0.0000052810447,0.0000035849732,0.99946684,0.00024058209,0.00010693495,0.00008059697,0.0000012860603],"about_ca_topic_score_codex":0.0067631975,"about_ca_topic_score_gemma":0.0042683193,"teacher_disagreement_score":0.0067631975,"about_ca_system_score_codex":0.0003631597,"about_ca_system_score_gemma":0.00059765315,"threshold_uncertainty_score":0.013447642},"labels":[],"label_agreement":null},{"id":"W4312287581","doi":"10.1109/tii.2022.3217499","title":"Edge-Oriented Social Distance Monitoring System Based on MTCNN","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Industrial Informatics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"National Research Foundation of Korea","keywords":"Minimum bounding box; Computer science; Bounding overwatch; Reliability (semiconductor); Convolutional neural network; Process (computing); Enhanced Data Rates for GSM Evolution; Real-time computing; Artificial intelligence; Power (physics); Image (mathematics)","score_opus":0.05895689383520082,"score_gpt":0.28565886158663834,"score_spread":0.22670196775143753,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312287581","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21725646,0.0006408229,0.7574462,0.000533371,0.0004081218,0.00025503902,0.001003331,0.008432067,0.014024624],"genre_scores_gemma":[0.8885026,0.00017655865,0.10358864,0.00035756416,0.0000547474,0.00014396121,0.0006890137,0.000051461673,0.006435409],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968874,0.000027232269,0.000015490914,0.000100368736,0.000120669436,0.00004745571],"domain_scores_gemma":[0.99967384,0.00002996205,0.000040750714,0.000036610665,0.00018637753,0.000032450316],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026653122,0.0005394169,0.0005812836,0.0005572333,0.0003888708,0.00042543458,0.0012691311,0.0005355694,0.0018550891],"category_scores_gemma":[0.00070409663,0.00018279436,0.00021312172,0.00041045883,0.00017831057,0.00082059536,0.0009369535,0.00040058699,0.0006094101],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013554564,0.00042797928,0.016908264,0.00029796333,0.00011832986,0.0007667844,0.00025484528,0.06538243,0.11355358,0.0042705033,0.021882907,0.774781],"study_design_scores_gemma":[0.00004748323,0.00018235647,0.004761805,0.000018011262,0.00004202322,0.00019522989,0.000044287397,0.9613472,0.02812992,0.0010414853,0.0041556917,0.00003451321],"about_ca_topic_score_codex":0.006374156,"about_ca_topic_score_gemma":0.011083773,"teacher_disagreement_score":0.006374156,"about_ca_system_score_codex":0.00067156286,"about_ca_system_score_gemma":0.0006740945,"threshold_uncertainty_score":0.012674093},"labels":[],"label_agreement":null},{"id":"W4312336271","doi":"10.1117/12.2612427","title":"Smartphone sensor application for gait analysis","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Gait analysis; Computer science; Gait; Physical medicine and rehabilitation; Medicine","score_opus":0.020404857304843583,"score_gpt":0.2935283531270681,"score_spread":0.2731234958222245,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312336271","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.097063534,0.008679934,0.6496431,0.0012957347,0.0016757828,0.004345185,0.039311074,0.104378365,0.09360726],"genre_scores_gemma":[0.5514039,0.0050067077,0.2880953,0.0020731697,0.0005221395,0.004777473,0.023747006,0.002226111,0.1221483],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99974173,0.000031100215,0.000024923964,0.000052934585,0.00012798588,0.0000213064],"domain_scores_gemma":[0.99961084,0.00006892004,0.000027779688,0.000040791812,0.00022730051,0.000024519417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022501308,0.000851928,0.00058217347,0.00089036964,0.00014380184,0.00031513034,0.0005388611,0.0005893661,0.0374455],"category_scores_gemma":[0.00091400906,0.0001851583,0.00032154046,0.00049663393,0.00006729883,0.00030364515,0.0004241117,0.00025491137,0.014018817],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009500515,0.00030058273,0.0057286886,0.0018960707,0.00012680054,0.00086025964,0.00021967481,0.0012162849,0.2051752,0.0015207818,0.2005722,0.5814335],"study_design_scores_gemma":[0.00052433123,0.0020551002,0.078981124,0.00083184586,0.0004913135,0.007742484,0.00039265692,0.092126675,0.22745124,0.0030098942,0.5860179,0.00037551214],"about_ca_topic_score_codex":0.00071741216,"about_ca_topic_score_gemma":0.0013363111,"teacher_disagreement_score":0.0374455,"about_ca_system_score_codex":0.00013234779,"about_ca_system_score_gemma":0.00019144062,"threshold_uncertainty_score":0.12526774},"labels":[],"label_agreement":null},{"id":"W4312545381","doi":"10.1109/cvpr52688.2022.00312","title":"Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Lakehead University; Vector Institute","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Interpolation (computer graphics); Consistency (knowledge bases); Constraint (computer-aided design); Computer vision; Scale (ratio); Shadow (psychology); Generalization; Pixel; Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.07243037119685516,"score_gpt":0.2973142349716297,"score_spread":0.2248838637747745,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312545381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061356664,0.0003843573,0.9320807,0.00021945701,0.00007505064,0.00013650212,0.00047215476,0.0029438057,0.002331318],"genre_scores_gemma":[0.7060427,0.00031785024,0.28584808,0.0003329051,0.00014079307,0.00023344945,0.0026193627,0.00033908276,0.004125687],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991934,0.0001244871,0.000027647116,0.0003732518,0.00017493824,0.00010627231],"domain_scores_gemma":[0.9987105,0.00039780868,0.00018254916,0.00033356858,0.0003025001,0.00007313651],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012889287,0.0011235104,0.0009718936,0.0008611969,0.0005118762,0.0007249111,0.002257887,0.0009890646,0.0013001572],"category_scores_gemma":[0.003651855,0.00054103066,0.00073418836,0.00085257075,0.0007666927,0.0016312865,0.0015428961,0.0015664846,0.000689905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051925634,0.00031051916,0.0072842203,0.00020715734,0.00012396548,0.00026462748,0.00026030946,0.3660869,0.033109136,0.0036381679,0.011276823,0.57691884],"study_design_scores_gemma":[0.000008804783,0.000038616257,0.0009799957,0.000008137442,0.0000101676615,0.000047494683,0.000024180512,0.99075186,0.0052295104,0.0020761115,0.00081780914,0.0000073116557],"about_ca_topic_score_codex":0.0061137085,"about_ca_topic_score_gemma":0.010003512,"teacher_disagreement_score":0.0061137085,"about_ca_system_score_codex":0.000838551,"about_ca_system_score_gemma":0.0010120233,"threshold_uncertainty_score":0.012156248},"labels":[],"label_agreement":null},{"id":"W4312852783","doi":"10.1007/978-3-031-19214-2_28","title":"Target Detection Algorithm Based on Feature Optimization and Sample Equalization","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Pascal (unit); Feature (linguistics); Artificial intelligence; Cascade; Pattern recognition (psychology); Convergence (economics); Algorithm; Data mining","score_opus":0.017705571299725224,"score_gpt":0.2627131672295567,"score_spread":0.24500759592983146,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312852783","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":[0.005118937,0.00010294606,0.99360293,0.000029795092,0.00003511537,0.000017624245,0.000014037235,0.00051399425,0.00056457013],"genre_scores_gemma":[0.11775604,0.00025942284,0.87570643,0.00008348839,0.00007307873,0.0000937371,0.00021557366,0.00014816978,0.00566408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997094,0.000028975737,0.000014974153,0.00009043997,0.00012508432,0.000031018673],"domain_scores_gemma":[0.999739,0.00007571207,0.000017576267,0.000036734098,0.00011803979,0.000013045936],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034696455,0.00051236607,0.0011534079,0.0005880769,0.00034443018,0.00057134044,0.00089359295,0.0006283125,0.0026313337],"category_scores_gemma":[0.0007656464,0.0003319071,0.0006287028,0.0008072409,0.00030247265,0.0010553754,0.00067068345,0.00089648314,0.0011227422],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031598378,0.00009835658,0.00047051505,0.000081479295,0.00006557624,0.000045891193,0.000045396835,0.02620817,0.091969006,0.005792832,0.0027722225,0.87213445],"study_design_scores_gemma":[0.000027899381,0.00013069501,0.0011188808,0.0000058618593,0.000047144124,0.00023004966,0.000011515577,0.955406,0.037616882,0.002523472,0.0028528455,0.000028798924],"about_ca_topic_score_codex":0.0014971422,"about_ca_topic_score_gemma":0.001578988,"teacher_disagreement_score":0.0026313337,"about_ca_system_score_codex":0.00033319808,"about_ca_system_score_gemma":0.0007595877,"threshold_uncertainty_score":0.008802712},"labels":[],"label_agreement":null},{"id":"W4312854824","doi":"10.54364/aaiml.2022.1129","title":"AI Based Approach for Shop Classification and a Comparative Study with Human","year":2022,"lang":"en","type":"article","venue":"Advances in Artificial Intelligence and Machine Learning","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Support vector machine; Classifier (UML); Artificial intelligence; Computer science; Machine learning; Pattern recognition (psychology)","score_opus":0.14289915172530154,"score_gpt":0.3978587606556734,"score_spread":0.25495960893037184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4312854824","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":[0.74428475,0.0067633786,0.17426684,0.0015030282,0.0008007695,0.0005603539,0.0013059855,0.002101317,0.06841363],"genre_scores_gemma":[0.9273441,0.001159331,0.06214371,0.00021888677,0.00016783847,0.00011596455,0.0014378951,0.00007158608,0.0073406887],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99769956,0.0005895913,0.00015099272,0.00054087274,0.00085146585,0.00016749001],"domain_scores_gemma":[0.99687433,0.0012867992,0.00018904758,0.00035250394,0.0011142396,0.00018304746],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030369963,0.0005500055,0.00040140754,0.004419102,0.00072261255,0.0022217173,0.00091392983,0.0009731278,0.003123567],"category_scores_gemma":[0.004916955,0.00011534553,0.0005514488,0.0023437534,0.00069702533,0.0019957724,0.00070748554,0.0005496259,0.0013494638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008435364,0.0010819235,0.089950405,0.00088462455,0.00027000383,0.0007421188,0.0030489175,0.0105829565,0.014647887,0.0077554886,0.012533447,0.8576587],"study_design_scores_gemma":[0.000073268966,0.0014939856,0.20695905,0.0003531864,0.000317961,0.0027193197,0.011515499,0.67092943,0.025912024,0.016442925,0.06304015,0.00024321486],"about_ca_topic_score_codex":0.0036292733,"about_ca_topic_score_gemma":0.0046550725,"teacher_disagreement_score":0.004419102,"about_ca_system_score_codex":0.0009593528,"about_ca_system_score_gemma":0.00052065024,"threshold_uncertainty_score":0.016061306},"labels":[],"label_agreement":null},{"id":"W4313013512","doi":"10.1109/cvpr52688.2022.00716","title":"Part-based Pseudo Label Refinement for Unsupervised Person Re-identification","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":278,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Smoothing; Context (archaeology); Cluster analysis; Similarity (geometry); Noise (video); Exploit; Machine learning; Pattern recognition (psychology); Feature (linguistics); Identification (biology); Code (set theory); Source code; Feature learning; Task (project management); Data mining; Image (mathematics)","score_opus":0.13511414583861167,"score_gpt":0.3274952700900095,"score_spread":0.19238112425139783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313013512","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014905122,0.000325187,0.9799203,0.00006923109,0.00007247701,0.000074043135,0.00022648189,0.0032507519,0.0011563431],"genre_scores_gemma":[0.30331123,0.00045365162,0.68115187,0.00041747693,0.00014625204,0.00022940428,0.0036610854,0.000967803,0.009661307],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977452,0.0005385149,0.00007424645,0.00087950233,0.0005665157,0.00019602082],"domain_scores_gemma":[0.9973195,0.00043564732,0.00023364867,0.0012842763,0.0006322264,0.000094684714],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014111217,0.0013288745,0.0016323754,0.0017646243,0.00087455433,0.0008557298,0.0025816923,0.0012409148,0.0034306995],"category_scores_gemma":[0.003965949,0.00052550115,0.0014749041,0.0018516585,0.000837202,0.0024426407,0.0019998404,0.001829912,0.005268384],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006569337,0.00028834929,0.003822347,0.00018956505,0.00014448055,0.00020681243,0.00037345733,0.04472072,0.03860487,0.007018558,0.013732584,0.89024127],"study_design_scores_gemma":[0.00003865119,0.00021805552,0.0027953715,0.000034781144,0.00007952513,0.0006051815,0.00019871532,0.9260126,0.039605692,0.016777027,0.013542191,0.00009226996],"about_ca_topic_score_codex":0.0036292574,"about_ca_topic_score_gemma":0.006201351,"teacher_disagreement_score":0.0036292574,"about_ca_system_score_codex":0.0005967313,"about_ca_system_score_gemma":0.00084558985,"threshold_uncertainty_score":0.011476815},"labels":[],"label_agreement":null},{"id":"W4313025015","doi":"10.1109/ijcnn55064.2022.9892052","title":"Exploring the Effectiveness of Appearance Descriptor in DeepSORT","year":2022,"lang":"en","type":"article","venue":"2022 International Joint Conference on Neural Networks (IJCNN)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Canadian Climate Forum","keywords":"Artificial intelligence; sort; Computer science; Intuition; Tracking (education); Computer vision; Active appearance model; Video tracking; Pattern recognition (psychology); Object detection; Component (thermodynamics); Machine learning; Object (grammar); Image (mathematics)","score_opus":0.11738076830328271,"score_gpt":0.2886747889039362,"score_spread":0.17129402060065352,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313025015","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.4059015,0.0013849415,0.5856335,0.000594952,0.0001552463,0.0000949648,0.00018518016,0.0023452437,0.0037044953],"genre_scores_gemma":[0.9089385,0.00024815896,0.08808074,0.0002127823,0.000032628264,0.00005055343,0.0003495668,0.000113079506,0.0019740365],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995161,0.0000895236,0.000024988422,0.00014777946,0.00013101977,0.000090539834],"domain_scores_gemma":[0.9988913,0.00051601423,0.00010376982,0.00014977953,0.00024318665,0.00009610897],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014929887,0.0010392223,0.001071421,0.0006570018,0.00029654996,0.0009209131,0.0017456989,0.0013127495,0.001415864],"category_scores_gemma":[0.003709499,0.0003557985,0.00051616895,0.0006242858,0.0006356282,0.002243975,0.0013482126,0.0012650159,0.00032503213],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038834504,0.00042678966,0.0040989253,0.00015446164,0.00011031382,0.0001296791,0.00008859266,0.65962934,0.014636933,0.0065316097,0.0020540473,0.31175095],"study_design_scores_gemma":[0.000010095542,0.00007458799,0.00020661743,0.0000031712123,0.000009063663,0.000016038255,0.000007741797,0.9968478,0.0015888948,0.0010836681,0.00014848397,0.0000038141384],"about_ca_topic_score_codex":0.005810504,"about_ca_topic_score_gemma":0.005332218,"teacher_disagreement_score":0.005810504,"about_ca_system_score_codex":0.0008820195,"about_ca_system_score_gemma":0.0011495338,"threshold_uncertainty_score":0.011553347},"labels":[],"label_agreement":null},{"id":"W4313040967","doi":"10.1109/igarss46834.2022.9884365","title":"Object Tracking and Anomaly Detection in Full Motion Video","year":2022,"lang":"en","type":"article","venue":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Centre For Cold Ocean Resources Engineering","funders":"","keywords":"Anomaly detection; Computer science; Artificial intelligence; Computer vision; Video tracking; Trajectory; Tracking (education); Cluster analysis; Object detection; Matching (statistics); Object (grammar); Pattern recognition (psychology); Anomaly (physics); Motion (physics); Similarity (geometry); Image (mathematics); Mathematics","score_opus":0.016748785803818993,"score_gpt":0.2701266559758033,"score_spread":0.2533778701719843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313040967","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1690622,0.000352353,0.8285049,0.00007304128,0.00003293164,0.000048293634,0.00019303513,0.0005649004,0.0011683377],"genre_scores_gemma":[0.67211276,0.00040314396,0.32447562,0.000035538196,0.000041650754,0.000067193185,0.00064448675,0.000057865676,0.0021617229],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99950445,0.00006422978,0.000025327208,0.00014600872,0.00019843328,0.000061517094],"domain_scores_gemma":[0.9990709,0.0003539906,0.00016238772,0.00010944991,0.0002643922,0.000038907467],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006131121,0.00035188967,0.0005330736,0.0017738129,0.00026192272,0.0006321905,0.0006572467,0.0007221712,0.0005991163],"category_scores_gemma":[0.0019834917,0.00022587697,0.000324421,0.0013450435,0.00035628674,0.0010622339,0.00045423428,0.0003092569,0.00028449434],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030735202,0.00015004963,0.012151802,0.00020141496,0.0000825645,0.00035114132,0.00021446585,0.09374448,0.17688736,0.006023572,0.0011317978,0.708754],"study_design_scores_gemma":[0.000008122938,0.00016974949,0.013629594,0.000013295644,0.0000150429805,0.0003267068,0.000093020455,0.94341636,0.037124153,0.003613638,0.0015699764,0.000020345855],"about_ca_topic_score_codex":0.0026443466,"about_ca_topic_score_gemma":0.0022059663,"teacher_disagreement_score":0.0026443466,"about_ca_system_score_codex":0.0004189418,"about_ca_system_score_gemma":0.00036266595,"threshold_uncertainty_score":0.005257964},"labels":[],"label_agreement":null},{"id":"W4313436908","doi":"10.3390/rs15010002","title":"A Real-Time Tracking Algorithm for Multi-Target UAV Based on Deep Learning","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"National Natural Science Foundation of China","keywords":"Computer science; Real-time computing; Tracking (education); Drone; Scheme (mathematics); Cloud computing; Reliability (semiconductor); Artificial intelligence; Deep learning; Computer vision; Simulation","score_opus":0.03578456934291481,"score_gpt":0.3041391041830963,"score_spread":0.2683545348401815,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4313436908","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":[0.04816578,0.00035410476,0.94754195,0.00013212043,0.000076638025,0.000053023174,0.000070480026,0.002231781,0.0013741202],"genre_scores_gemma":[0.63505787,0.00031111515,0.35828757,0.00022093854,0.000035564954,0.00012013159,0.0005220001,0.00011978403,0.005324992],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997929,0.000016901327,0.000012971963,0.000074432464,0.000055466382,0.00004735964],"domain_scores_gemma":[0.99976724,0.00005120374,0.000028314143,0.000026257072,0.00010622661,0.000020776008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004601223,0.0006302122,0.00054152776,0.00056363636,0.0003889572,0.0004315443,0.0010344052,0.00069183187,0.0011543334],"category_scores_gemma":[0.0008123,0.00035263487,0.0004973173,0.0005491208,0.00024199508,0.00078803115,0.0006081385,0.0010161066,0.00036346584],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001986622,0.00014502314,0.003574063,0.0000734019,0.00008970895,0.000115536,0.000081672435,0.31707188,0.021387488,0.002218927,0.0038381873,0.6512054],"study_design_scores_gemma":[0.000005749184,0.000023457349,0.00024187028,0.0000024690219,0.000005709244,0.000014981039,0.0000039880365,0.9968573,0.002334216,0.0002214433,0.00028574804,0.0000031737839],"about_ca_topic_score_codex":0.019679602,"about_ca_topic_score_gemma":0.017184205,"teacher_disagreement_score":0.019679602,"about_ca_system_score_codex":0.00082488096,"about_ca_system_score_gemma":0.001006812,"threshold_uncertainty_score":0.03913009},"labels":[],"label_agreement":null},{"id":"W4316012726","doi":"10.1109/cicn56167.2022.10008357","title":"An IoT Based Traffic Management System Using Drone and AI","year":2022,"lang":"en","type":"article","venue":"2022 14th International Conference on Computational Intelligence and Communication Networks (CICN)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Real-time computing; Drone; Bandwidth (computing); Cloud computing; Server; Computer network","score_opus":0.06889041557505308,"score_gpt":0.3453378493631648,"score_spread":0.2764474337881117,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4316012726","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.41986394,0.000739685,0.5098569,0.00043990184,0.00051774736,0.0010500009,0.0009121278,0.020533955,0.046085794],"genre_scores_gemma":[0.93878394,0.00019129648,0.05228242,0.00018243212,0.000046817742,0.00023898084,0.0006223472,0.000060519436,0.007591281],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99983084,0.000017830089,0.000013048056,0.000048589893,0.000062739884,0.00002706068],"domain_scores_gemma":[0.9998536,0.000016124462,0.000015250299,0.000025077303,0.00005594651,0.000033966495],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00015584602,0.00046044347,0.00044782396,0.0005907295,0.00048826466,0.0006181743,0.0008690693,0.00045574535,0.0021782615],"category_scores_gemma":[0.0002451784,0.00015097686,0.00022961438,0.00036634624,0.00017198201,0.00068252627,0.00048291322,0.00031857062,0.0005084373],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0017428134,0.002149714,0.02023156,0.00081219734,0.0002786422,0.003013532,0.0007542658,0.12679496,0.35198382,0.019178152,0.02696731,0.44609302],"study_design_scores_gemma":[0.00016100137,0.0007633429,0.0065128,0.00005544941,0.000103033795,0.0007828585,0.0001788134,0.9172779,0.047498956,0.0021860339,0.024392406,0.00008732647],"about_ca_topic_score_codex":0.0027531416,"about_ca_topic_score_gemma":0.0025255927,"teacher_disagreement_score":0.0027531416,"about_ca_system_score_codex":0.00028309118,"about_ca_system_score_gemma":0.00040562113,"threshold_uncertainty_score":0.007286966},"labels":[],"label_agreement":null},{"id":"W4317242591","doi":"10.1109/rivf55975.2022.10013871","title":"SunFA - An open-source application for behavior analysis in online video-conferencing","year":2022,"lang":"en","type":"article","venue":"2022 RIVF International Conference on Computing and Communication Technologies (RIVF)","topic":"Video Surveillance and Tracking Methods","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":"ASTER","funders":"","keywords":"Computer science; Videoconferencing; Open source; Multimedia; Source code; Zoom; Teleconference; Asterisk; Software; Open source software; Operating system; The Internet; Voice over IP","score_opus":0.07055845637842148,"score_gpt":0.37949387603963336,"score_spread":0.30893541966121185,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317242591","genre_codex":"software","genre_gemma":"software","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"software","genre_consensus":"software","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01317141,0.00051497674,0.22387944,0.00016218377,0.00019420097,0.00077691855,0.020875484,0.7331351,0.0072902637],"genre_scores_gemma":[0.28447837,0.0010931446,0.5020756,0.00070485024,0.00021642563,0.004671832,0.08230372,0.086711764,0.03774425],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950635,0.000062633924,0.000048648064,0.00014359571,0.00016524788,0.00007355916],"domain_scores_gemma":[0.9990646,0.000358583,0.00008184494,0.00012779277,0.00025328973,0.00011389909],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073367916,0.0015492205,0.0008358486,0.0018835176,0.00038691986,0.0008026677,0.0016854616,0.00067845004,0.032345615],"category_scores_gemma":[0.0030403447,0.00054892147,0.0010654279,0.0006859478,0.00019077206,0.0011068396,0.0014805848,0.00086819363,0.0107586235],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022544214,0.0006576088,0.012094236,0.0024983806,0.00050086767,0.0007255616,0.0012548129,0.006031818,0.04491278,0.0040060147,0.35007575,0.57498777],"study_design_scores_gemma":[0.0011998288,0.00075664785,0.073065154,0.00086408143,0.0004570084,0.0025345557,0.0006078874,0.27937442,0.1171389,0.018158976,0.50500405,0.0008384427],"about_ca_topic_score_codex":0.0030165466,"about_ca_topic_score_gemma":0.0038803734,"teacher_disagreement_score":0.032345615,"about_ca_system_score_codex":0.0004924778,"about_ca_system_score_gemma":0.0009776498,"threshold_uncertainty_score":0.10820687},"labels":[],"label_agreement":null},{"id":"W4317383319","doi":"10.1109/robio55434.2022.10011950","title":"HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re-Identification Implemented on Tour-Guide Robot","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Task (project management); Robot; Computer science; Identification (biology); Artificial intelligence; Inference; Computer vision; Engineering; Biology; Systems engineering","score_opus":0.09231934371884551,"score_gpt":0.381988969151443,"score_spread":0.28966962543259744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317383319","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0984932,0.0014693992,0.73118526,0.0006816393,0.0009251569,0.00066827575,0.0060119526,0.14958075,0.010984374],"genre_scores_gemma":[0.44400567,0.0005306545,0.5100759,0.0008668366,0.00012473635,0.0006629801,0.015088016,0.0021686223,0.026476491],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99978095,0.000028665923,0.00000814797,0.00009669418,0.000040613348,0.00004489507],"domain_scores_gemma":[0.99977976,0.000043651733,0.000016682836,0.00005975322,0.00007930939,0.000020917223],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051610696,0.002051374,0.0006867446,0.0007864311,0.0004011675,0.00042798521,0.0023791147,0.0010856087,0.0058864574],"category_scores_gemma":[0.0011742488,0.0006416952,0.0006670484,0.0004717795,0.00033753007,0.0011778977,0.0008020355,0.0010929988,0.0037121435],"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":[0.0008416673,0.00045365503,0.0021805814,0.00035246825,0.0002959935,0.00041813412,0.00019186946,0.16982244,0.023890575,0.0028477435,0.099385865,0.699319],"study_design_scores_gemma":[0.00007330649,0.0002160507,0.0008502741,0.000020649792,0.000046160425,0.000091732705,0.000047111364,0.9695784,0.016802989,0.0018034626,0.010436307,0.000033602],"about_ca_topic_score_codex":0.028091695,"about_ca_topic_score_gemma":0.035795815,"teacher_disagreement_score":0.028091695,"about_ca_system_score_codex":0.0008273654,"about_ca_system_score_gemma":0.0010199614,"threshold_uncertainty_score":0.055856347},"labels":[],"label_agreement":null},{"id":"W4317383733","doi":"10.1109/robio55434.2022.10011698","title":"TGRMPT: A Head-Shoulder Aided Multi-Person Tracker and a New Large-Scale Dataset for Tour-Guide Robot","year":2022,"lang":"en","type":"article","venue":"2022 IEEE International Conference on Robotics and Biomimetics (ROBIO)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"BitTorrent tracker; Computer science; Artificial intelligence; Computer vision; Robot; Metric (unit); Tracking (education); Scale (ratio); Eye tracking; Track (disk drive); Video tracking; Visualization; Object (grammar); Engineering","score_opus":0.15437206092122527,"score_gpt":0.3789699168256016,"score_spread":0.22459785590437634,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317383733","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10337736,0.005189288,0.12356798,0.0009045175,0.0021662705,0.0019541862,0.67408156,0.07183569,0.016923089],"genre_scores_gemma":[0.05537233,0.00043220745,0.06400302,0.00027810168,0.000093941126,0.0006322352,0.8738725,0.0007768717,0.004538877],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99842334,0.00019609569,0.00010255487,0.00072619924,0.0003939305,0.00015788169],"domain_scores_gemma":[0.99853563,0.0001687387,0.00013679832,0.00044967423,0.0005031216,0.00020600806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010064922,0.0027132728,0.0015401219,0.0020022248,0.0010829783,0.0010578507,0.0026276319,0.0023070155,0.0060185874],"category_scores_gemma":[0.0029714718,0.00050896796,0.0013997656,0.002199596,0.000561346,0.0014807055,0.002287726,0.0019054925,0.011058103],"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":[0.0015442622,0.0011583607,0.016486816,0.0028524885,0.00054413395,0.0007676615,0.00036115604,0.014936802,0.020078495,0.001349214,0.6777598,0.26216075],"study_design_scores_gemma":[0.0008934224,0.0018880107,0.13291363,0.0012650951,0.00060022,0.0043717837,0.0013645325,0.25449812,0.04269912,0.0057986025,0.55308604,0.0006213459],"about_ca_topic_score_codex":0.02910739,"about_ca_topic_score_gemma":0.058631778,"teacher_disagreement_score":0.02910739,"about_ca_system_score_codex":0.0009757176,"about_ca_system_score_gemma":0.0017727333,"threshold_uncertainty_score":0.05787593},"labels":[],"label_agreement":null},{"id":"W4317662918","doi":"10.1049/cit2.12182","title":"Multi‐granularity re‐ranking for visible‐infrared person re‐identification","year":2023,"lang":"en","type":"article","venue":"CAAI Transactions on Intelligence Technology","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Granularity; Modality (human–computer interaction); Computer science; Identification (biology); Encoder; Artificial intelligence; Ranking (information retrieval); Pattern recognition (psychology); Autoencoder; Feature (linguistics); Reciprocal; Similarity (geometry); Computer vision; Deep learning","score_opus":0.09731236392277717,"score_gpt":0.3574756507002333,"score_spread":0.26016328677745615,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4317662918","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16545773,0.003943497,0.7815531,0.00047654627,0.00084038643,0.00043337012,0.003922749,0.030029558,0.013343053],"genre_scores_gemma":[0.61371934,0.0006786078,0.35109445,0.0004238521,0.00019427176,0.00016414707,0.010628643,0.0008404887,0.022256216],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9985455,0.00020395438,0.00006762781,0.00042426056,0.00051118765,0.00024758774],"domain_scores_gemma":[0.999046,0.00013899112,0.000068511865,0.00030704387,0.0003804913,0.0000589042],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006739345,0.001391733,0.0017988392,0.0015964289,0.0007755699,0.0009604214,0.0019568605,0.001024342,0.007667688],"category_scores_gemma":[0.0023058173,0.0002937406,0.00087762123,0.0013736684,0.00028901314,0.0015697052,0.0012100228,0.0010485327,0.006763242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083746464,0.00044819072,0.002263527,0.00028827877,0.000121146535,0.0002465083,0.000098324,0.032712124,0.0402544,0.002808472,0.05003925,0.8698823],"study_design_scores_gemma":[0.00006959224,0.00033002533,0.0043607447,0.000035406614,0.00008751754,0.00077025325,0.0001952744,0.92042613,0.053627964,0.004110923,0.015881497,0.000104683066],"about_ca_topic_score_codex":0.011440332,"about_ca_topic_score_gemma":0.016418887,"teacher_disagreement_score":0.011440332,"about_ca_system_score_codex":0.0007163744,"about_ca_system_score_gemma":0.0009968987,"threshold_uncertainty_score":0.025650978},"labels":[],"label_agreement":null},{"id":"W4318464426","doi":"10.3390/s23031504","title":"Person Re-Identification with RGB–D and RGB–IR Sensors: A Comprehensive Survey","year":2023,"lang":"en","type":"review","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"RGB color model; Computer science; Artificial intelligence; Embedding; Identification (biology); Computer vision; Deep learning","score_opus":0.20060736219307732,"score_gpt":0.38162880661906956,"score_spread":0.18102144442599225,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318464426","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061064303,0.16114289,0.71881706,0.0015463679,0.0024484377,0.00084722054,0.007546123,0.012535649,0.034051996],"genre_scores_gemma":[0.2866813,0.13225195,0.5091566,0.0021888767,0.0015603533,0.00062776066,0.03777321,0.001065357,0.028694654],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9977488,0.00039139227,0.00017193887,0.0007692741,0.00076366606,0.00015483216],"domain_scores_gemma":[0.99827707,0.00037429764,0.0001413163,0.00058146985,0.0005677694,0.000058170805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013987209,0.0024552718,0.0022735232,0.0036027934,0.00057851034,0.0014940582,0.0020410623,0.0014238125,0.003141438],"category_scores_gemma":[0.003723192,0.0006036205,0.0015500903,0.0035995631,0.00058246596,0.0027464386,0.0017271339,0.0011871703,0.0065916106],"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":[0.00023062238,0.00016081685,0.0042493585,0.0019394624,0.000200817,0.00014387304,0.00012290914,0.0052213916,0.005555795,0.0015171927,0.027086603,0.95357114],"study_design_scores_gemma":[0.00007443547,0.00081249897,0.06496122,0.003325998,0.0008117171,0.009363997,0.0024463895,0.38729218,0.11538596,0.019231997,0.3955943,0.00069934194],"about_ca_topic_score_codex":0.005301963,"about_ca_topic_score_gemma":0.005818285,"teacher_disagreement_score":0.005301963,"about_ca_system_score_codex":0.00050650723,"about_ca_system_score_gemma":0.0007289637,"threshold_uncertainty_score":0.010542154},"labels":[],"label_agreement":null},{"id":"W4318606478","doi":"10.1109/ssci51031.2022.10022278","title":"Beta-Liouville and Inverted Beta-Liouville Based Predictive Models for Occupancy Detection using Small Training Data","year":2022,"lang":"en","type":"article","venue":"2022 IEEE Symposium Series on Computational Intelligence (SSCI)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"BETA (programming language); Computer science; Training set; Gaussian; Flexibility (engineering); Alpha (finance); Artificial intelligence; Gaussian process; Mixture model; Machine learning; Pattern recognition (psychology); Algorithm; Mathematics; Statistics; Physics","score_opus":0.16458087595283558,"score_gpt":0.32846541712828364,"score_spread":0.16388454117544807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4318606478","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0073473584,0.00032320764,0.99108934,0.00017529778,0.000032392643,0.000019650597,0.00007006733,0.00022995567,0.0007127891],"genre_scores_gemma":[0.66467243,0.0015479058,0.32144982,0.00071945117,0.00034121188,0.0004322518,0.0016804524,0.00040271704,0.008753909],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988751,0.00038800438,0.00004670582,0.00032445314,0.00023536292,0.00013037951],"domain_scores_gemma":[0.99642295,0.0024942162,0.00023894095,0.00027644192,0.00044048228,0.00012692722],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002977384,0.0013570602,0.0017066704,0.0013661713,0.0006041019,0.0019453948,0.0050575193,0.002229167,0.0020346271],"category_scores_gemma":[0.010448857,0.0012074468,0.001776394,0.0014277337,0.0017117783,0.0036699611,0.0021273477,0.0035901312,0.0009964178],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016155098,0.00009646116,0.0031275481,0.00012142935,0.00009040781,0.00011438071,0.00022860429,0.8610156,0.0023622522,0.047429875,0.0017956069,0.08345635],"study_design_scores_gemma":[0.0000020977166,0.000009349189,0.00010788332,0.000006842851,0.000004683499,0.000013908361,0.000004933936,0.99365944,0.0002091295,0.0057118926,0.0002624783,0.0000073360507],"about_ca_topic_score_codex":0.007866559,"about_ca_topic_score_gemma":0.007105684,"teacher_disagreement_score":0.007866559,"about_ca_system_score_codex":0.0012236137,"about_ca_system_score_gemma":0.0010268962,"threshold_uncertainty_score":0.015746117},"labels":[],"label_agreement":null},{"id":"W4319299834","doi":"10.1109/wacv56688.2023.00170","title":"AttTrack: Online Deep Attention Transfer for Multi-object Tracking","year":2023,"lang":"en","type":"article","venue":"2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Inference; Video tracking; Component (thermodynamics); Artificial intelligence; Object detection; Tracking (education); Object (grammar); Key (lock); Deep learning; Transfer of learning; Machine learning; Analytics; Interleaving; Visual analytics; Real-time computing; Visualization; Data mining; Pattern recognition (psychology); Computer security","score_opus":0.09286112842069297,"score_gpt":0.3786234061387497,"score_spread":0.28576227771805673,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319299834","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":[0.038448807,0.001229462,0.9274907,0.00039258602,0.00033665966,0.00015038527,0.00062744913,0.02720299,0.0041208873],"genre_scores_gemma":[0.65890354,0.0004673129,0.3167603,0.00088844885,0.00023276139,0.00033638944,0.0032743565,0.0010505881,0.018086439],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99952877,0.00005548347,0.000013832097,0.00021546354,0.00009977619,0.000086764216],"domain_scores_gemma":[0.9993358,0.00023378458,0.000050624883,0.00015880643,0.00014027175,0.00008066637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011722288,0.0016885896,0.0009618432,0.0007910139,0.0006800208,0.000909351,0.0033686322,0.0018129194,0.006255667],"category_scores_gemma":[0.002786625,0.0006336729,0.0007995382,0.00091429,0.00051251263,0.002730132,0.0026360168,0.0026897846,0.0025454247],"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":[0.00043904647,0.0005695897,0.0021771002,0.00014089089,0.00019078866,0.00019162054,0.0001498709,0.15872322,0.016138911,0.004991994,0.029453818,0.7868331],"study_design_scores_gemma":[0.000025653368,0.0000781173,0.00028489283,0.000007669026,0.000015658992,0.00003512725,0.0000126935975,0.98999995,0.0038873798,0.0040931148,0.0015508474,0.00000886927],"about_ca_topic_score_codex":0.014556023,"about_ca_topic_score_gemma":0.019256592,"teacher_disagreement_score":0.014556023,"about_ca_system_score_codex":0.0012712658,"about_ca_system_score_gemma":0.0015274523,"threshold_uncertainty_score":0.028942585},"labels":[],"label_agreement":null},{"id":"W4319336138","doi":"10.1109/wacvw58289.2023.00008","title":"Multimodal Data Augmentation for Visual-Infrared Person ReID with Corrupted Data","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Modality (human–computer interaction); Computer science; Modalities; Artificial intelligence; Generalization; Task (project management); Deep learning; Machine learning; Sensor fusion; Exploit; RGB color model; Identification (biology); Computer vision; Mathematics","score_opus":0.2057462445707791,"score_gpt":0.41027613063375146,"score_spread":0.20452988606297234,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4319336138","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.2045557,0.0014847284,0.77919704,0.0008616332,0.0003739988,0.00015594352,0.0021248239,0.007559881,0.0036862143],"genre_scores_gemma":[0.8421641,0.0004024224,0.14672618,0.00048381358,0.00009556018,0.00013638496,0.005059028,0.00024309887,0.004689441],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99910575,0.00026089972,0.00003639063,0.00030063442,0.00016759273,0.00012868727],"domain_scores_gemma":[0.9985917,0.0003458186,0.00018560723,0.0006093661,0.00021614654,0.000051290754],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0020092775,0.0014402254,0.0012713416,0.00078575243,0.0004913009,0.0006991653,0.0025238863,0.0015221599,0.0020891072],"category_scores_gemma":[0.0052972953,0.00048539756,0.0018026506,0.0008668639,0.0009586297,0.0019373548,0.002669194,0.0022688683,0.0015148938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010750361,0.00052351545,0.012432366,0.00027465745,0.0004369366,0.0007604516,0.00045805346,0.5148102,0.0201444,0.003999321,0.015479303,0.42960572],"study_design_scores_gemma":[0.000012990076,0.00020269887,0.0029085963,0.00003510419,0.00005642804,0.0003150655,0.000120998244,0.9674333,0.0203906,0.0046314346,0.003847708,0.000044995824],"about_ca_topic_score_codex":0.0042765616,"about_ca_topic_score_gemma":0.0046149576,"teacher_disagreement_score":0.0042765616,"about_ca_system_score_codex":0.00066776486,"about_ca_system_score_gemma":0.0007312361,"threshold_uncertainty_score":0.010626197},"labels":[],"label_agreement":null},{"id":"W4320029434","doi":"10.1109/globecom48099.2022.10000957","title":"Real-Time Jaywalking Detection and Notification System using Deep Learning and Multi-Object Tracking","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Computer science; Object detection; Artificial intelligence; Computer vision; Deep learning; Trajectory; Segmentation; Video tracking; Real-time computing; Object (grammar); Tracking system; Tracking (education); Path (computing); Component (thermodynamics); Motion (physics); Computer network; Kalman filter","score_opus":0.05396969457280422,"score_gpt":0.32151514583903307,"score_spread":0.26754545126622886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320029434","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":[0.157338,0.0005522793,0.8269091,0.00031696286,0.00029324886,0.00013437362,0.00035872028,0.010476382,0.0036208706],"genre_scores_gemma":[0.863006,0.0002744385,0.13101329,0.00028707858,0.000058409325,0.000102112,0.0007237459,0.0000853933,0.0044494756],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99958974,0.00002779487,0.000026891608,0.0001375932,0.00013761566,0.00008032847],"domain_scores_gemma":[0.9996277,0.000052286177,0.000054749056,0.000051177118,0.00015054765,0.00006362283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00054101314,0.0007296705,0.00090251287,0.0008663235,0.00035822517,0.0005944553,0.0012960043,0.0008087002,0.0010817375],"category_scores_gemma":[0.0008834036,0.00037573918,0.00047020992,0.000579054,0.00018618225,0.0009992317,0.0010189057,0.0009287264,0.0006287927],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075014186,0.00082497066,0.011394046,0.00019378254,0.00011784607,0.0006166883,0.00014417461,0.072951786,0.07848873,0.0012976235,0.0084730955,0.8247471],"study_design_scores_gemma":[0.000024301477,0.000121381025,0.0026816602,0.000009041851,0.00002788573,0.00013116273,0.000021489483,0.980646,0.014850011,0.0005520604,0.00091175316,0.000023246908],"about_ca_topic_score_codex":0.0068814475,"about_ca_topic_score_gemma":0.007003159,"teacher_disagreement_score":0.0068814475,"about_ca_system_score_codex":0.00068336364,"about_ca_system_score_gemma":0.0009898461,"threshold_uncertainty_score":0.013682783},"labels":[],"label_agreement":null},{"id":"W4320036905","doi":"10.1007/978-3-031-25085-9_25","title":"The Tenth Visual Object Tracking VOT2022 Challenge Results","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":58,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; BitTorrent tracker; Tracking (education); Eye tracking; Term (time); Bounding overwatch; RGB color model","score_opus":0.044780553239919925,"score_gpt":0.31235569012251246,"score_spread":0.26757513688259255,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320036905","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06928499,0.08361362,0.49284643,0.035764206,0.057855867,0.001807998,0.04812358,0.04318108,0.16752225],"genre_scores_gemma":[0.18945628,0.013490805,0.2899053,0.011939614,0.009188158,0.0011583262,0.26181456,0.006144664,0.21690224],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9967416,0.0004158939,0.00015447347,0.00084558956,0.0013639187,0.00047849474],"domain_scores_gemma":[0.995883,0.0011002345,0.00008801032,0.0008885904,0.0013130774,0.00072725216],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0045355735,0.002410238,0.00313515,0.0012420333,0.0018239433,0.0048166756,0.0030892103,0.004148776,0.014236051],"category_scores_gemma":[0.00822068,0.0005639123,0.001326638,0.0014214119,0.00093950646,0.0031358923,0.004397523,0.0037217792,0.016513472],"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":[0.0007689725,0.0002886265,0.0007381323,0.0007328758,0.00015288951,0.00022685144,0.000106360356,0.004781886,0.00622253,0.0058216196,0.5886676,0.3914916],"study_design_scores_gemma":[0.00059255405,0.0010923472,0.0055120084,0.00045611142,0.0002972503,0.0011477307,0.00041973818,0.12274938,0.030456688,0.049149133,0.7880025,0.00012455617],"about_ca_topic_score_codex":0.016596274,"about_ca_topic_score_gemma":0.020682605,"teacher_disagreement_score":0.016596274,"about_ca_system_score_codex":0.0016264708,"about_ca_system_score_gemma":0.003056843,"threshold_uncertainty_score":0.04762435},"labels":[],"label_agreement":null},{"id":"W4320713382","doi":"10.1109/tim.2023.3244819","title":"An Improved SSD-Like Deep Network-Based Object Detection Method for Indoor Scenes","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Instrumentation and Measurement","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Object detection; Computer vision; Feature extraction; Object (grammar); Feature (linguistics); Detector; Deep learning; Pattern recognition (psychology)","score_opus":0.048972136119174725,"score_gpt":0.32211055203782146,"score_spread":0.27313841591864674,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4320713382","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.031154463,0.0009782779,0.9583502,0.00021633731,0.00019832926,0.00011298823,0.0006520671,0.0053390646,0.0029982831],"genre_scores_gemma":[0.29891568,0.0011658343,0.6750271,0.00063828647,0.00019028873,0.00019765546,0.0051215542,0.0005410788,0.018202573],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936503,0.000038181675,0.000027656879,0.00024561537,0.00022176487,0.00010189278],"domain_scores_gemma":[0.9997049,0.00003317441,0.000026483727,0.00005182711,0.0001543209,0.000029311292],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005119223,0.0014342873,0.0013243424,0.0013637332,0.00039530415,0.0007932883,0.0020500508,0.0008973129,0.0029454248],"category_scores_gemma":[0.0008140419,0.000602006,0.0011288008,0.0011228353,0.00032122087,0.0014032298,0.0013355013,0.0010471776,0.0015598603],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023848139,0.00018317503,0.002120769,0.00016349404,0.00014892464,0.00013169314,0.0000612721,0.034787625,0.03911535,0.0018388781,0.009166275,0.9120441],"study_design_scores_gemma":[0.000020773154,0.00008456728,0.0017919298,0.000014303005,0.000053996686,0.00017504574,0.000024841733,0.9698989,0.022209197,0.0013792943,0.0043206085,0.000026454327],"about_ca_topic_score_codex":0.008752029,"about_ca_topic_score_gemma":0.016645337,"teacher_disagreement_score":0.008752029,"about_ca_system_score_codex":0.0007722791,"about_ca_system_score_gemma":0.0012042072,"threshold_uncertainty_score":0.017402172},"labels":[],"label_agreement":null},{"id":"W4321192188","doi":"10.1109/icce56470.2023.10043379","title":"Disentanglement-Based Multi-Vehicle Detection and Tracking for Gate-Free Parking Lot Management","year":2023,"lang":"en","type":"article","venue":"2023 IEEE International Conference on Consumer Electronics (ICCE)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital; University of British Columbia","funders":"Ministry of Education","keywords":"BitTorrent tracker; Computer science; Computer vision; Parking lot; Frame (networking); Artificial intelligence; Object detection; Tracking (education); Video tracking; Object (grammar); Real-time computing; Pattern recognition (psychology); Eye tracking; Engineering; Computer network","score_opus":0.10283876073329484,"score_gpt":0.35591810269555546,"score_spread":0.2530793419622606,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321192188","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.09901362,0.00077199115,0.89280576,0.00012265568,0.0001015946,0.00010235305,0.00026156587,0.004812606,0.0020077985],"genre_scores_gemma":[0.6476402,0.00044660494,0.34807026,0.00011832466,0.000040067956,0.00006808065,0.0012735015,0.00017221554,0.0021706563],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99962926,0.00005054465,0.000013023745,0.00012592705,0.000115890565,0.00006534579],"domain_scores_gemma":[0.9996911,0.000057889407,0.000038643244,0.00007990605,0.00009144672,0.000041103285],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005399956,0.0006494444,0.00072679395,0.000994721,0.0003368049,0.0007073885,0.0011073347,0.000576381,0.0010800945],"category_scores_gemma":[0.0009167368,0.00041916416,0.00042938997,0.0008938373,0.0002670583,0.0011337142,0.0012434289,0.00088267785,0.00075779756],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007319984,0.00039916503,0.0067464025,0.00021027662,0.00014805845,0.0002613169,0.00028319322,0.08202377,0.14158739,0.0026178395,0.0067562987,0.7582343],"study_design_scores_gemma":[0.0000258227,0.00012977864,0.005037987,0.000013045024,0.00004140911,0.00023688682,0.00006632725,0.9502301,0.03802839,0.0014721962,0.004688096,0.00002994991],"about_ca_topic_score_codex":0.0045923856,"about_ca_topic_score_gemma":0.009928321,"teacher_disagreement_score":0.0045923856,"about_ca_system_score_codex":0.00039180042,"about_ca_system_score_gemma":0.00085335545,"threshold_uncertainty_score":0.009131312},"labels":[],"label_agreement":null},{"id":"W4321231180","doi":"10.1007/978-3-031-25072-9_48","title":"Visible-Infrared Person Re-Identification Using Privileged Intermediate Information","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Artificial intelligence; RGB color model; Representation (politics); Domain (mathematical analysis); Identification (biology); Matching (statistics); Feature (linguistics); Modal; Computer vision; Deep learning; Benchmark (surveying); Pattern recognition (psychology)","score_opus":0.0435553397549575,"score_gpt":0.2976164437715187,"score_spread":0.2540611040165612,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321231180","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":[0.06557887,0.0005039515,0.9069049,0.00012888339,0.00030756136,0.000076643366,0.00023617232,0.0028903687,0.023372613],"genre_scores_gemma":[0.577291,0.00050049945,0.37422317,0.0001691908,0.00013702743,0.000060953953,0.00079033175,0.00031917295,0.046508625],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993175,0.00007130013,0.00002009088,0.00017904158,0.0002922474,0.000119801254],"domain_scores_gemma":[0.9994374,0.0000681488,0.00003749552,0.00032244937,0.00010688409,0.000027615892],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005141694,0.00061986496,0.00076416385,0.0008112441,0.0006585396,0.001226417,0.00100171,0.0008019835,0.0056723338],"category_scores_gemma":[0.0009921475,0.00036150235,0.000634516,0.00064743403,0.0005437824,0.0025763824,0.002362265,0.0010553076,0.006333821],"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":[0.0008796294,0.00035695476,0.0027478551,0.00014784875,0.0000708699,0.00037318585,0.00039554643,0.012188407,0.1605416,0.017475642,0.007501215,0.79732126],"study_design_scores_gemma":[0.000052382893,0.000687853,0.012357629,0.00011620745,0.0001575888,0.00276431,0.00045002758,0.5699113,0.35158533,0.027288435,0.03448067,0.00014831116],"about_ca_topic_score_codex":0.0009945655,"about_ca_topic_score_gemma":0.0014827846,"teacher_disagreement_score":0.0056723338,"about_ca_system_score_codex":0.00026039316,"about_ca_system_score_gemma":0.00042496278,"threshold_uncertainty_score":0.018975854},"labels":[],"label_agreement":null},{"id":"W4321231212","doi":"10.1007/978-3-031-25072-9_46","title":"Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Resolution (logic); Low resolution; Infrared; High resolution; Computer graphics (images); Computer security; Remote sensing; Optics","score_opus":0.049794902575039574,"score_gpt":0.29019806756505373,"score_spread":0.24040316499001416,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321231212","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":[0.022035988,0.0008371894,0.970873,0.0001125765,0.0000902001,0.000025909047,0.00013395131,0.0009713456,0.0049198065],"genre_scores_gemma":[0.5205052,0.0024860143,0.45797685,0.0002156399,0.00022417463,0.000057131747,0.0007217389,0.00022026786,0.017592996],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9991416,0.000120269615,0.000028088218,0.0002448451,0.00036355533,0.00010170129],"domain_scores_gemma":[0.9993011,0.00018402617,0.00008327396,0.00031475467,0.000094401985,0.000022486875],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00047984353,0.0006615814,0.0008442815,0.00066680333,0.0004063594,0.0010706434,0.0011444493,0.00062446523,0.0022470718],"category_scores_gemma":[0.0014386489,0.00049851905,0.00077660073,0.0008809672,0.0004517753,0.0019002525,0.0011615625,0.0009899042,0.001923874],"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":[0.000553319,0.00015833507,0.0013826776,0.00027946933,0.000095947464,0.00026109346,0.00016358614,0.02034746,0.16904348,0.012622237,0.006408909,0.7886835],"study_design_scores_gemma":[0.000031878306,0.0003987959,0.004718368,0.000063234,0.00014629547,0.0026580843,0.00013443032,0.594461,0.35755917,0.02145993,0.018294916,0.00007382891],"about_ca_topic_score_codex":0.00042621396,"about_ca_topic_score_gemma":0.0005317828,"teacher_disagreement_score":0.0022470718,"about_ca_system_score_codex":0.00026449873,"about_ca_system_score_gemma":0.00029675162,"threshold_uncertainty_score":0.007517159},"labels":[],"label_agreement":null},{"id":"W4321433952","doi":"10.1155/2023/5349965","title":"A Small Target Pedestrian Detection Model Based on Autonomous Driving","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China; Fujian Provincial Finance Department","keywords":"Computer science; Pedestrian detection; Artificial intelligence; Feature (linguistics); Residual; Pyramid (geometry); Robustness (evolution); Feature extraction; Block (permutation group theory); Pattern recognition (psychology); Computer vision; Object detection; Process (computing); Pixel; Pedestrian; Algorithm; Engineering; Mathematics","score_opus":0.027570539030380842,"score_gpt":0.280790832558423,"score_spread":0.2532202935280422,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321433952","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10111099,0.00069197116,0.8894875,0.00041486605,0.00017596722,0.00007107224,0.00024084715,0.0014979511,0.006308812],"genre_scores_gemma":[0.9574173,0.00037983077,0.03487526,0.00011135335,0.00005057291,0.00007715089,0.00024945952,0.000047669968,0.006791503],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986017,0.0000109757475,0.0000053087447,0.00005789842,0.000034755307,0.000030825813],"domain_scores_gemma":[0.99986637,0.000025137986,0.000014172519,0.000008975654,0.00006988877,0.000015489679],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00024550897,0.0006962777,0.0006571985,0.00049730187,0.00036220453,0.00059586205,0.0016749542,0.00066578423,0.0016315635],"category_scores_gemma":[0.0004993689,0.00044485801,0.00076504983,0.00036015495,0.0003356178,0.0008127212,0.0006024164,0.00071340194,0.00043692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00023601942,0.00008955878,0.0037117207,0.0001023819,0.00009240828,0.00029096074,0.00012590067,0.86546206,0.0132142175,0.007069153,0.0024355105,0.1071701],"study_design_scores_gemma":[0.000002231975,0.000012801364,0.0001411383,0.0000012072082,0.000007114705,0.00001574179,0.0000019492172,0.9988973,0.0003651492,0.00039592915,0.00015630692,0.0000030343133],"about_ca_topic_score_codex":0.021768948,"about_ca_topic_score_gemma":0.01192681,"teacher_disagreement_score":0.021768948,"about_ca_system_score_codex":0.0007159212,"about_ca_system_score_gemma":0.00078089006,"threshold_uncertainty_score":0.043284476},"labels":[],"label_agreement":null},{"id":"W4321599502","doi":"10.3390/s23052430","title":"In-Bed Posture Classification Using Deep Neural Network","year":2023,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":36,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Rehabilitation Institute; University of Toronto; University Health Network","funders":"Mitacs","keywords":"Artificial neural network; Artificial intelligence; Computer science; Pattern recognition (psychology); Engineering","score_opus":0.06269272518478532,"score_gpt":0.3314686701493391,"score_spread":0.2687759449645538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4321599502","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":[0.6965566,0.004453797,0.28243798,0.00065335067,0.00076725014,0.00021310053,0.0036653355,0.0055312174,0.0057213004],"genre_scores_gemma":[0.9634084,0.0006724413,0.028865116,0.00027246174,0.00008884419,0.00009411806,0.003049824,0.00004085268,0.003507931],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997527,0.000033749737,0.000015469199,0.00007150662,0.00004657046,0.0000799892],"domain_scores_gemma":[0.99982387,0.000046226418,0.000026156187,0.000018060742,0.000061065446,0.00002474553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028792294,0.0013104455,0.00073168706,0.0007345498,0.0002099049,0.00048633627,0.0007501353,0.0006533954,0.0011913673],"category_scores_gemma":[0.00062534463,0.0003647759,0.0006947105,0.00051047804,0.00015523765,0.00034973622,0.0005132562,0.0006935066,0.0005479559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011629319,0.0011452833,0.034595765,0.00021716877,0.00037309196,0.0006058035,0.00012140764,0.20894945,0.02453913,0.00042286896,0.0116254985,0.7162417],"study_design_scores_gemma":[0.000016275362,0.00015599803,0.008041372,0.000027142281,0.00003625664,0.000073358635,0.000038820097,0.9873095,0.003350041,0.00033450002,0.00060085824,0.000015835561],"about_ca_topic_score_codex":0.011082577,"about_ca_topic_score_gemma":0.017071294,"teacher_disagreement_score":0.011082577,"about_ca_system_score_codex":0.00062297937,"about_ca_system_score_gemma":0.00048268732,"threshold_uncertainty_score":0.022036135},"labels":[],"label_agreement":null},{"id":"W4322096581","doi":"10.2139/ssrn.4370111","title":"3d Guided Spatio-Temporal Attention for Video Person Re-Identification","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Identification (biology); Computer science; Artificial intelligence; Psychology; Cognitive psychology","score_opus":0.045528762078595804,"score_gpt":0.32428563332157584,"score_spread":0.27875687124298004,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322096581","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":[0.048356574,0.0012929633,0.94291973,0.00016269597,0.0003144612,0.00010822468,0.00045268555,0.0023954464,0.003997276],"genre_scores_gemma":[0.6427408,0.0013773877,0.34342217,0.00054876396,0.00033535206,0.00016051477,0.0011200411,0.0004021054,0.009892835],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994735,0.00008086551,0.000015031447,0.00017211246,0.00015298527,0.000105573476],"domain_scores_gemma":[0.99949515,0.00014674106,0.00004587404,0.00011034171,0.00016040115,0.000041505005],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005256798,0.0008927947,0.0010732873,0.0016269757,0.00038568984,0.00082486094,0.0010280531,0.0010421432,0.002727136],"category_scores_gemma":[0.0014743017,0.00041703164,0.0007777424,0.0014133736,0.00030124007,0.00082910043,0.0017425233,0.0006997419,0.0017631467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00076475844,0.00027072904,0.0022726837,0.00020068129,0.00017631128,0.00029199012,0.00022920447,0.02891404,0.12196179,0.0020485513,0.008752322,0.834117],"study_design_scores_gemma":[0.000016622012,0.00014637761,0.005064062,0.000023125143,0.00007316303,0.0005167378,0.0001024733,0.9612387,0.026463237,0.0027333328,0.0035938048,0.000028416756],"about_ca_topic_score_codex":0.005099276,"about_ca_topic_score_gemma":0.007819229,"teacher_disagreement_score":0.005099276,"about_ca_system_score_codex":0.00035939063,"about_ca_system_score_gemma":0.0005312769,"threshold_uncertainty_score":0.010139167},"labels":[],"label_agreement":null},{"id":"W4322096648","doi":"10.1007/978-3-031-26351-4_20","title":"Cluster Contrast for Unsupervised Person Re-identification","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":192,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Softmax function; Artificial intelligence; Pattern recognition (psychology); Feature learning; Contrast (vision); Feature (linguistics); Cluster analysis; Unsupervised learning; Feature vector; Identification (biology); Artificial neural network; Machine learning","score_opus":0.05271762679865026,"score_gpt":0.2996299347600211,"score_spread":0.2469123079613708,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4322096648","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":[0.011447123,0.000589527,0.9835437,0.000076262564,0.0001238285,0.00007375165,0.0003051786,0.0019170031,0.0019235658],"genre_scores_gemma":[0.1782467,0.00055460044,0.8064847,0.00017390536,0.0001704081,0.00018588235,0.0017444879,0.0010569075,0.011382447],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99912494,0.0001544685,0.000026704924,0.00031790475,0.00024885352,0.00012718233],"domain_scores_gemma":[0.99865025,0.0005459669,0.00006539109,0.00034458993,0.00034429037,0.000049512677],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011629316,0.0010250254,0.0018566251,0.0013720191,0.0006663123,0.0010813243,0.0021880153,0.0012013871,0.005853112],"category_scores_gemma":[0.0026707938,0.00055560435,0.0010090854,0.0015966342,0.0005663292,0.0014538973,0.0020410265,0.0015327462,0.0034383752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00090883486,0.00018194233,0.0011303754,0.00018745087,0.00019417945,0.00009587056,0.00008092636,0.033507973,0.05515072,0.010117918,0.011053293,0.8873905],"study_design_scores_gemma":[0.000028517668,0.00016334806,0.0026180118,0.000029411949,0.000079913065,0.00027383945,0.00006192636,0.9311529,0.046748273,0.010926009,0.00787139,0.000046558587],"about_ca_topic_score_codex":0.0038951153,"about_ca_topic_score_gemma":0.0077038812,"teacher_disagreement_score":0.005853112,"about_ca_system_score_codex":0.00070210046,"about_ca_system_score_gemma":0.0008764764,"threshold_uncertainty_score":0.019580603},"labels":[],"label_agreement":null},{"id":"W4323322511","doi":"10.1016/j.cviu.2023.103664","title":"Weakly supervised multi-class semantic video segmentation for road scenes","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Institute for Information and Communications Technology Promotion; National Research Foundation of Korea; Information Technology Research Centre; Ministry of Science, ICT and Future Planning","keywords":"Computer science; Segmentation; Artificial intelligence; Computer vision; Pixel; Feature (linguistics); Class (philosophy); Object (grammar); Key (lock); Computation; Pattern recognition (psychology); Image segmentation","score_opus":0.0966125858195375,"score_gpt":0.351806053681381,"score_spread":0.2551934678618435,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323322511","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07712126,0.00047467215,0.91646945,0.00017259513,0.000069286216,0.00013199939,0.00064965937,0.0025527827,0.0023583253],"genre_scores_gemma":[0.61095804,0.00042597044,0.3742732,0.00019714703,0.00015863251,0.0001810331,0.0058146263,0.0007275256,0.0072638346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99904865,0.00013365278,0.000041723182,0.00038837967,0.00019448702,0.0001931136],"domain_scores_gemma":[0.99913764,0.00020106246,0.00011145779,0.00021779588,0.00025449545,0.00007747303],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007908873,0.0012623732,0.0017084228,0.0024654123,0.0009400477,0.0014423371,0.0016494517,0.0016922686,0.001998207],"category_scores_gemma":[0.0016895947,0.00049334794,0.0014706559,0.0016009042,0.00081226754,0.0018107385,0.0011654451,0.0013579238,0.0015963027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013512172,0.0006940707,0.0053223,0.00041646522,0.00021837985,0.00023812469,0.0002092839,0.09114067,0.12631497,0.007973228,0.008960814,0.75716054],"study_design_scores_gemma":[0.000014688606,0.00008420952,0.0023019628,0.000020296487,0.00004552039,0.00010916338,0.00008297104,0.9645845,0.024639824,0.0060534747,0.0020495316,0.000013858146],"about_ca_topic_score_codex":0.0072155995,"about_ca_topic_score_gemma":0.016157148,"teacher_disagreement_score":0.0072155995,"about_ca_system_score_codex":0.0009105231,"about_ca_system_score_gemma":0.0017427431,"threshold_uncertainty_score":0.014347196},"labels":[],"label_agreement":null},{"id":"W4323845265","doi":"10.18280/isi.280103","title":"A Study and Analysis on Pedestrian Detection and Tracking Through Rear-View Images","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian; Computer vision; Pedestrian detection; Tracking (education); Artificial intelligence; Computer science; Computer graphics (images); Transport engineering; Engineering; Psychology","score_opus":0.03885976331737833,"score_gpt":0.3053665935840855,"score_spread":0.2665068302667072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4323845265","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":[0.816774,0.0040864777,0.17173633,0.00014255199,0.00009720311,0.00010046968,0.0005082295,0.00029012986,0.006264576],"genre_scores_gemma":[0.9517185,0.0025976358,0.036630366,0.00004279274,0.00008613414,0.000033583383,0.0009681565,0.000060036717,0.007862823],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994349,0.00009132454,0.000032330936,0.00014698363,0.00023058035,0.00006399371],"domain_scores_gemma":[0.9980488,0.0009910224,0.00013943358,0.00019759535,0.0005704153,0.000052744053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065720914,0.00034660517,0.00034425684,0.0016357921,0.00038786084,0.00061101204,0.00039855693,0.000545548,0.0016920697],"category_scores_gemma":[0.0017246298,0.0001801652,0.000613011,0.0013551229,0.0002893026,0.0007382168,0.00014176265,0.00025232646,0.0006789882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014471929,0.00071275566,0.080850914,0.00090993324,0.0003227101,0.0015019837,0.0008359901,0.013879919,0.26486862,0.004254003,0.0029788667,0.6274372],"study_design_scores_gemma":[0.000034641453,0.0017267053,0.38120803,0.00012127769,0.0007454823,0.00566684,0.0009339845,0.32197973,0.2685192,0.0013045173,0.01762207,0.00013753326],"about_ca_topic_score_codex":0.006747604,"about_ca_topic_score_gemma":0.005278146,"teacher_disagreement_score":0.006747604,"about_ca_system_score_codex":0.0003136834,"about_ca_system_score_gemma":0.00030720956,"threshold_uncertainty_score":0.013416648},"labels":[],"label_agreement":null},{"id":"W4360584516","doi":"10.2316/j.2023.206-0889","title":"LOW-COMPLEXITY CHANNEL ESTIMATION AND MULTI-USER DETECTION IN MIMO-ENABLED UAV-ASSISTED MASSIVE IoT ACCESS, 231-240.","year":2023,"lang":"en","type":"article","venue":"International Journal of Robotics and Automation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Internet of Things; Computer science; Channel (broadcasting); MIMO; Computer network; Real-time computing; Embedded system","score_opus":0.055361461127967415,"score_gpt":0.3451197145959313,"score_spread":0.2897582534679639,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360584516","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06402445,0.002245968,0.92246467,0.00058286096,0.00030818247,0.00007610416,0.00020421298,0.00040632472,0.009687146],"genre_scores_gemma":[0.8033248,0.0018846238,0.18048617,0.00019686665,0.00026342462,0.000071907714,0.00024832648,0.0000628042,0.013461082],"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995944,0.00012284331,0.000016231816,0.0000611716,0.00012526163,0.00008021829],"domain_scores_gemma":[0.9992446,0.00046768878,0.000042705513,0.000061419625,0.00015176371,0.00003182412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006357327,0.0008124893,0.0005390268,0.00035602818,0.00053359667,0.00084317126,0.00049816445,0.000648309,0.0018234418],"category_scores_gemma":[0.0022091202,0.00045998898,0.00037722758,0.0004630571,0.0005588146,0.0010650936,0.000641182,0.00074126245,0.0007214569],"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":[0.0016674609,0.00037021164,0.007478381,0.00046149443,0.00024753495,0.0015747227,0.00054215983,0.46160564,0.078933984,0.048110012,0.012201462,0.38680694],"study_design_scores_gemma":[0.000013735504,0.0001321391,0.0013993282,0.000013424553,0.00002597644,0.00035307632,0.000089986854,0.9794662,0.011478085,0.0051282775,0.0018798477,0.000019905587],"about_ca_topic_score_codex":0.004634845,"about_ca_topic_score_gemma":0.013375194,"teacher_disagreement_score":0.004634845,"about_ca_system_score_codex":0.000479865,"about_ca_system_score_gemma":0.00074339285,"threshold_uncertainty_score":0.009215772},"labels":[],"label_agreement":null},{"id":"W4360764665","doi":"10.1109/ispa-bdcloud-socialcom-sustaincom57177.2022.00041","title":"Parallel Processing Techniques for Analyzing Large Video Files: a Deep Learning Based Approach","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; SPARK (programming language); Parallel processing; Leverage (statistics); Cloud computing; Data processing; Deep learning; Workload; Video processing; Stream processing; Executor; Artificial intelligence; Big data; Image processing; Real-time computing; Distributed computing; Data mining; Parallel computing; Database; Operating system; Image (mathematics)","score_opus":0.02900256954581462,"score_gpt":0.3029661494669383,"score_spread":0.27396357992112363,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4360764665","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":[0.01140047,0.00026007128,0.98522806,0.0002486718,0.00004425081,0.00007935031,0.000103767954,0.0016290162,0.0010063594],"genre_scores_gemma":[0.16021408,0.0006657819,0.83546287,0.00012743149,0.000086712804,0.00014960041,0.00045764982,0.0002403503,0.0025955408],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993413,0.000081170736,0.000055903027,0.00014911541,0.00028053127,0.00009201446],"domain_scores_gemma":[0.9986027,0.00046982753,0.00013092051,0.00027777103,0.00045483338,0.000063868996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010395097,0.001227387,0.0008805328,0.0016032998,0.00072420714,0.0011807819,0.0014975265,0.0006575515,0.0020068095],"category_scores_gemma":[0.0029692843,0.0005160548,0.00087040395,0.0021864593,0.0006732531,0.0018635197,0.0010579253,0.0017146864,0.00086193444],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00038668874,0.00034115394,0.0023762106,0.00021441912,0.00015555722,0.00025709864,0.00020147425,0.25060484,0.04208091,0.012951879,0.006343094,0.6840868],"study_design_scores_gemma":[0.000010394157,0.000034086104,0.00035806003,0.0000080237205,0.000013510138,0.000052898413,0.00003286339,0.9819763,0.007636036,0.008193938,0.0016733815,0.000010581136],"about_ca_topic_score_codex":0.010782447,"about_ca_topic_score_gemma":0.011966243,"teacher_disagreement_score":0.010782447,"about_ca_system_score_codex":0.0010479256,"about_ca_system_score_gemma":0.0016314262,"threshold_uncertainty_score":0.021439373},"labels":[],"label_agreement":null},{"id":"W4364297033","doi":"10.1109/aipr57179.2022.10092207","title":"An Intelligent Traffic Monitoring Embedded System using Video Data Mining","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"SAFER; Computer science; Installation; Workflow; Real-time computing; Structuring; Track (disk drive); Data mining; Database; Computer security","score_opus":0.15541006704335358,"score_gpt":0.3760287825069714,"score_spread":0.22061871546361783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4364297033","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":[0.10306701,0.00027936048,0.86082387,0.00031483566,0.0001634376,0.0004101187,0.0009878031,0.027377488,0.0065760226],"genre_scores_gemma":[0.6490444,0.0003571058,0.34088954,0.00030416396,0.000086542495,0.00032994556,0.0018935573,0.00036013563,0.006734523],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973613,0.000027906919,0.000020819987,0.00009073336,0.000099179124,0.000025213385],"domain_scores_gemma":[0.9996012,0.000083476785,0.000043713582,0.00006159174,0.00016545926,0.000044535896],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004026921,0.0005307126,0.0005176599,0.0009789678,0.00037156572,0.00071571686,0.00088194804,0.00042928153,0.0020533332],"category_scores_gemma":[0.0008898468,0.00020885005,0.00021552734,0.0006583052,0.00015808038,0.00082535786,0.0004850823,0.00038133044,0.0007255158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00092538807,0.0007321047,0.012117042,0.00023217067,0.00016015826,0.00060495344,0.00022312968,0.03768989,0.14098689,0.004133324,0.019987678,0.78220725],"study_design_scores_gemma":[0.00008833859,0.00033732908,0.0052874386,0.000045128356,0.000072951625,0.00043280586,0.00005129509,0.9112791,0.065377295,0.0023216768,0.014652354,0.00005436536],"about_ca_topic_score_codex":0.0026995519,"about_ca_topic_score_gemma":0.0021929792,"teacher_disagreement_score":0.0026995519,"about_ca_system_score_codex":0.0004617927,"about_ca_system_score_gemma":0.0005562619,"threshold_uncertainty_score":0.006869018},"labels":[],"label_agreement":null},{"id":"W4366378394","doi":"10.1109/tmm.2023.3268369","title":"Discriminative Identity-Feature Exploring and Differential Aware Learning for Unsupervised Person Re-Identification","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Mila - Quebec Artificial Intelligence Institute","funders":"Dalian Science and Technology Innovation Fund; Fundamental Research Funds for the Central Universities; Natural Science Foundation of Liaoning Province; National Natural Science Foundation of China","keywords":"Discriminative model; Computer science; Artificial intelligence; Salient; Pattern recognition (psychology); Redundancy (engineering); Machine learning; Feature learning; Robustness (evolution); Identification (biology)","score_opus":0.11069083483817996,"score_gpt":0.3299612520130516,"score_spread":0.21927041717487164,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366378394","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06406352,0.00059997523,0.9301018,0.00018468777,0.000057439556,0.00010468449,0.0002685662,0.0022936275,0.002325724],"genre_scores_gemma":[0.76692015,0.00039445632,0.22446997,0.00041787224,0.000091348615,0.00012268724,0.0016431644,0.00021568041,0.0057247346],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990772,0.00018075184,0.00002808933,0.00044009977,0.00015545923,0.000118358425],"domain_scores_gemma":[0.9991767,0.0002224733,0.00009172903,0.00031919294,0.0001351392,0.000054687625],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000789945,0.001023174,0.0011843471,0.0010844609,0.00041524155,0.0005277847,0.0021425793,0.00095489866,0.0014332584],"category_scores_gemma":[0.0021202243,0.0003660589,0.000979486,0.001239686,0.0006761163,0.001633428,0.0014440453,0.001386413,0.0010531748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00040446947,0.0005022866,0.0064755273,0.00016089912,0.000168313,0.0002497674,0.0002983291,0.08783882,0.020535534,0.005899981,0.00859906,0.86886704],"study_design_scores_gemma":[0.000015593701,0.00012843047,0.0020315002,0.0000136436665,0.0000449493,0.0002604784,0.00008664188,0.9773142,0.009313904,0.008278319,0.0024892443,0.000023107796],"about_ca_topic_score_codex":0.003175311,"about_ca_topic_score_gemma":0.0055148993,"teacher_disagreement_score":0.003175311,"about_ca_system_score_codex":0.0005891745,"about_ca_system_score_gemma":0.00060431386,"threshold_uncertainty_score":0.0063136816},"labels":[],"label_agreement":null},{"id":"W4366608144","doi":"10.1016/j.compag.2023.107839","title":"A semi-supervised generative adversarial network for amodal instance segmentation of piglets in farrowing pens","year":2023,"lang":"en","type":"article","venue":"Computers and Electronics in Agriculture","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"City University of Hong Kong","keywords":"Amodal perception; Artificial intelligence; Segmentation; Intersection (aeronautics); Computer science; Pattern recognition (psychology); Machine learning; Computer vision; Engineering","score_opus":0.016426599114441484,"score_gpt":0.2661724265751489,"score_spread":0.2497458274607074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366608144","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055152837,0.000596164,0.93992513,0.00031379363,0.00010023003,0.00007486107,0.0003286864,0.0020228697,0.0014855018],"genre_scores_gemma":[0.77367324,0.00045034746,0.2126375,0.00062929385,0.00013146848,0.00017450146,0.001964164,0.00040950536,0.009930053],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996076,0.00007538341,0.000011423798,0.00018401943,0.000048732585,0.00007281543],"domain_scores_gemma":[0.99938905,0.00035761495,0.000057318637,0.00007186125,0.000072540446,0.000051567815],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008727305,0.0010130034,0.001307911,0.0007003726,0.00039075338,0.0008286099,0.0022149223,0.0022461347,0.002184385],"category_scores_gemma":[0.0013608801,0.00085522037,0.0014142556,0.0006248528,0.0007688228,0.0007742171,0.0014727701,0.0020248736,0.001049531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043048017,0.00011319819,0.0014845851,0.000096184835,0.000091529175,0.00020997663,0.00010872593,0.8266198,0.012054046,0.0025072957,0.002738597,0.15354563],"study_design_scores_gemma":[0.0000027953588,0.000013990023,0.000109673165,0.0000036665888,0.0000043897053,0.000018322231,0.0000040015634,0.9984848,0.00062094425,0.00061938993,0.000114741764,0.0000033995693],"about_ca_topic_score_codex":0.0066741738,"about_ca_topic_score_gemma":0.007780879,"teacher_disagreement_score":0.0066741738,"about_ca_system_score_codex":0.00073456025,"about_ca_system_score_gemma":0.0007402894,"threshold_uncertainty_score":0.0132706165},"labels":[],"label_agreement":null},{"id":"W4366668996","doi":"10.1109/lra.2023.3269306","title":"Robots Autonomously Detecting People: A Multimodal Deep Contrastive Learning Method Robust to Intraclass Variations","year":2023,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Artificial intelligence; Computer science; Discriminative model; Deep learning; Computer vision; Robot; Pattern recognition (psychology); Invariant (physics); Machine learning; Mathematics","score_opus":0.018503124107269998,"score_gpt":0.2823260928955713,"score_spread":0.2638229687883013,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366668996","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07499212,0.0003198448,0.9205482,0.00021239657,0.000054643646,0.00005431244,0.00007618594,0.0014833589,0.002258928],"genre_scores_gemma":[0.7210929,0.00019091983,0.26781824,0.00047843115,0.00007404499,0.00009128838,0.00034199515,0.00016783217,0.009744436],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997421,0.00004345479,0.0000069421853,0.00010122522,0.000052865038,0.000053362397],"domain_scores_gemma":[0.9997781,0.000058639653,0.000035570716,0.00003819869,0.000057570374,0.00003205273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00057315594,0.0007020018,0.00052079966,0.0005536476,0.0002502347,0.00039510694,0.0013798164,0.00076665176,0.0014740153],"category_scores_gemma":[0.00095274346,0.0003206362,0.0005816072,0.00024774185,0.0005371517,0.00058394205,0.0010833467,0.00090711575,0.0005897645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037945167,0.00021839527,0.003112612,0.00007190102,0.00013394335,0.00022030663,0.00016219531,0.14533281,0.06667395,0.004836923,0.0046537924,0.7742037],"study_design_scores_gemma":[0.000010331355,0.00009045483,0.00071247434,0.000006094046,0.000018431616,0.000071633905,0.000013507305,0.98794866,0.008436506,0.0016817946,0.0010013984,0.0000086929385],"about_ca_topic_score_codex":0.0030236777,"about_ca_topic_score_gemma":0.0043731984,"teacher_disagreement_score":0.0030236777,"about_ca_system_score_codex":0.0005815715,"about_ca_system_score_gemma":0.00049862167,"threshold_uncertainty_score":0.0060121417},"labels":[],"label_agreement":null},{"id":"W4366810973","doi":"10.23977/jaip.2023.060204","title":"Improved Method for Pedestrian Recognition Based on Generative Adversarial Networks","year":2023,"lang":"en","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Pedestrian; Benchmark (surveying); Artificial intelligence; Train; Image (mathematics); Class (philosophy); Process (computing); Pedestrian detection; Feature (linguistics); Machine learning; Field (mathematics); Pattern recognition (psychology); Generative grammar; Data mining; Computer vision; Engineering; Mathematics","score_opus":0.12444374033177845,"score_gpt":0.4114869856206453,"score_spread":0.2870432452888668,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4366810973","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":[0.007104484,0.00026030964,0.98893285,0.00010675589,0.00013307708,0.00004507969,0.00008535235,0.0020033307,0.0013287715],"genre_scores_gemma":[0.4584716,0.0006666287,0.5205955,0.0005632042,0.0002565512,0.00017964892,0.0013069414,0.00048376134,0.017476209],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99909544,0.00020697984,0.000033058015,0.0002914936,0.0002520671,0.00012099803],"domain_scores_gemma":[0.9995148,0.00012319416,0.000041028954,0.00012052251,0.00016111492,0.000039439747],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010319975,0.0011655954,0.0013233545,0.0010855524,0.00037294618,0.00067745533,0.0016362345,0.0009205329,0.0035873144],"category_scores_gemma":[0.0013204191,0.00055817846,0.0015675833,0.0006605243,0.00046646103,0.00093114167,0.0012610892,0.0016833296,0.001999042],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039139178,0.00014546163,0.0025726093,0.00010650922,0.00021074271,0.00029184154,0.00010384972,0.30595872,0.016879825,0.00826938,0.010912188,0.6541575],"study_design_scores_gemma":[0.000006007079,0.000020797728,0.000261999,0.00000466095,0.000014829062,0.00011023645,0.0000055465343,0.99374795,0.0034370886,0.0013573074,0.0010240537,0.000009484537],"about_ca_topic_score_codex":0.0048000435,"about_ca_topic_score_gemma":0.004344025,"teacher_disagreement_score":0.0048000435,"about_ca_system_score_codex":0.00063507474,"about_ca_system_score_gemma":0.0007824607,"threshold_uncertainty_score":0.01200074},"labels":[],"label_agreement":null},{"id":"W4367059291","doi":"10.1523/eneuro.0127-22.2023","title":"PyMouseTracks: Flexible Computer Vision and RFID-Based System for Multiple Mouse Tracking and Behavioral Assessment","year":2023,"lang":"en","type":"article","venue":"eNeuro","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Fondation Leducq; Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Heart and Stroke Foundation of Canada","keywords":"Computer vision; Computer science; Tracking (education); Artificial intelligence; Tracking system; Human–computer interaction; Psychology; Kalman filter","score_opus":0.05587938742423252,"score_gpt":0.364067591578036,"score_spread":0.30818820415380344,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4367059291","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.071487784,0.0006086123,0.87742513,0.00013326031,0.0002101724,0.00072622485,0.00312595,0.042804707,0.003478112],"genre_scores_gemma":[0.3091636,0.00079522823,0.66380894,0.0005902982,0.00011406266,0.0028635012,0.006501175,0.0023756712,0.013787541],"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994875,0.000035118344,0.00002965287,0.0001901243,0.00021305219,0.0000444799],"domain_scores_gemma":[0.9995372,0.00008761099,0.00009444016,0.00008528898,0.00011152385,0.00008384147],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00075705047,0.0007507713,0.0007169593,0.0009927592,0.00021595294,0.00046647925,0.0014900564,0.0006453155,0.0056522656],"category_scores_gemma":[0.0009574216,0.0003941044,0.0004948994,0.00037061176,0.00027698217,0.00072838285,0.0011306179,0.00068642554,0.0019126968],"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":[0.00092472485,0.00026934946,0.0057945615,0.00047373935,0.00012917045,0.00027863408,0.00018995738,0.0017222484,0.6697233,0.0010669616,0.019557854,0.29986945],"study_design_scores_gemma":[0.00050161005,0.0033318829,0.061936527,0.0002564513,0.000377214,0.0041938475,0.00014329504,0.16308968,0.62635857,0.002474349,0.1367555,0.0005810688],"about_ca_topic_score_codex":0.0011659091,"about_ca_topic_score_gemma":0.0021500303,"teacher_disagreement_score":0.0056522656,"about_ca_system_score_codex":0.00033925683,"about_ca_system_score_gemma":0.0005811955,"threshold_uncertainty_score":0.01890874},"labels":[],"label_agreement":null},{"id":"W4372340837","doi":"10.1109/icassp49357.2023.10095618","title":"Joint Robust Representation And Generalization Enhancement For Cross-Modality Person Re-Identification","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Nature; National Natural Science Foundation of China; Ministry of Natural Resources","keywords":"Generalization; Modality (human–computer interaction); Computer science; Representation (politics); Joint (building); Artificial intelligence; Identification (biology); Pattern recognition (psychology); Mathematics; Engineering; Political science","score_opus":0.17935195999078637,"score_gpt":0.39303992203454235,"score_spread":0.21368796204375598,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4372340837","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023125356,0.00038086015,0.9729331,0.0001346498,0.000077326855,0.0000516241,0.00013298061,0.0021800355,0.0009840144],"genre_scores_gemma":[0.5572881,0.000704291,0.4311155,0.00058340176,0.0001912951,0.00018091191,0.0016786852,0.00054531713,0.0077124876],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99865353,0.00026642033,0.000058630812,0.0005693013,0.0002706938,0.00018145812],"domain_scores_gemma":[0.99859804,0.00022419693,0.000170155,0.00064544735,0.0002966679,0.00006554888],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018072409,0.0014143727,0.0015120726,0.00092988275,0.00046052047,0.0006771161,0.0019807336,0.0014512138,0.0020284043],"category_scores_gemma":[0.0036000777,0.00048799271,0.0019517146,0.0010194824,0.0007002665,0.002324782,0.0022961362,0.0023244338,0.0015462752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046209924,0.00034381935,0.0027800438,0.0001360788,0.00029955083,0.0002568408,0.00022091139,0.12262648,0.04977684,0.0053355815,0.009509893,0.808252],"study_design_scores_gemma":[0.00001239122,0.00013363999,0.0017739366,0.000015110583,0.00006967055,0.0002732706,0.00004896268,0.96984595,0.020399025,0.004541358,0.0028435532,0.000043079515],"about_ca_topic_score_codex":0.0036069038,"about_ca_topic_score_gemma":0.0036992144,"teacher_disagreement_score":0.0036069038,"about_ca_system_score_codex":0.0005067584,"about_ca_system_score_gemma":0.0008075124,"threshold_uncertainty_score":0.009557724},"labels":[],"label_agreement":null},{"id":"W4376638861","doi":"10.18280/isi.280226","title":"Camshift Algorithm with GOA-Neural Network for Drone Object Tracking","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Drone; Artificial intelligence; Computer science; Computer vision; Artificial neural network; Object (grammar); Tracking (education); Video tracking; Algorithm; Pattern recognition (psychology); Psychology","score_opus":0.0242547180263369,"score_gpt":0.2680701575781636,"score_spread":0.24381543955182672,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4376638861","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06410069,0.0015883903,0.92435944,0.00025648478,0.00024568284,0.00010879633,0.000118170086,0.003966576,0.005255757],"genre_scores_gemma":[0.69968617,0.00060201855,0.28974423,0.00029377363,0.000095236726,0.00017272054,0.0004228388,0.00013605859,0.008846948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981314,0.000014726582,0.00000940072,0.000071023365,0.00006283386,0.000028876864],"domain_scores_gemma":[0.99984455,0.00003823666,0.000016653461,0.000022016724,0.00006788959,0.0000105227355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00034178328,0.00057318,0.00061520387,0.00072769664,0.00042903543,0.0004995501,0.0012852234,0.00083559053,0.0023636196],"category_scores_gemma":[0.0008401217,0.0002508582,0.00039234126,0.0006535997,0.00029127035,0.0006066996,0.0005555263,0.0007390935,0.0007060657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022383893,0.00012804731,0.0015641274,0.000090568225,0.00005328996,0.00008284197,0.00008108223,0.17967029,0.022944622,0.0027465168,0.0031346665,0.78928006],"study_design_scores_gemma":[0.0000068981362,0.000030200099,0.0004677512,0.000005877448,0.000008098913,0.000020492585,0.000006891206,0.9949232,0.003123718,0.00050738745,0.00089406245,0.00000553856],"about_ca_topic_score_codex":0.015839117,"about_ca_topic_score_gemma":0.016143797,"teacher_disagreement_score":0.015839117,"about_ca_system_score_codex":0.0006946192,"about_ca_system_score_gemma":0.0006764305,"threshold_uncertainty_score":0.031493843},"labels":[],"label_agreement":null},{"id":"W4377028640","doi":"10.1177/03611981231165997","title":"Vision-Based Work Zone Safety Alert System in a Connected Vehicle Environment","year":2023,"lang":"en","type":"article","venue":"Transportation Research Record Journal of the Transportation Research Board","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto; Spinal Cord Injury Alberta; University of Alberta","funders":"","keywords":"Work zone; Global Positioning System; Monocular vision; Computer science; Work (physics); Real-time computing; Latency (audio); Monocular; Mobile device; Computer vision; Simulation; Engineering; Telecommunications","score_opus":0.07661056564289163,"score_gpt":0.3734078066858505,"score_spread":0.29679724104295885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377028640","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.237428,0.00020830972,0.7483555,0.00018710089,0.00013112919,0.00023189302,0.00016941888,0.008675062,0.0046135867],"genre_scores_gemma":[0.86206263,0.00011818673,0.13425137,0.00014266635,0.00002839859,0.000088518784,0.00021318311,0.00007413237,0.003020971],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997868,0.000022993137,0.000008277538,0.00006636055,0.00008171756,0.00003392903],"domain_scores_gemma":[0.999708,0.000038366725,0.000034940735,0.000036852434,0.00014065842,0.00004121778],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021945794,0.00036219537,0.00034251754,0.0005395025,0.0003102509,0.0004748577,0.0007171189,0.0005815812,0.0014389189],"category_scores_gemma":[0.00080508564,0.00018288466,0.00019680579,0.00021764734,0.00018885056,0.00052709144,0.0006066438,0.00040058818,0.00075055356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012876622,0.0006728079,0.008230501,0.00019835883,0.00008932782,0.00079832337,0.00056876824,0.05831209,0.28248686,0.002069418,0.010262272,0.6350236],"study_design_scores_gemma":[0.00009440451,0.00061970914,0.00876147,0.000030500187,0.00006249906,0.0004815243,0.00019760059,0.8844954,0.099305004,0.0010580224,0.0048451875,0.00004873219],"about_ca_topic_score_codex":0.0041904715,"about_ca_topic_score_gemma":0.004419356,"teacher_disagreement_score":0.0041904715,"about_ca_system_score_codex":0.00045568324,"about_ca_system_score_gemma":0.00070672476,"threshold_uncertainty_score":0.008332193},"labels":[],"label_agreement":null},{"id":"W4377234492","doi":"10.18280/ts.400205","title":"A New Automatic Vehicle Tracking and Detection Algorithm for Multi-Traffic Video Cameras","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Computer vision; Tracking (education); Artificial intelligence; Video tracking; Algorithm; Video processing","score_opus":0.04716674370462887,"score_gpt":0.30853645947199465,"score_spread":0.2613697157673658,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377234492","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016691739,0.0005912647,0.97784996,0.00009296228,0.00016701133,0.000097398915,0.00015336875,0.002716923,0.0016392934],"genre_scores_gemma":[0.13764049,0.0005318476,0.85042644,0.0001771577,0.0000797126,0.0001593204,0.0008639187,0.00013532015,0.009985799],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994454,0.000027840275,0.000028084141,0.0001996746,0.0002393987,0.000059634025],"domain_scores_gemma":[0.99970156,0.000032811644,0.000033801705,0.000036865236,0.00017479052,0.000020059326],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005669184,0.0007854294,0.0007342975,0.0016903124,0.0005047341,0.0007383033,0.0011901876,0.0010146318,0.0019042323],"category_scores_gemma":[0.0007612101,0.00045454098,0.0006862656,0.0009762462,0.00024658174,0.0012007281,0.0005849718,0.001084104,0.0012979166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015824355,0.000108539636,0.0015033983,0.00006269225,0.0000783473,0.00006341612,0.000039740997,0.016263567,0.043209713,0.0018694737,0.004591928,0.932051],"study_design_scores_gemma":[0.00003498718,0.00012796264,0.0034027526,0.000023007491,0.00004994191,0.000303038,0.000022424085,0.94129974,0.042728372,0.0010531881,0.010920339,0.00003425665],"about_ca_topic_score_codex":0.010474383,"about_ca_topic_score_gemma":0.015744964,"teacher_disagreement_score":0.010474383,"about_ca_system_score_codex":0.0008988908,"about_ca_system_score_gemma":0.0010444397,"threshold_uncertainty_score":0.020826817},"labels":[],"label_agreement":null},{"id":"W4377832632","doi":"10.18280/ts.400239","title":"NAM-YOLOV7: An Improved YOLOv7 Based on Attention Model for Animal Death Detection","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Environmental science","score_opus":0.06244350257705562,"score_gpt":0.3153146872862343,"score_spread":0.2528711847091787,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4377832632","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.36745554,0.012058864,0.5576785,0.0019522213,0.001972379,0.00074360525,0.00876126,0.031412866,0.01796467],"genre_scores_gemma":[0.7797949,0.0016955942,0.16495772,0.0016282577,0.00036685917,0.00045127526,0.021897731,0.0007636809,0.02844413],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996623,0.00006275773,0.000013607446,0.00014537509,0.00005298442,0.00006298438],"domain_scores_gemma":[0.99955803,0.00015132478,0.000031331772,0.000055350432,0.00016034441,0.000043504628],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008368471,0.0014158788,0.0012500266,0.0009889025,0.00036745114,0.00077637983,0.0025244192,0.0014394925,0.0032543114],"category_scores_gemma":[0.0015094473,0.00040355674,0.0013376214,0.0005200055,0.00032498845,0.0011645989,0.0008452475,0.0014919278,0.0019357207],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001650154,0.0010255488,0.015204792,0.00072475715,0.000623577,0.00040140277,0.00015933401,0.2746607,0.028661437,0.0034353957,0.0636319,0.609821],"study_design_scores_gemma":[0.000042018386,0.00024371721,0.0019583518,0.000027985849,0.00006268728,0.00007954754,0.000021406582,0.9911908,0.0028362037,0.0007761014,0.0027434481,0.000017729391],"about_ca_topic_score_codex":0.02387778,"about_ca_topic_score_gemma":0.038188808,"teacher_disagreement_score":0.02387778,"about_ca_system_score_codex":0.0010615335,"about_ca_system_score_gemma":0.0012469331,"threshold_uncertainty_score":0.047477543},"labels":[],"label_agreement":null},{"id":"W4378085613","doi":"10.1002/cav.2163","title":"RAIF: A deep learning‐based architecture for multi‐modal aesthetic biometric system","year":2023,"lang":"en","type":"article","venue":"Computer Animation and Virtual Worlds","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Biometrics; Artificial intelligence; Audio visual; Deep learning; Merge (version control); Modal; Architecture; Domain (mathematical analysis); Human–computer interaction; Speech recognition; Computer vision; Multimedia; Information retrieval","score_opus":0.03941341375078661,"score_gpt":0.307352421743952,"score_spread":0.2679390079931654,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378085613","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03298555,0.0009530704,0.95318377,0.00031370562,0.00014492817,0.000103089274,0.00040683788,0.008109199,0.0037998282],"genre_scores_gemma":[0.65782154,0.00054815446,0.3244794,0.00071713346,0.00010180757,0.0002174948,0.001405571,0.00018402457,0.014524904],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99963045,0.000055078668,0.000016210897,0.0001202164,0.00010348004,0.00007462207],"domain_scores_gemma":[0.9997832,0.000033270448,0.000023125485,0.00003326508,0.00010883397,0.000018265351],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008921543,0.00077035226,0.0005983325,0.00076424354,0.0003240581,0.00050843385,0.0015724428,0.0010051262,0.0037356243],"category_scores_gemma":[0.0008090521,0.00033829277,0.00064556405,0.00051009236,0.000319832,0.0008702206,0.000776821,0.001004059,0.0018072822],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004805029,0.00032488786,0.0024366882,0.00012006194,0.0001961297,0.0001495947,0.00006953364,0.11671977,0.046225328,0.0033563485,0.013486238,0.81643486],"study_design_scores_gemma":[0.000009994734,0.000097930984,0.0007819345,0.00001099598,0.000021816915,0.00008199501,0.000009888454,0.98575956,0.009872638,0.0009550363,0.0023795406,0.00001877476],"about_ca_topic_score_codex":0.008197092,"about_ca_topic_score_gemma":0.008455443,"teacher_disagreement_score":0.008197092,"about_ca_system_score_codex":0.0008718819,"about_ca_system_score_gemma":0.00070451206,"threshold_uncertainty_score":0.01629877},"labels":[],"label_agreement":null},{"id":"W4378416660","doi":"10.1007/s11042-023-15235-x","title":"Reliable interconnected channels for dynamic DCF based visual tracking","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Discriminative model; Computer science; BitTorrent tracker; Channel (broadcasting); Reliability (semiconductor); Filter (signal processing); Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Eye tracking; Computer vision; Computer network","score_opus":0.047079649099592634,"score_gpt":0.3438550237133782,"score_spread":0.2967753746137856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4378416660","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014887214,0.00023861906,0.98166037,0.00007631133,0.000058790152,0.000039349605,0.000076058925,0.0007210027,0.00224224],"genre_scores_gemma":[0.7995001,0.00034533572,0.1932339,0.00010827864,0.000096945085,0.00017480379,0.00021937587,0.0001415086,0.0061797425],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99927956,0.00012861728,0.00002835915,0.00019808463,0.00024488498,0.00012044072],"domain_scores_gemma":[0.9979431,0.00085090735,0.00015618293,0.00045698808,0.00052426534,0.00006850086],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007192937,0.00062005996,0.00073426176,0.0008926662,0.000999663,0.0014414033,0.0010439716,0.0010813593,0.004480504],"category_scores_gemma":[0.003239552,0.00036314063,0.0002559957,0.000995221,0.0009129655,0.0017581701,0.0013611335,0.00097417383,0.00095659867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013269155,0.0003133867,0.0020916243,0.0002642083,0.00006160342,0.0003753693,0.00054599193,0.25096625,0.08601351,0.07120093,0.009161154,0.5776791],"study_design_scores_gemma":[0.000032377928,0.000115337614,0.00036916693,0.000023909004,0.000022142538,0.00010665632,0.00004702324,0.96293145,0.01833436,0.012346078,0.005640941,0.000030575564],"about_ca_topic_score_codex":0.003647255,"about_ca_topic_score_gemma":0.004662265,"teacher_disagreement_score":0.004480504,"about_ca_system_score_codex":0.0010341307,"about_ca_system_score_gemma":0.0008673485,"threshold_uncertainty_score":0.01498878},"labels":[],"label_agreement":null},{"id":"W4379116992","doi":"10.1007/978-3-031-35501-1_11","title":"Real Time Detection and Tracking in Multi Speakers Video Conferencing","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"","keywords":"Videoconferencing; Computer science; Tracking (education); Convolutional neural network; Artificial intelligence; Window (computing); Sliding window protocol; Computer vision; Real-time computing; Multimedia; World Wide Web; Psychology","score_opus":0.040649758260345455,"score_gpt":0.27668028122752786,"score_spread":0.2360305229671824,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379116992","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":[0.043826446,0.0014606613,0.9482891,0.00008822729,0.00013172608,0.000031894982,0.000052435153,0.0006216436,0.0054977783],"genre_scores_gemma":[0.664453,0.0019087752,0.29780048,0.00008601907,0.00015949964,0.00007793983,0.00017827137,0.00014741995,0.03518852],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995782,0.0000904381,0.000017514078,0.00011455618,0.00015248258,0.000046806494],"domain_scores_gemma":[0.99945563,0.00034210252,0.000037515267,0.000052482086,0.0000896973,0.000022569551],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005075456,0.00042124648,0.0005415048,0.00051872403,0.00029102413,0.00086070254,0.0009995345,0.0009978014,0.0021095553],"category_scores_gemma":[0.0014026152,0.00043168233,0.00035582934,0.0005581059,0.0003661772,0.00084963493,0.00063399284,0.0007180599,0.0008252485],"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":[0.00045106714,0.00015190356,0.0013993912,0.0001619865,0.00005915156,0.0002412517,0.00023346575,0.1212017,0.1706234,0.015642582,0.0029043884,0.68692976],"study_design_scores_gemma":[0.000010069615,0.00015530856,0.0021202946,0.000018237266,0.000032375057,0.0004556359,0.000051046612,0.94246155,0.046689276,0.0043776915,0.0035986374,0.000029818986],"about_ca_topic_score_codex":0.0022454818,"about_ca_topic_score_gemma":0.002277582,"teacher_disagreement_score":0.0022454818,"about_ca_system_score_codex":0.00039653364,"about_ca_system_score_gemma":0.0002600711,"threshold_uncertainty_score":0.00705719},"labels":[],"label_agreement":null},{"id":"W4379523227","doi":"10.21428/594757db.f0bc10fd","title":"Quantifying Path Smoothness in Video Object Tracking by Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; Royal Military College of Canada; Queen's University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Smoothness; Object detection; Video tracking; Minimum bounding box; Smoothing; Object (grammar); Tracking (education); Bounding overwatch; Jerk; Acceleration; Pattern recognition (psychology); Mathematics; Image (mathematics)","score_opus":0.06202583762709948,"score_gpt":0.3298718394588258,"score_spread":0.26784600183172635,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4379523227","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.20447339,0.0020399985,0.779838,0.00030397225,0.00013915391,0.00024060864,0.0027023468,0.008192499,0.0020699208],"genre_scores_gemma":[0.5240726,0.0006630045,0.4647284,0.0001443449,0.000058582216,0.00015831643,0.008233457,0.00079619134,0.0011451835],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978509,0.0003494646,0.00015879565,0.00076851965,0.00071848027,0.00015387085],"domain_scores_gemma":[0.9942821,0.0031493222,0.00086636964,0.00081737345,0.00074297143,0.00014196],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0033998229,0.0012877813,0.00096403545,0.0034968841,0.0005498758,0.0015738859,0.0014632667,0.0013026487,0.0009908222],"category_scores_gemma":[0.02064367,0.0005305237,0.00066993246,0.0032204492,0.0010897903,0.0022215806,0.0013169083,0.0011365148,0.00043222768],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010866071,0.00020739493,0.02226423,0.00051783136,0.00029470524,0.00017391772,0.00023458492,0.59065247,0.027245397,0.008916824,0.0075350488,0.34087098],"study_design_scores_gemma":[0.000043336713,0.00019922457,0.01136107,0.000042142045,0.000048752383,0.00023357554,0.00004963075,0.95986384,0.017163033,0.007996023,0.0029439933,0.000055354434],"about_ca_topic_score_codex":0.014144983,"about_ca_topic_score_gemma":0.013020785,"teacher_disagreement_score":0.014144983,"about_ca_system_score_codex":0.0014807105,"about_ca_system_score_gemma":0.0013486499,"threshold_uncertainty_score":0.028125286},"labels":[],"label_agreement":null},{"id":"W4381194700","doi":"10.1016/j.asoc.2023.110554","title":"A synergy of the adaptive whale optimization algorithm and differential evolution for abrupt motion tracking","year":2023,"lang":"en","type":"article","venue":"Applied Soft Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"Science and Technology Innovation Talents in Universities of Henan Province; Henan Province University Innovation Talents Support Program; Natural Science Foundation of Henan Province; National Natural Science Foundation of China","keywords":"BitTorrent tracker; Benchmark (surveying); Computer science; Differential evolution; Tracking (education); Population; Optimization problem; Optimization algorithm; Artificial intelligence; Algorithm; Mathematical optimization; Eye tracking; Mathematics","score_opus":0.021136254603742542,"score_gpt":0.2577439394291503,"score_spread":0.23660768482540775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4381194700","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007268641,0.000126901,0.9911,0.00010222798,0.00006760596,0.000015183187,0.0000067003025,0.000084537685,0.0012282542],"genre_scores_gemma":[0.45926026,0.00030128952,0.53349596,0.00024906668,0.00014267549,0.0001515462,0.000073614705,0.00015690413,0.0061686235],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996978,0.00009540618,0.000018259156,0.00005695345,0.0001076034,0.000023992146],"domain_scores_gemma":[0.9994137,0.00031939778,0.000032136715,0.00005485398,0.00013510902,0.00004477367],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00095964264,0.0004559105,0.0010109219,0.00063524966,0.0003846199,0.000597517,0.0010832703,0.0010287052,0.0013686514],"category_scores_gemma":[0.0025669655,0.00036443945,0.000638932,0.00066821655,0.0006257011,0.0009279069,0.0016784703,0.00080985733,0.00034865027],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00013380394,0.00011339712,0.0013631982,0.00012645718,0.00016328903,0.00011802887,0.00013648828,0.72895116,0.010469627,0.05366548,0.0017437374,0.20301525],"study_design_scores_gemma":[0.0000031690906,0.000013067708,0.00005105769,0.0000019021645,0.0000043079535,0.000010064053,0.0000020420953,0.997626,0.00023541904,0.001723871,0.0003255817,0.0000034379066],"about_ca_topic_score_codex":0.0030463992,"about_ca_topic_score_gemma":0.0026587571,"teacher_disagreement_score":0.0030463992,"about_ca_system_score_codex":0.0003457524,"about_ca_system_score_gemma":0.00068633753,"threshold_uncertainty_score":0.006057322},"labels":[],"label_agreement":null},{"id":"W4385078389","doi":"10.18280/isi.280318","title":"Real-Time Person Re-Identification Using Omni-Scale Feature Learning Network and Yolov5: A Comparative Study","year":2023,"lang":"en","type":"article","venue":"Ingénierie des systèmes d information","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Feature (linguistics); Computer science; Identification (biology); Scale (ratio); Artificial intelligence; Machine learning; Geography; Cartography","score_opus":0.045048445055090786,"score_gpt":0.3054127060371304,"score_spread":0.2603642609820396,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385078389","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":[0.88032746,0.0027535085,0.107257836,0.00028309386,0.00036084227,0.00016557504,0.00062486646,0.0018801274,0.0063467645],"genre_scores_gemma":[0.96804917,0.00044565127,0.027861616,0.000052422318,0.000040495073,0.00003043436,0.0013141619,0.000038457147,0.002167621],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9989317,0.00026001997,0.0000602398,0.0003232001,0.00026585182,0.00015885035],"domain_scores_gemma":[0.9989864,0.00032183918,0.000115169394,0.00021048255,0.00031462105,0.000051494415],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018940738,0.00069771084,0.00066824775,0.0012271467,0.00031411168,0.0005744485,0.0010151104,0.0007062272,0.00089890964],"category_scores_gemma":[0.0036367313,0.00014329265,0.00037964975,0.0007103,0.00030959083,0.0014123088,0.0006249707,0.00040336733,0.00042968828],"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":[0.0021202704,0.00076551386,0.032137092,0.00040220775,0.00035694378,0.00035957116,0.00019457121,0.17565715,0.0125380345,0.0016502441,0.0059745973,0.76784384],"study_design_scores_gemma":[0.00001492996,0.00042929774,0.014549683,0.000021499673,0.000057408073,0.00020606497,0.00012835722,0.9740276,0.008613396,0.00039793886,0.0015293177,0.000024511004],"about_ca_topic_score_codex":0.011837398,"about_ca_topic_score_gemma":0.008238805,"teacher_disagreement_score":0.011837398,"about_ca_system_score_codex":0.00082136947,"about_ca_system_score_gemma":0.00042162053,"threshold_uncertainty_score":0.02353698},"labels":[],"label_agreement":null},{"id":"W4385388133","doi":"10.18280/ria.370304","title":"Background-Foreground Segmentation Using Multi-Scale Attention Net (MA-Net): A Deep Learning Approach","year":2023,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Net (polyhedron); Artificial intelligence; Deep learning; Segmentation; Computer science; Scale (ratio); Pattern recognition (psychology); Machine learning; Mathematics; Geography; Cartography","score_opus":0.11341392926913177,"score_gpt":0.34344947785635666,"score_spread":0.2300355485872249,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385388133","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":[0.023006141,0.0009940546,0.9688746,0.00030518067,0.000104245446,0.00007191479,0.00020486634,0.0035329552,0.00290613],"genre_scores_gemma":[0.44120902,0.0011148821,0.5437776,0.000758995,0.00019932291,0.00012766986,0.0016298846,0.0005414966,0.010641163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967444,0.00003198767,0.000014350684,0.00012464578,0.000075387085,0.00007915362],"domain_scores_gemma":[0.9997358,0.00007104401,0.000034599925,0.000037633585,0.00008852471,0.0000324623],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00063837564,0.0014238309,0.001073112,0.0016301732,0.0005027065,0.0012560975,0.0018545293,0.001447544,0.0020795441],"category_scores_gemma":[0.00085625995,0.0006577809,0.0012823726,0.001236879,0.000531572,0.0015248758,0.0014483421,0.001608053,0.0007928212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032933537,0.0001801031,0.0017546847,0.00016001798,0.00019961603,0.00021371705,0.00012970286,0.22989419,0.035394836,0.008029144,0.00709864,0.71661603],"study_design_scores_gemma":[0.0000072779458,0.00002969137,0.00051637413,0.000011367724,0.000031594333,0.00004319764,0.000012480384,0.9881677,0.0067141037,0.0030534694,0.0014037032,0.0000089931855],"about_ca_topic_score_codex":0.012386952,"about_ca_topic_score_gemma":0.018864244,"teacher_disagreement_score":0.012386952,"about_ca_system_score_codex":0.0015879008,"about_ca_system_score_gemma":0.0013208677,"threshold_uncertainty_score":0},"labels":[{"model":"gpt","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"high"},{"model":"grok","categories":[],"domain":null,"study_design":"bench_or_experimental","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"},{"model":"opus","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"methods","about_ca_system":false,"about_ca_topic":false,"confidence":"medium"}],"label_agreement":"split"},{"id":"W4385462424","doi":"10.1016/j.imavis.2023.104791","title":"Context-aware and part alignment for visible-infrared person re-identification","year":2023,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Six Talent Peaks Project in Jiangsu Province; Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Six Talent Climax Foundation of Jiangsu","keywords":"Computer science; Discriminative model; Artificial intelligence; Pattern recognition (psychology); Discriminant; Feature learning; Graph; Convolutional neural network; Feature extraction; Transformer; Machine learning; Theoretical computer science","score_opus":0.04917918406279083,"score_gpt":0.36698598589628145,"score_spread":0.31780680183349064,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385462424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04037034,0.0011445176,0.95227784,0.00008609218,0.0003091789,0.00007147646,0.00032975906,0.0032314388,0.0021792732],"genre_scores_gemma":[0.48044467,0.0007970989,0.50749177,0.00021942946,0.00018890276,0.00010896666,0.0015469801,0.0005225446,0.008679634],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990947,0.00010795948,0.000038682705,0.00036575992,0.0002448509,0.00014799381],"domain_scores_gemma":[0.9995065,0.00005849762,0.000047235084,0.00021556295,0.00013538249,0.000036879595],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042090833,0.0010917645,0.0015726801,0.0012630618,0.00064173434,0.0007889456,0.0012914767,0.00088790176,0.0031141436],"category_scores_gemma":[0.0012375149,0.00054248684,0.0010504554,0.0013701479,0.00034318416,0.0012362595,0.0017284852,0.0009935948,0.0031011072],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007499431,0.00027140786,0.0026516053,0.00012762066,0.00013561072,0.00020749161,0.0001462097,0.021408878,0.10933798,0.0018508116,0.006588683,0.8565237],"study_design_scores_gemma":[0.000027112352,0.0003094446,0.010865516,0.000038070048,0.00012554832,0.00081370963,0.00019641433,0.88533175,0.08699838,0.005866481,0.009362529,0.00006491224],"about_ca_topic_score_codex":0.0040381234,"about_ca_topic_score_gemma":0.009210489,"teacher_disagreement_score":0.0040381234,"about_ca_system_score_codex":0.00028310737,"about_ca_system_score_gemma":0.00072573015,"threshold_uncertainty_score":0.010417819},"labels":[],"label_agreement":null},{"id":"W4385494459","doi":"10.1007/s13042-023-01927-1","title":"An improved deep network-based RGB-D semantic segmentation method for indoor scenes","year":2023,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; RGB color model; Segmentation; Feature (linguistics); Computer vision; Pattern recognition (psychology); Feature extraction; Convolutional neural network; Image segmentation; Semantics (computer science)","score_opus":0.015257356060375125,"score_gpt":0.34590642378309205,"score_spread":0.3306490677227169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385494459","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.012511359,0.00043003043,0.98032403,0.00011140776,0.0001175858,0.00006413199,0.00054592616,0.003766239,0.0021294828],"genre_scores_gemma":[0.22578825,0.00087332306,0.7525452,0.00044592711,0.00011934862,0.00017918622,0.0041480963,0.00083369674,0.015067007],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99964976,0.000021375834,0.000014747784,0.00012802515,0.00011828189,0.00006773017],"domain_scores_gemma":[0.99983716,0.00002019155,0.00001546667,0.00003250642,0.00007705846,0.000017528911],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002984136,0.0013954997,0.0012124983,0.0014782548,0.00044174975,0.0008465613,0.0016570227,0.0010257566,0.004260071],"category_scores_gemma":[0.00045367412,0.0006942929,0.0012357433,0.0014551982,0.00033651604,0.0010274638,0.0011341658,0.0011253383,0.0026646],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026618422,0.00021023354,0.001085268,0.00016233415,0.00013659963,0.00008177377,0.000066422224,0.08491371,0.052616555,0.003571229,0.009742553,0.8471472],"study_design_scores_gemma":[0.000010500922,0.0000321115,0.00075392687,0.0000147125165,0.000031673197,0.00006750836,0.000016946604,0.9808126,0.0131718805,0.0017938913,0.0032766538,0.000017558219],"about_ca_topic_score_codex":0.019900333,"about_ca_topic_score_gemma":0.03523087,"teacher_disagreement_score":0.019900333,"about_ca_system_score_codex":0.00081510295,"about_ca_system_score_gemma":0.0017534532,"threshold_uncertainty_score":0.03956896},"labels":[],"label_agreement":null},{"id":"W4385690081","doi":"10.1155/2023/9869015","title":"Retracted: A Novel Traffic Surveillance System Using an Uncalibrated Camera","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":true,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Transport engineering; Engineering","score_opus":0.03687441282670619,"score_gpt":0.30519753786256826,"score_spread":0.2683231250358621,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385690081","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":[0.5191362,0.0014496361,0.44370893,0.0008050358,0.0017741133,0.00061146053,0.0010904424,0.01708605,0.014338093],"genre_scores_gemma":[0.7849619,0.00042250528,0.19895709,0.00076975673,0.00027362577,0.00015186015,0.0009717191,0.00031618486,0.013175412],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995092,0.000050069022,0.000020497027,0.00018143565,0.00016402165,0.00007472624],"domain_scores_gemma":[0.9994734,0.000042756783,0.000033417167,0.00009776844,0.00022683856,0.0001258642],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00040288235,0.0009419988,0.0011159308,0.00092728465,0.0005747895,0.00092206907,0.0017408702,0.0012129996,0.0028061233],"category_scores_gemma":[0.0007948436,0.00045882937,0.00036550235,0.0004474404,0.00030129062,0.000935237,0.0013240044,0.0010384322,0.0014092824],"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":[0.0023847495,0.0006943455,0.013261043,0.0003411314,0.00023797511,0.004047603,0.0009162779,0.009958393,0.3772352,0.001396967,0.025356356,0.56416994],"study_design_scores_gemma":[0.0004126011,0.002515305,0.037141174,0.00010150067,0.00035537325,0.006119322,0.00043360822,0.784469,0.12950604,0.0009917788,0.037641708,0.00031254033],"about_ca_topic_score_codex":0.0039019678,"about_ca_topic_score_gemma":0.005612636,"teacher_disagreement_score":0.998787,"about_ca_system_score_codex":0.00040805363,"about_ca_system_score_gemma":0.0009942752,"threshold_uncertainty_score":0.0093874335},"labels":[],"label_agreement":null},{"id":"W4385738293","doi":"10.3390/rs15163955","title":"Cloud Shadow Detection via Ray Casting with Probability Analysis Refinement Using Sentinel-2 Satellite Data","year":2023,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; University of Calgary","keywords":"Computer science; Cloud computing; Shadow (psychology); Satellite; Pixel; Computer vision; Remote sensing; Artificial intelligence; Object detection; Pattern recognition (psychology); Geology","score_opus":0.10337314120006663,"score_gpt":0.3236822841349253,"score_spread":0.22030914293485865,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385738293","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":[0.064304434,0.00009826485,0.9330045,0.000062214225,0.000023358878,0.00007495591,0.00008181494,0.0014377269,0.00091276615],"genre_scores_gemma":[0.30954057,0.00017102405,0.6887392,0.000044440778,0.000025864738,0.000045610737,0.00034590779,0.00029093545,0.0007965149],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993942,0.00009349954,0.000026357342,0.00008632092,0.00031961885,0.00008006391],"domain_scores_gemma":[0.9986559,0.00048568376,0.00016161482,0.00021010332,0.0004326745,0.00005403423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010029953,0.0007581547,0.00058907596,0.0014341951,0.00038630105,0.0012516642,0.000823859,0.0003496997,0.00087045657],"category_scores_gemma":[0.0025257042,0.0005058014,0.0010213597,0.0007903136,0.00047563933,0.0006883239,0.00072745816,0.00081280485,0.00043905896],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004372534,0.0001803186,0.013863553,0.00016151514,0.00013559061,0.0003328276,0.00044929347,0.45369297,0.11575032,0.0057677724,0.0019103707,0.40731817],"study_design_scores_gemma":[0.00000814293,0.000017934097,0.0013975563,0.000005333159,0.0000099515355,0.000049188257,0.000022959508,0.98401445,0.013247132,0.0005971716,0.00061638,0.000013779651],"about_ca_topic_score_codex":0.01906757,"about_ca_topic_score_gemma":0.022778928,"teacher_disagreement_score":0.01906757,"about_ca_system_score_codex":0.00082461926,"about_ca_system_score_gemma":0.0013767614,"threshold_uncertainty_score":0.037913203},"labels":[],"label_agreement":null},{"id":"W4385801120","doi":"10.1109/cvprw59228.2023.00558","title":"Robust and Scalable Vehicle Re-Identification via Self-Supervision","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Scalability; Software deployment; Overhead (engineering); Identification (biology); Code (set theory); Machine learning; Focus (optics); Artificial intelligence; State (computer science); Resource (disambiguation); Simple (philosophy); Data mining; Distributed computing; Database; Software engineering; Algorithm; Programming language","score_opus":0.043796558563366596,"score_gpt":0.28233567177422697,"score_spread":0.23853911321086038,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4385801120","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048864547,0.0003856841,0.9263308,0.00025323807,0.00021402857,0.00015810566,0.0008428872,0.016592024,0.0063587013],"genre_scores_gemma":[0.674512,0.00020970014,0.30880967,0.00027458594,0.00012269636,0.00015039957,0.0049439734,0.00070022687,0.010276607],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989868,0.000112785354,0.000029633862,0.00046057143,0.0002453129,0.0001649155],"domain_scores_gemma":[0.9984956,0.0002408556,0.00012715823,0.0006603495,0.00040593697,0.00007021019],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008495204,0.001421621,0.0012988268,0.00083066424,0.0005386403,0.00083582866,0.0029823922,0.0011310828,0.002634558],"category_scores_gemma":[0.0028690584,0.00071231567,0.00063105486,0.0006731748,0.00057435624,0.0021997185,0.0024001799,0.001740576,0.003490826],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004377322,0.00037020884,0.0055459086,0.00024282731,0.00015290991,0.00022479155,0.00022628966,0.3130051,0.033205125,0.0037173915,0.026934572,0.6159372],"study_design_scores_gemma":[0.000012325962,0.000044222037,0.0010324405,0.000012833706,0.000011956923,0.00010404669,0.000057638357,0.9836479,0.009631299,0.0028865128,0.0025435714,0.000015294303],"about_ca_topic_score_codex":0.0073714475,"about_ca_topic_score_gemma":0.0136255715,"teacher_disagreement_score":0.0073714475,"about_ca_system_score_codex":0.0005195004,"about_ca_system_score_gemma":0.0013552817,"threshold_uncertainty_score":0.01465708},"labels":[],"label_agreement":null},{"id":"W4386012961","doi":"10.1016/j.cviu.2023.103804","title":"Global-aware and local-aware enhancement network for person search","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Benchmark (surveying); Machine learning; Matching (statistics); Context (archaeology); Pedestrian","score_opus":0.08925833839007498,"score_gpt":0.3512620330894341,"score_spread":0.26200369469935914,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386012961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16333553,0.0017868635,0.82321024,0.00024555944,0.00018730215,0.00014977343,0.00048770677,0.002830608,0.0077663804],"genre_scores_gemma":[0.83321625,0.00066108914,0.15448639,0.00015816957,0.00012623276,0.0000624344,0.0006415534,0.00008049764,0.010567405],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99972206,0.000037660353,0.000009335666,0.00009606038,0.00007346404,0.00006150532],"domain_scores_gemma":[0.99975353,0.000051112438,0.000023724175,0.00006339347,0.00007651841,0.00003170724],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033893017,0.0005212737,0.00075382926,0.00083750166,0.00041591027,0.0004899136,0.0008345828,0.0006095306,0.0019642126],"category_scores_gemma":[0.00063583814,0.00023213307,0.00033804693,0.00072812435,0.00022345514,0.0013184111,0.0008492623,0.00042764205,0.00077665667],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014565069,0.0006551616,0.00626711,0.00016357552,0.00012840194,0.00031607953,0.0001987676,0.07270388,0.1021098,0.008738917,0.01205203,0.79520965],"study_design_scores_gemma":[0.0000195655,0.00024160546,0.003337881,0.000008900709,0.00006729192,0.0003550145,0.000075893564,0.96657,0.023445386,0.0025961788,0.0032596155,0.000022620097],"about_ca_topic_score_codex":0.0039767963,"about_ca_topic_score_gemma":0.0076354146,"teacher_disagreement_score":0.0039767963,"about_ca_system_score_codex":0.0004220846,"about_ca_system_score_gemma":0.0005754283,"threshold_uncertainty_score":0.007907271},"labels":[],"label_agreement":null},{"id":"W4386071639","doi":"10.1109/cvpr52729.2023.00148","title":"Good is Bad: Causality Inspired Cloth-debiasing for Cloth-changing Person Re-identification","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Key Research and Development Program of China; National Natural Science Foundation of China","keywords":"Clothing; Computer science; Identification (biology); Debiasing; Artificial intelligence; Causality (physics); Causal inference; Discriminative model; Representation (politics); Inference; Machine learning; Psychology; Social psychology; Mathematics; Econometrics; Law","score_opus":0.10072874935494916,"score_gpt":0.3521748838833079,"score_spread":0.25144613452835873,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386071639","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052393816,0.0008260091,0.93884605,0.0004861053,0.00019034861,0.000121044875,0.00043711675,0.0041735317,0.0025259887],"genre_scores_gemma":[0.6974921,0.00057096814,0.2875147,0.0008488007,0.00020115306,0.00015117669,0.0020463767,0.00047876127,0.010695979],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989373,0.00033466026,0.000040228515,0.00040252059,0.00014885752,0.00013644712],"domain_scores_gemma":[0.99775994,0.000782709,0.0002506918,0.0007687784,0.00028326383,0.00015451305],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0024496687,0.0011807571,0.0014074557,0.001163593,0.0006757424,0.00092893786,0.0026810444,0.0016760844,0.0047559673],"category_scores_gemma":[0.0059696757,0.0005786739,0.0015223172,0.000802095,0.0011036972,0.0020607815,0.0025223994,0.0020267072,0.0022982473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001132548,0.00043321177,0.015322716,0.00032841208,0.00032986855,0.0004484441,0.00053614535,0.26874572,0.013290535,0.017696802,0.0129211135,0.66881454],"study_design_scores_gemma":[0.000023674558,0.000101597245,0.0018441458,0.000018786144,0.000039596638,0.00015748134,0.00005660144,0.97984695,0.004396961,0.011491383,0.001995237,0.000027625883],"about_ca_topic_score_codex":0.004015891,"about_ca_topic_score_gemma":0.006389047,"teacher_disagreement_score":0.0047559673,"about_ca_system_score_codex":0.0007133734,"about_ca_system_score_gemma":0.0008712432,"threshold_uncertainty_score":0.015910327},"labels":[],"label_agreement":null},{"id":"W4386176662","doi":"10.2139/ssrn.4552237","title":"Reinforcement Learning-Based Mixture of Vision Transformers for Video Violence Recognition","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Reinforcement learning; Reinforcement; Transformer; Artificial intelligence; Computer science; Computer vision; Psychology; Engineering; Social psychology; Electrical engineering","score_opus":0.025729697838549406,"score_gpt":0.30663914090586825,"score_spread":0.28090944306731885,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386176662","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019708648,0.00014678221,0.97881156,0.000070560935,0.000032384116,0.00004622352,0.000050911814,0.0006597627,0.00047321912],"genre_scores_gemma":[0.8546642,0.00013674618,0.14243543,0.00010212108,0.00003945263,0.00011970401,0.00020731382,0.000082081395,0.002212922],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993149,0.00020173047,0.000035007426,0.00021052487,0.0001291725,0.00010871433],"domain_scores_gemma":[0.99806815,0.0011221409,0.00016043847,0.00015402665,0.0003592022,0.00013611562],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015031941,0.0006864677,0.0013133773,0.0007367363,0.0003228207,0.0007261605,0.0017414022,0.001009837,0.0025747812],"category_scores_gemma":[0.004680332,0.000458633,0.00072193594,0.00063325156,0.00060859893,0.0012590633,0.0014290798,0.0016111617,0.0007130842],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010025017,0.00041653,0.003344483,0.00014209871,0.000115775896,0.00010669031,0.00009731946,0.400314,0.013265961,0.012455668,0.0025133009,0.56622565],"study_design_scores_gemma":[0.000009921873,0.000037909926,0.00020828577,0.000003094299,0.000007955799,0.000020223388,0.0000040606124,0.99603516,0.001382642,0.002165787,0.00011984835,0.0000051439542],"about_ca_topic_score_codex":0.0049937475,"about_ca_topic_score_gemma":0.0040653916,"teacher_disagreement_score":0.0049937475,"about_ca_system_score_codex":0.00091559463,"about_ca_system_score_gemma":0.0009527112,"threshold_uncertainty_score":0.009929359},"labels":[],"label_agreement":null},{"id":"W4386243204","doi":"10.1109/crv60082.2023.00017","title":"InterTrack: Interaction Transformer for 3D Multi-Object Tracking","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Discriminative model; Computer science; Artificial intelligence; Benchmark (surveying); Data association; Video tracking; Object detection; Tracking (education); Object (grammar); Computer vision; Machine learning; Data mining; Pattern recognition (psychology); Geography","score_opus":0.10097023106871778,"score_gpt":0.37867065621820795,"score_spread":0.27770042514949017,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386243204","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":[0.002290866,0.00007241939,0.9841912,0.000041058967,0.00003719088,0.00006041894,0.00026225226,0.012048979,0.000995667],"genre_scores_gemma":[0.13496643,0.00016577284,0.8536187,0.00024459066,0.00006754855,0.00027133193,0.0031395894,0.0024210736,0.0051050605],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99849856,0.00018922328,0.000050783594,0.0003648171,0.0007681184,0.00012843427],"domain_scores_gemma":[0.99875855,0.00032511592,0.00009797171,0.0004908814,0.00021647682,0.000111036614],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013424251,0.0016694043,0.0010594805,0.0013874711,0.0007632508,0.0016217753,0.004563033,0.0014906893,0.010064048],"category_scores_gemma":[0.0035338353,0.0009099498,0.0016150106,0.0017744149,0.00083117525,0.0023813953,0.0054128296,0.0022049304,0.0053340215],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008095149,0.00035948856,0.0023004636,0.00025016678,0.00020189128,0.00022372301,0.0002783213,0.10099371,0.027896866,0.01915415,0.04633675,0.80119485],"study_design_scores_gemma":[0.00005227268,0.00011364997,0.00049248955,0.000011444955,0.00002144442,0.00018524482,0.00004335649,0.9612475,0.014862782,0.0097885495,0.01315064,0.000030629224],"about_ca_topic_score_codex":0.0062898607,"about_ca_topic_score_gemma":0.011919766,"teacher_disagreement_score":0.010064048,"about_ca_system_score_codex":0.0010164609,"about_ca_system_score_gemma":0.0010136396,"threshold_uncertainty_score":0.033667564},"labels":[],"label_agreement":null},{"id":"W4386245828","doi":"10.1007/s11263-023-01864-0","title":"Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-identification","year":2023,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Skeleton (computer programming); Artificial intelligence; Computer science; Identification (biology); Pattern recognition (psychology); Discriminative model; Flexibility (engineering); Consistency (knowledge bases); Topological skeleton; Construct (python library); Mathematics; Segmentation; Active shape model","score_opus":0.061524534617128354,"score_gpt":0.3448180076763345,"score_spread":0.28329347305920616,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386245828","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027847962,0.000514867,0.96686536,0.00010086497,0.0000815924,0.00008713034,0.00022800051,0.0028046349,0.0014696069],"genre_scores_gemma":[0.38429677,0.00035957494,0.6066024,0.00026669877,0.000112140435,0.0001844185,0.0016347746,0.0005213354,0.0060219057],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99848074,0.00020610834,0.00006808968,0.00064777356,0.00039132562,0.00020591384],"domain_scores_gemma":[0.99856275,0.00037621617,0.000118110016,0.00048621913,0.00037349426,0.00008319799],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013018269,0.0011135256,0.002244338,0.0019817594,0.00060969446,0.0010070835,0.0030482495,0.0019482649,0.0036919182],"category_scores_gemma":[0.0028188787,0.00074653287,0.0015956083,0.0017619125,0.0007215594,0.0016740542,0.0026614368,0.0015206893,0.0028843994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005717555,0.0003501021,0.0027363508,0.00014277844,0.0001869918,0.00017661785,0.00011087322,0.03177206,0.03595257,0.002391291,0.0063468073,0.91926175],"study_design_scores_gemma":[0.00001962931,0.00016072113,0.0018157192,0.000020803727,0.000058490627,0.00027618546,0.000040284143,0.9824181,0.009799719,0.0036626523,0.0017077441,0.000019995341],"about_ca_topic_score_codex":0.0025663844,"about_ca_topic_score_gemma":0.006438984,"teacher_disagreement_score":0.0036919182,"about_ca_system_score_codex":0.0003901467,"about_ca_system_score_gemma":0.0008897718,"threshold_uncertainty_score":0.012350678},"labels":[],"label_agreement":null},{"id":"W4386249599","doi":"10.1109/crv60082.2023.00029","title":"Diffusion Dataset Generation: Towards Closing the Sim2Real Gap for Pedestrian Detection","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Computer science; Real world data; Closing (real estate); Pedestrian; Task (project management); Pedestrian detection; Data mining; Synthetic data; Artificial intelligence; Training set; Data modeling; Machine learning; Labeled data; Data science; Database; Engineering","score_opus":0.15560377892608507,"score_gpt":0.36851145319100775,"score_spread":0.21290767426492269,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386249599","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08593486,0.0007070289,0.8957895,0.0012992127,0.00032559715,0.00028966335,0.0018227637,0.0112757115,0.0025557291],"genre_scores_gemma":[0.41530898,0.00028490802,0.5700134,0.0009523394,0.00015293165,0.00043876306,0.010200822,0.00076126173,0.0018866314],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9951507,0.002314538,0.00020366885,0.0012690232,0.0008608881,0.0002010796],"domain_scores_gemma":[0.9846875,0.0065489,0.0007684379,0.005564816,0.001897867,0.0005325185],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.010191872,0.0018595364,0.0014008303,0.0023867278,0.00079175935,0.0016392572,0.0039940607,0.0026369095,0.0018783252],"category_scores_gemma":[0.025374942,0.0010415,0.001327222,0.0013644322,0.0013694839,0.0045536966,0.005000137,0.0029252032,0.0015773815],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016522327,0.0018759936,0.034308568,0.0006149761,0.00046694628,0.00044764386,0.0005167353,0.29896873,0.030014273,0.015595813,0.03410489,0.58143324],"study_design_scores_gemma":[0.00005197617,0.00019228445,0.0013191943,0.000027139127,0.000016816119,0.00020670405,0.000051106224,0.97482026,0.010718017,0.0066743535,0.005891343,0.000030811752],"about_ca_topic_score_codex":0.003062738,"about_ca_topic_score_gemma":0.0056529376,"teacher_disagreement_score":0.010191872,"about_ca_system_score_codex":0.0012855785,"about_ca_system_score_gemma":0.0012572543,"threshold_uncertainty_score":0.05390042},"labels":[],"label_agreement":null},{"id":"W4386392998","doi":"10.1007/s00521-023-08913-2","title":"Vision transformer with multiple granularities for person re-identification","year":2023,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Granularity; Discriminative model; Computer science; Transformer; Artificial intelligence; Benchmark (surveying); Pattern recognition (psychology); Feature extraction; Machine learning; Voltage; Engineering","score_opus":0.03952398071804249,"score_gpt":0.3236776671711854,"score_spread":0.28415368645314293,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386392998","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":[0.025481522,0.0004830051,0.9694344,0.00013242332,0.00011671916,0.000064912405,0.00021589958,0.0016665362,0.0024045738],"genre_scores_gemma":[0.7716102,0.0005460536,0.22141166,0.00023105215,0.000095211,0.00005835332,0.00057166244,0.00014267587,0.0053331116],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993291,0.00007620612,0.000036593116,0.00021924925,0.00020709557,0.00013183041],"domain_scores_gemma":[0.9994301,0.00012011599,0.000042567415,0.0002386403,0.00012156561,0.000047060217],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007788898,0.0005840492,0.0012051339,0.0012569998,0.0003715517,0.001100342,0.0011494401,0.0009438742,0.0037304512],"category_scores_gemma":[0.0019188519,0.00037306477,0.00081981765,0.0014411728,0.0004924548,0.002298651,0.00182528,0.0012867374,0.0016129176],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00088472984,0.0003273001,0.0016440789,0.00015470946,0.00013244452,0.00019274977,0.00007317714,0.046026498,0.05798562,0.016008181,0.00548927,0.87108123],"study_design_scores_gemma":[0.000025721294,0.00010807424,0.0015430179,0.00001904697,0.00005476064,0.00026811764,0.00004770631,0.9543507,0.026191603,0.014825678,0.0025399595,0.000025705258],"about_ca_topic_score_codex":0.005419582,"about_ca_topic_score_gemma":0.005143297,"teacher_disagreement_score":0.005419582,"about_ca_system_score_codex":0.0006149169,"about_ca_system_score_gemma":0.00079822895,"threshold_uncertainty_score":0.012479603},"labels":[],"label_agreement":null},{"id":"W4386590365","doi":"10.1109/icip49359.2023.10222086","title":"Domain Generalization Method for Person Re-Id Using Metabin and Mixstyle","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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":"Artificial Intelligence in Medicine (Canada)","funders":"Institute for Information and Communications Technology Promotion; Ministry of Science and ICT, South Korea","keywords":"Normalization (sociology); Computer science; Generalization; Artificial intelligence; Domain (mathematical analysis); Identification (biology); Bin; Machine learning; Algorithm; Mathematics","score_opus":0.09759487060788355,"score_gpt":0.37561205823267146,"score_spread":0.2780171876247879,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386590365","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":[0.030995598,0.0005099976,0.9547928,0.00015553381,0.00018175216,0.00016931554,0.0004285526,0.010135161,0.0026313476],"genre_scores_gemma":[0.35660878,0.000418716,0.6261141,0.0005779174,0.00011947754,0.0002572044,0.0038284915,0.0010118487,0.0110634845],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986507,0.00019650826,0.00006893689,0.0006964272,0.0002460428,0.00014132014],"domain_scores_gemma":[0.9990872,0.00011715674,0.00006946645,0.00041573914,0.00024459983,0.00006578024],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018895934,0.001729444,0.0014263503,0.0017678435,0.0008134054,0.00092768413,0.0024797488,0.0011706533,0.004251255],"category_scores_gemma":[0.0026888635,0.0005365172,0.0016409938,0.001213819,0.0006132793,0.0024684537,0.0024508433,0.0019117583,0.0035369685],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036749014,0.0002881098,0.0064991964,0.00012768393,0.00019383641,0.00024120203,0.00026109148,0.037774686,0.019598912,0.004083539,0.0157285,0.9148357],"study_design_scores_gemma":[0.00003401476,0.00013037436,0.0039899047,0.000031425734,0.000082052575,0.00063103065,0.0001822848,0.95105,0.02512537,0.008520843,0.01015792,0.00006486536],"about_ca_topic_score_codex":0.0036383888,"about_ca_topic_score_gemma":0.0061725886,"teacher_disagreement_score":0.004251255,"about_ca_system_score_codex":0.00073881814,"about_ca_system_score_gemma":0.00084141194,"threshold_uncertainty_score":0.014221847},"labels":[],"label_agreement":null},{"id":"W4386598330","doi":"10.1109/icip49359.2023.10222660","title":"SiamCLIM: Text-Based Pedestrian Search Via Multi-Modal Siamese Contrastive Learning","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Guangdong Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Benchmark (surveying); Discriminative model; Modal; Artificial intelligence; Pedestrian; Image (mathematics); Matching (statistics); Task (project management); Projection (relational algebra); Natural language processing; Pattern recognition (psychology); Algorithm; Geography; Mathematics","score_opus":0.05579582966077868,"score_gpt":0.3409606625826991,"score_spread":0.2851648329219204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386598330","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":[0.055917192,0.0010494381,0.9280327,0.00046440164,0.00021523329,0.00027367537,0.0011553038,0.007885,0.0050070644],"genre_scores_gemma":[0.6406728,0.00069881795,0.32666713,0.0013633199,0.00026987196,0.0003505336,0.0053556655,0.0005622169,0.024059638],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995338,0.00007793185,0.000019072504,0.00018685787,0.00010339408,0.0000789294],"domain_scores_gemma":[0.9995191,0.00014296162,0.00004839367,0.00009014482,0.00012725824,0.00007221282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008881794,0.0014194517,0.0017749784,0.0014438893,0.0004910717,0.001038404,0.0029515952,0.0017732516,0.0055111214],"category_scores_gemma":[0.0019271447,0.00053187046,0.0014215993,0.0013688665,0.0007154363,0.0020230205,0.0016553806,0.0014440268,0.0025210415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000790419,0.00089588924,0.003291246,0.00027034248,0.0002920558,0.0003934752,0.0001399734,0.40167877,0.017666541,0.011389563,0.03337464,0.52981704],"study_design_scores_gemma":[0.000023820605,0.00007081592,0.00017085583,0.000004597661,0.000010795517,0.00007024814,0.000008389904,0.99488896,0.0013503815,0.002537759,0.000852849,0.000010599291],"about_ca_topic_score_codex":0.01387137,"about_ca_topic_score_gemma":0.020586137,"teacher_disagreement_score":0.01387137,"about_ca_system_score_codex":0.0010305145,"about_ca_system_score_gemma":0.0015350503,"threshold_uncertainty_score":0.027581275},"labels":[],"label_agreement":null},{"id":"W4386920282","doi":"10.1109/sas58821.2023.10254089","title":"Comparison of Spatial Coverage of LiDAR Systems for In Home Activity of Daily Living Applications","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Lidar; Computer science; Remote sensing; Environmental science; Geography","score_opus":0.05654632564961214,"score_gpt":0.3610775256439672,"score_spread":0.3045311999943551,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386920282","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":[0.97588986,0.0006612955,0.016078427,0.00022735559,0.000019623094,0.000048141756,0.0006556901,0.00018338671,0.006236091],"genre_scores_gemma":[0.9974751,0.00015081759,0.001763413,0.000013326275,0.000004557581,0.000015729243,0.00023463418,0.000011998299,0.00033046468],"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","domain_scores_codex":[0.99908006,0.00027372272,0.00004202215,0.00013413276,0.00027422645,0.00019586216],"domain_scores_gemma":[0.9970637,0.0017179567,0.00025428925,0.00016663692,0.00071134866,0.00008603836],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009113356,0.0005642408,0.00030822607,0.0008431322,0.00026823723,0.0007480925,0.0005851976,0.0005074184,0.0010349725],"category_scores_gemma":[0.0033842786,0.00019762132,0.0005022268,0.0009129647,0.0002935877,0.0009334819,0.00047226634,0.00021023481,0.00020945547],"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":[0.00052759144,0.00017372801,0.09138881,0.00019063539,0.00018208173,0.0003085919,0.00022484563,0.8454565,0.009319535,0.0017978201,0.0015200236,0.048909802],"study_design_scores_gemma":[0.000021465186,0.00062585506,0.06427454,0.00004035665,0.00010180349,0.00044237595,0.00051567197,0.92446494,0.0067285276,0.0008119761,0.0019306118,0.000041821764],"about_ca_topic_score_codex":0.013970417,"about_ca_topic_score_gemma":0.016827984,"teacher_disagreement_score":0.013970417,"about_ca_system_score_codex":0.0009159938,"about_ca_system_score_gemma":0.0005020416,"threshold_uncertainty_score":0.027778149},"labels":[],"label_agreement":null},{"id":"W4386937866","doi":"10.1007/978-3-031-43950-6_17","title":"Real-Time Multiple Object Tracking for Safe Cooking Activities","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Computer science; Stove; Computer vision; Frame (networking); Context (archaeology); Video tracking; Real-time computing; Frame rate; Artificial intelligence; Segmentation; Object (grammar); Telecommunications; Engineering","score_opus":0.03878915059857371,"score_gpt":0.2957166060063782,"score_spread":0.2569274554078045,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386937866","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.054663766,0.001096002,0.9362341,0.00005545516,0.00016465489,0.000080874524,0.00015105201,0.004024375,0.0035297333],"genre_scores_gemma":[0.45246792,0.0010303789,0.53394145,0.000101894344,0.00005617601,0.00012723438,0.0006088789,0.00032480463,0.011341245],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997106,0.000037383357,0.0000113063315,0.00011157022,0.00010153219,0.00002751205],"domain_scores_gemma":[0.9998122,0.00005376362,0.000024561969,0.000031864554,0.000057371624,0.000020348438],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039764767,0.00047018105,0.00053844706,0.00070265133,0.00023843028,0.000609752,0.00066179334,0.00066248776,0.0032229193],"category_scores_gemma":[0.00042137885,0.0002654557,0.00030163466,0.00052179716,0.00017594478,0.00048294154,0.00039704842,0.0003570246,0.0013743836],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00055533147,0.00013175418,0.001269256,0.00025900474,0.000049140624,0.00016193108,0.00016598473,0.014579926,0.33447433,0.00096730504,0.0038536652,0.6435324],"study_design_scores_gemma":[0.00010237172,0.0004903777,0.015606876,0.00010516683,0.00012416462,0.0008717468,0.00013795946,0.7228915,0.22375876,0.0013883135,0.03441211,0.000110604735],"about_ca_topic_score_codex":0.0020381254,"about_ca_topic_score_gemma":0.0024392444,"teacher_disagreement_score":0.0032229193,"about_ca_system_score_codex":0.0002691392,"about_ca_system_score_gemma":0.000305399,"threshold_uncertainty_score":0.010781705},"labels":[],"label_agreement":null},{"id":"W4386985033","doi":"10.1007/978-981-99-1431-9_37","title":"Application of Image-To-Image Translation in Improving Pedestrian Detection","year":2023,"lang":"en","type":"book-chapter","venue":"Algorithms for intelligent systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Translation (biology); Pedestrian; Pyramid (geometry); Night vision; Pedestrian detection; Image translation; Image (mathematics); Deep learning; Pattern recognition (psychology); Engineering; Mathematics","score_opus":0.06029351336320358,"score_gpt":0.3169929405518935,"score_spread":0.2566994271886899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4386985033","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":[0.016617216,0.0021342149,0.9699747,0.00017920468,0.000438246,0.000085943175,0.00013906478,0.0035143972,0.0069169565],"genre_scores_gemma":[0.13559087,0.0033111633,0.83912176,0.0002825018,0.00033078462,0.00008713434,0.0005830719,0.0010737402,0.019618927],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994722,0.00009446382,0.000026466252,0.00015444616,0.00020308478,0.0000493859],"domain_scores_gemma":[0.9991714,0.0002890095,0.000051549207,0.000172993,0.0002883416,0.000026690532],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008072878,0.0014442997,0.0009983596,0.0012197293,0.00043119045,0.001089783,0.000964147,0.00095597096,0.008093963],"category_scores_gemma":[0.0023726996,0.00043636395,0.0008232598,0.0022137254,0.00059138896,0.0011096833,0.00090521615,0.0009480313,0.0047267],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000252205,0.00010541952,0.00060692406,0.00017727046,0.000055054785,0.00014203522,0.00006151448,0.015122686,0.08022498,0.0040316144,0.004876558,0.89434373],"study_design_scores_gemma":[0.000030134976,0.0003912666,0.0030758274,0.000060538136,0.00017866264,0.0011708773,0.00008421039,0.71777934,0.24800608,0.008998561,0.020180516,0.00004396375],"about_ca_topic_score_codex":0.0016782569,"about_ca_topic_score_gemma":0.0020971547,"teacher_disagreement_score":0.008093963,"about_ca_system_score_codex":0.00033860593,"about_ca_system_score_gemma":0.00048604523,"threshold_uncertainty_score":0.02707696},"labels":[],"label_agreement":null},{"id":"W4387604812","doi":"10.2139/ssrn.4601111","title":"Deep Learning and Multi-Modal Fusion for Real-Time Multi-Object Tracking: Algorithms, Challenges, Datasets, and Comparative Study","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"","keywords":"Modal; Computer science; Artificial intelligence; Fusion; Tracking (education); Deep learning; Object (grammar); Algorithm; Video tracking; Computer vision; Machine learning; Pattern recognition (psychology)","score_opus":0.11869408984257114,"score_gpt":0.3808986459174012,"score_spread":0.26220455607483006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387604812","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.21373218,0.019595947,0.7499238,0.0016465832,0.00063519826,0.0002949721,0.004067671,0.0038179378,0.0062856036],"genre_scores_gemma":[0.7506831,0.0050510946,0.2284637,0.00031376033,0.00022029737,0.0002210647,0.010914199,0.0002265548,0.003906209],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975343,0.00078660116,0.00016098547,0.0005404864,0.000710541,0.00026710902],"domain_scores_gemma":[0.9951426,0.002496961,0.00027984884,0.0010083079,0.00091377634,0.00015849642],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006464338,0.0016558454,0.0017731822,0.002123135,0.0006744441,0.0022162148,0.0017987997,0.0021201244,0.00215951],"category_scores_gemma":[0.010942854,0.0005856217,0.0012457157,0.0034848333,0.00072803895,0.003493921,0.00239117,0.0022110308,0.0008130811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00207997,0.0010921089,0.012168215,0.0010501301,0.0008030029,0.00008695438,0.00012868893,0.16166379,0.0070717414,0.007023324,0.011941671,0.79489046],"study_design_scores_gemma":[0.000059548125,0.0003154819,0.0070883753,0.00015222137,0.00022284247,0.00014602733,0.0001385011,0.9666927,0.008179151,0.012187987,0.0047560404,0.00006109204],"about_ca_topic_score_codex":0.012404691,"about_ca_topic_score_gemma":0.0092493445,"teacher_disagreement_score":0.012404691,"about_ca_system_score_codex":0.0013175388,"about_ca_system_score_gemma":0.0018242578,"threshold_uncertainty_score":0.03418708},"labels":[],"label_agreement":null},{"id":"W4387676203","doi":"10.1016/j.patcog.2023.110045","title":"A Novel Attention-Driven Framework for Unsupervised Pedestrian Re-identification with Clustering Optimization","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"National Natural Science Foundation of China-Shandong Joint Fund for Marine Science Research Centers; Natural Science Foundation of Shandong Province","keywords":"Computer science; Artificial intelligence; Cluster analysis; Pedestrian; Pattern recognition (psychology); Identification (biology); Machine learning; Field (mathematics); Unsupervised learning; Convolutional neural network; Task (project management); Pedestrian detection","score_opus":0.09206838056496137,"score_gpt":0.3193227789167085,"score_spread":0.22725439835174716,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387676203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0024036504,0.00014536151,0.9959351,0.00007397399,0.00003787121,0.000033095457,0.000066281085,0.0006644532,0.00064019754],"genre_scores_gemma":[0.20461999,0.00035413133,0.7804083,0.00050512847,0.00025288286,0.00032252437,0.0012140202,0.00090892834,0.011414103],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99885464,0.00024769577,0.000048056503,0.00040643243,0.0002675491,0.00017558584],"domain_scores_gemma":[0.99906784,0.00026842,0.000072304094,0.00015294024,0.0003562399,0.000082356884],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001269156,0.0014208705,0.0023940043,0.0015908806,0.00089166994,0.0013213237,0.0049109347,0.0023376534,0.0042307074],"category_scores_gemma":[0.0025547394,0.0010473885,0.0018184675,0.0018632156,0.00087009044,0.0015187885,0.0029036824,0.0019537134,0.0019263304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032192844,0.00027760887,0.0008163788,0.00014534219,0.00022460728,0.00013917977,0.00012573124,0.5590631,0.014794857,0.023316085,0.012047749,0.38872746],"study_design_scores_gemma":[0.0000033078065,0.00000976789,0.00006089319,0.000002687792,0.00000580753,0.000012491178,0.0000036007816,0.99645233,0.00060702005,0.0024427031,0.0003940498,0.0000053379513],"about_ca_topic_score_codex":0.022048077,"about_ca_topic_score_gemma":0.0265505,"teacher_disagreement_score":0.022048077,"about_ca_system_score_codex":0.0013696026,"about_ca_system_score_gemma":0.0022021323,"threshold_uncertainty_score":0.043839514},"labels":[],"label_agreement":null},{"id":"W4387790368","doi":"10.11834/jig.220026","title":"Short-term memory and CenterTrack based vehicle-related multi-target tracking method","year":2023,"lang":"en","type":"article","venue":"Journal of Image and Graphics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; Vehicle tracking system; Tracking (education); Artificial intelligence; Computer vision; Video tracking; Adaptability; Key (lock); Tracking system; Metric (unit); Real-time computing; Trajectory; Term (time); Intelligent transportation system; Object detection; Object (grammar); Pattern recognition (psychology); Engineering; Computer security; Kalman filter","score_opus":0.03653671942401341,"score_gpt":0.33071443432676395,"score_spread":0.2941777149027505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387790368","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07775167,0.0028681979,0.89996,0.00047724287,0.00039326813,0.00014661174,0.00028663166,0.0013558023,0.016760565],"genre_scores_gemma":[0.82780415,0.0020787406,0.14923127,0.0003954889,0.00024177803,0.00022577171,0.00068614766,0.000120628356,0.019216035],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99907184,0.00012230496,0.000054870678,0.00035301375,0.00030156752,0.00009632281],"domain_scores_gemma":[0.99904424,0.0002960403,0.0000883687,0.00009461502,0.00043002825,0.000046604],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011061208,0.00072803,0.0008014972,0.0009466467,0.00094303186,0.0016242934,0.0014848454,0.00102983,0.0032373106],"category_scores_gemma":[0.0030671442,0.00030880465,0.0005168966,0.0014423742,0.0006268104,0.0033928761,0.001343328,0.0009294775,0.00085331633],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094060996,0.00023415821,0.017002605,0.00060681714,0.00032605,0.0003447537,0.0009237175,0.15006644,0.018979687,0.034404993,0.012187328,0.7639828],"study_design_scores_gemma":[0.0000933849,0.0002994787,0.0069316113,0.000051051135,0.00019331282,0.00046140148,0.00042661227,0.9502193,0.014483737,0.01628365,0.01045785,0.00009869866],"about_ca_topic_score_codex":0.0090932185,"about_ca_topic_score_gemma":0.0053703776,"teacher_disagreement_score":0.0090932185,"about_ca_system_score_codex":0.00080340117,"about_ca_system_score_gemma":0.0017310074,"threshold_uncertainty_score":0.018080592},"labels":[],"label_agreement":null},{"id":"W4387829418","doi":"10.1109/igarss52108.2023.10282752","title":"Moving Object Detection by Low-Rank Analysis of Region-Based Correlated Motion Fields","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Queen's University","funders":"","keywords":"Robust principal component analysis; Artificial intelligence; Robustness (evolution); Computer vision; Computer science; Object detection; Motion estimation; Principal component analysis; Motion field; Quarter-pixel motion; Exploit; Motion detection; Motion compensation; Pattern recognition (psychology); Noise (video); Motion (physics); Image (mathematics)","score_opus":0.018292965855655662,"score_gpt":0.2691507442541142,"score_spread":0.2508577783984586,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387829418","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":[0.01697577,0.0002711074,0.9819901,0.00005021037,0.000017582015,0.000027530437,0.00005808861,0.0002670196,0.00034260927],"genre_scores_gemma":[0.36815006,0.000708962,0.6284951,0.00010953633,0.0001277555,0.000072105075,0.0005237101,0.00009883748,0.0017138949],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994973,0.000107269254,0.000025508245,0.000117340605,0.00019272076,0.000059888866],"domain_scores_gemma":[0.99928516,0.00024637338,0.00016754563,0.000085824926,0.00017400652,0.000041088715],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070039264,0.0008222512,0.00082267245,0.0020504722,0.00025270492,0.0006046676,0.00072264666,0.00050152146,0.0006336283],"category_scores_gemma":[0.0020293603,0.00035417895,0.00073708955,0.0012528815,0.00046038357,0.0009449264,0.00052169646,0.00067570084,0.0003145606],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00029382628,0.0001474991,0.0034071412,0.00020195126,0.00013142999,0.00026752753,0.00014531348,0.1784589,0.11813613,0.010420266,0.002763946,0.68562603],"study_design_scores_gemma":[0.000008756143,0.000050680363,0.0017692626,0.000009385597,0.000016501195,0.0001033345,0.000015440843,0.9854214,0.0093872845,0.0020772477,0.0011218733,0.000018755938],"about_ca_topic_score_codex":0.0033871185,"about_ca_topic_score_gemma":0.003661803,"teacher_disagreement_score":0.0033871185,"about_ca_system_score_codex":0.0004357456,"about_ca_system_score_gemma":0.00058626255,"threshold_uncertainty_score":0.0067347884},"labels":[],"label_agreement":null},{"id":"W4387846884","doi":"10.1007/s10489-023-05081-7","title":"Fast online multi-target multi-camera tracking for vehicles","year":2023,"lang":"en","type":"article","venue":"Applied Intelligence","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Cluster analysis; Tracking (education); Perspective (graphical); Domain (mathematical analysis); Feature (linguistics); Computer vision; Field (mathematics); Video tracking; Class (philosophy); Real-time computing; Object (grammar)","score_opus":0.11684963323028012,"score_gpt":0.36436570328498424,"score_spread":0.2475160700547041,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387846884","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016639153,0.00044334974,0.98089004,0.000053499738,0.000086292785,0.000026176414,0.000068675436,0.00075968297,0.0010331102],"genre_scores_gemma":[0.5126999,0.00058966153,0.4773435,0.00010871756,0.000108671426,0.00008588169,0.00062554935,0.00015851005,0.008279588],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995105,0.00006399587,0.000016209633,0.00014119114,0.00019208038,0.000075958],"domain_scores_gemma":[0.9994184,0.00018501037,0.000057865167,0.00010721313,0.00018807841,0.000043445772],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005845566,0.0007396188,0.00087411114,0.0010042642,0.00044047023,0.0007541203,0.00095177814,0.00096152286,0.00200827],"category_scores_gemma":[0.0016937517,0.0005557272,0.0004916663,0.0009420459,0.00022790873,0.0012630551,0.0010066183,0.0009483421,0.00100195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005508874,0.00014571958,0.0015702813,0.00016384224,0.00011697155,0.00013325743,0.00008628463,0.12766106,0.049302787,0.004128804,0.0052383333,0.8109017],"study_design_scores_gemma":[0.000009078145,0.000039981413,0.0008194154,0.0000053333924,0.000011170846,0.000067313464,0.000010257692,0.9890035,0.007648601,0.001252839,0.0011249466,0.000007632421],"about_ca_topic_score_codex":0.00509358,"about_ca_topic_score_gemma":0.0056813504,"teacher_disagreement_score":0.00509358,"about_ca_system_score_codex":0.0004458521,"about_ca_system_score_gemma":0.00063654996,"threshold_uncertainty_score":0.010127842},"labels":[],"label_agreement":null},{"id":"W4387869178","doi":"10.7554/elife.91243.1.sa3","title":"eLife Assessment: Integrating Gaze, image analysis, and body tracking: Foothold selection during locomotion","year":2023,"lang":"en","type":"peer-review","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Terrain; Gaze; Context (archaeology); Computer science; Stability (learning theory); Artificial intelligence; Eye tracking; Natural (archaeology); Photogrammetry; Computer vision; Selection (genetic algorithm); Human–computer interaction; Machine learning; Geography; Cartography","score_opus":0.03707709950266415,"score_gpt":0.3717458273157415,"score_spread":0.3346687278130774,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4387869178","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":"evaluation","model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":"evaluation","domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.61663276,0.004706565,0.33969724,0.0017030434,0.0002774614,0.0008661934,0.0025109078,0.0063233185,0.027282506],"genre_scores_gemma":[0.79756814,0.0019161286,0.17654392,0.00020595135,0.0001328529,0.00024606497,0.0015762035,0.00052299676,0.021287737],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9990891,0.00019185132,0.000053869466,0.00017447134,0.00042000323,0.00007073009],"domain_scores_gemma":[0.99731207,0.0005622112,0.0003569883,0.00015379758,0.0014279548,0.00018696851],"candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0024621063,0.00045313625,0.0005455207,0.0030922513,0.00035465302,0.0013813792,0.0006185887,0.00055684603,0.0053913314],"category_scores_gemma":[0.008461023,0.0002571155,0.00018600909,0.0011549823,0.0002791614,0.001180332,0.000925287,0.00029990645,0.0025608477],"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":[0.00022504172,0.00010038424,0.067375004,0.00022613653,0.00010118102,0.0001755693,0.00031825528,0.0023370595,0.035144676,0.00039568095,0.0066613406,0.88693964],"study_design_scores_gemma":[0.00010445119,0.00058805424,0.738299,0.00034896177,0.00025398433,0.0012734132,0.0012126764,0.14884886,0.0667933,0.003396918,0.038668595,0.00021180887],"about_ca_topic_score_codex":0.009616479,"about_ca_topic_score_gemma":0.029501392,"teacher_disagreement_score":0.9975379,"about_ca_system_score_codex":0.0004468441,"about_ca_system_score_gemma":0.0009422213,"threshold_uncertainty_score":0.01912105},"labels":[],"label_agreement":null},{"id":"W4388024018","doi":"10.18280/ts.400503","title":"Joint Solution for Temporal-Spatial Synchronization of Multi-View Videos and Pedestrian Matching in Crowd Scenes","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Joint (building); Pedestrian; Computer science; Matching (statistics); Computer vision; Artificial intelligence; Synchronization (alternating current); Geography; Mathematics; Engineering; Telecommunications; Statistics","score_opus":0.0627574904598235,"score_gpt":0.3100703147067267,"score_spread":0.2473128242469032,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388024018","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021591647,0.00009042457,0.9773183,0.000063340485,0.000031856092,0.000024444138,0.000035512632,0.00013088866,0.0007135971],"genre_scores_gemma":[0.7743805,0.00023812569,0.22039352,0.00008565284,0.0000947879,0.0001406718,0.00025517523,0.00008309251,0.004328464],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.999642,0.000061393155,0.000014934296,0.0001320707,0.00008789771,0.00006162004],"domain_scores_gemma":[0.9995828,0.0001416405,0.00010052746,0.000037168247,0.00008986592,0.00004797056],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007615025,0.00078131433,0.0008845849,0.0005811116,0.00032290258,0.0006425085,0.0010408806,0.0010770824,0.001328424],"category_scores_gemma":[0.0023375722,0.0004510715,0.00061046926,0.00061279285,0.00044225348,0.0007716678,0.0011209698,0.00068455975,0.00027195516],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027781902,0.00007782668,0.0022048326,0.00014590833,0.00007778163,0.00023544829,0.00022641674,0.8330576,0.013585366,0.013516101,0.0019874373,0.13460758],"study_design_scores_gemma":[0.00000409256,0.000018595008,0.00022326472,0.0000027877525,0.000004458378,0.000018436547,0.000016059736,0.9975986,0.0007575593,0.0010958351,0.00025614034,0.0000042823126],"about_ca_topic_score_codex":0.007390502,"about_ca_topic_score_gemma":0.0054046926,"teacher_disagreement_score":0.007390502,"about_ca_system_score_codex":0.0005685041,"about_ca_system_score_gemma":0.0013204461,"threshold_uncertainty_score":0.014694989},"labels":[],"label_agreement":null},{"id":"W4388071781","doi":"10.1109/pimrc56721.2023.10293825","title":"DissIdent: A Dissimilarity-based Approach for Improving the Identification of Unknown UAVs","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Canada Research Chairs","keywords":"Computer science; Identification (biology); Drone; Artificial intelligence; Cluster analysis; Categorization; Range (aeronautics); Machine learning; DBSCAN; Function (biology); Data mining; Traceability; Class (philosophy); Pattern recognition (psychology); Engineering; Fuzzy clustering","score_opus":0.04590974386599206,"score_gpt":0.31635352578734244,"score_spread":0.2704437819213504,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388071781","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":[0.057299603,0.0004544014,0.9388057,0.00019173871,0.00013607608,0.00010265158,0.00014304975,0.00074810744,0.0021187735],"genre_scores_gemma":[0.45611182,0.00026024212,0.53775805,0.0002455704,0.00017931608,0.00009349035,0.0006550593,0.00020729825,0.004489215],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99833256,0.0002753867,0.000107174295,0.00046658976,0.00067195593,0.00014629443],"domain_scores_gemma":[0.99774617,0.0006326091,0.00031535412,0.0004028858,0.00071182137,0.00019112151],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016065127,0.0009868112,0.0013530209,0.0028374551,0.00073114445,0.0010332862,0.0017614503,0.001095343,0.002222568],"category_scores_gemma":[0.005355914,0.00033169106,0.00095651846,0.0012814837,0.00067746436,0.0017546045,0.0028792021,0.0010952491,0.0007475658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008748196,0.00038412667,0.009356025,0.00030735292,0.00024327441,0.00036601193,0.00050446007,0.07629541,0.06452146,0.012488192,0.0048593045,0.82979965],"study_design_scores_gemma":[0.00006082479,0.0005860435,0.006093615,0.000031672284,0.000060173403,0.00096494745,0.00028003615,0.9501701,0.021932172,0.010543232,0.009196413,0.00008075623],"about_ca_topic_score_codex":0.0018663228,"about_ca_topic_score_gemma":0.0025353031,"teacher_disagreement_score":0.0028374551,"about_ca_system_score_codex":0.0005406546,"about_ca_system_score_gemma":0.00058706803,"threshold_uncertainty_score":0.008496106},"labels":[],"label_agreement":null},{"id":"W4388447631","doi":"10.1155/2023/6615879","title":"Subway Platform Passenger Flow Counting Algorithm Based on Feature-Enhanced Pyramid and Mixed Attention","year":2023,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Pyramid (geometry); Feature (linguistics); Computer science; Context (archaeology); Algorithm; Aliasing; Mean squared error; Optical flow; Interference (communication); Representation (politics); Flow (mathematics); Channel (broadcasting); Scale (ratio); Artificial intelligence; Pattern recognition (psychology); Data mining; Image (mathematics); Mathematics; Statistics; Telecommunications","score_opus":0.014168550281238187,"score_gpt":0.273275242367489,"score_spread":0.2591066920862508,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388447631","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1620214,0.00082065247,0.82303256,0.0002922596,0.00019437129,0.00027613612,0.00065260177,0.0069499575,0.005759995],"genre_scores_gemma":[0.64320284,0.0005594841,0.34385943,0.00028930558,0.000111630994,0.00019304061,0.0029485105,0.00021433363,0.008621429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99958557,0.000019565285,0.000018699619,0.0001384527,0.00014185092,0.00009595158],"domain_scores_gemma":[0.9997154,0.000033521857,0.000022726803,0.000031373787,0.00016895722,0.00002797295],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042839404,0.0010088326,0.0009866956,0.00196345,0.00043847525,0.00082082365,0.0015365147,0.00053004856,0.0015858943],"category_scores_gemma":[0.0009137936,0.0002761937,0.0007774345,0.0012198833,0.00021559023,0.0011839726,0.0008163393,0.0007346408,0.0006452666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002574264,0.00021625588,0.00403498,0.00007428807,0.000084717714,0.000108925764,0.000078350335,0.028315188,0.025438761,0.0013845229,0.0070479442,0.93295866],"study_design_scores_gemma":[0.000029028033,0.00010425141,0.0055588004,0.00000893836,0.000053271604,0.000114451825,0.000050143844,0.9759113,0.0146789225,0.0009704442,0.002499599,0.000020725782],"about_ca_topic_score_codex":0.02819028,"about_ca_topic_score_gemma":0.029942991,"teacher_disagreement_score":0.02819028,"about_ca_system_score_codex":0.0007724042,"about_ca_system_score_gemma":0.0014903636,"threshold_uncertainty_score":0.056052387},"labels":[],"label_agreement":null},{"id":"W4388449994","doi":"10.1109/istas57930.2023.10305989","title":"Enhanced Video Surveillance Systems for “Signal for Help” Detection on Edge Devices","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Computer science; SIGNAL (programming language); Enhanced Data Rates for GSM Evolution; Detection theory; Signal processing; Telecommunications; Detector","score_opus":0.046149261998596314,"score_gpt":0.31885259788755727,"score_spread":0.27270333588896095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388449994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.15954527,0.0011336198,0.8248016,0.00034568753,0.00034942798,0.00037788265,0.00046434102,0.0050718444,0.0079102265],"genre_scores_gemma":[0.72650295,0.0005198877,0.26500773,0.0003922698,0.00012675859,0.00016908135,0.00065631303,0.00007665814,0.006548304],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99950147,0.00012976378,0.000030907697,0.00009755697,0.00018655963,0.000053782747],"domain_scores_gemma":[0.99932075,0.00020168876,0.00006655323,0.0000787348,0.00029795928,0.000034359637],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005010377,0.00048292798,0.0004055597,0.0006388708,0.00018729082,0.0005586194,0.00075012736,0.00065644947,0.0025477102],"category_scores_gemma":[0.0014787496,0.0001574795,0.0002427762,0.00027240484,0.00012142222,0.00075860566,0.0004843368,0.00040247795,0.001011765],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012972634,0.0005202692,0.008123286,0.00027448896,0.00009998685,0.00055827625,0.00029566415,0.011404976,0.3128409,0.004029764,0.01254512,0.6480101],"study_design_scores_gemma":[0.00018014699,0.0016691344,0.020209052,0.00013550323,0.00015394657,0.0011136534,0.00017691773,0.7525956,0.19264208,0.0016178053,0.029396048,0.00011014685],"about_ca_topic_score_codex":0.0014449492,"about_ca_topic_score_gemma":0.0017883085,"teacher_disagreement_score":0.0025477102,"about_ca_system_score_codex":0.00027000843,"about_ca_system_score_gemma":0.00021808024,"threshold_uncertainty_score":0.008522928},"labels":[],"label_agreement":null},{"id":"W4388597122","doi":"10.1007/s11042-023-17363-w","title":"Building height estimation from street-view imagery using deep learning, image processing and automated geospatial analysis","year":2023,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Statistics Canada","funders":"","keywords":"Geospatial analysis; Computer science; Footprint; Field (mathematics); Scalability; Deep learning; Data set; Data science; Image processing; Set (abstract data type); Artificial intelligence; Machine learning; Data mining; Image (mathematics); Remote sensing; Database","score_opus":0.028749338265065226,"score_gpt":0.3312389039748356,"score_spread":0.30248956570977037,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388597122","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1470441,0.0006301998,0.8447644,0.00019028633,0.00008141127,0.000041619653,0.0013123611,0.0031014937,0.002834108],"genre_scores_gemma":[0.76123005,0.00050280767,0.2315538,0.00009198303,0.00008174955,0.00003545254,0.0030192754,0.00018264039,0.0033021914],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980956,0.000015286043,0.0000066356083,0.000060025843,0.00005827233,0.000050242223],"domain_scores_gemma":[0.9997832,0.000045823414,0.00003359166,0.000036266832,0.000080017955,0.000021092694],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00018917154,0.00066621194,0.0006150546,0.0017284615,0.0001962399,0.00065108977,0.00075873797,0.0005777238,0.0012658897],"category_scores_gemma":[0.0005894186,0.00047763254,0.0007236459,0.0016189127,0.00025250888,0.00068480807,0.0006362376,0.00081176305,0.001005801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00016193635,0.00021662761,0.010523159,0.00013769203,0.00017773137,0.00013551,0.00007865975,0.29062712,0.033273064,0.0021734019,0.006092428,0.65640265],"study_design_scores_gemma":[0.0000038264316,0.000009297453,0.0033341886,0.0000069676425,0.000013313797,0.000027759239,0.0000161725,0.99224794,0.002957953,0.0009457946,0.0004302682,0.0000065413697],"about_ca_topic_score_codex":0.015095666,"about_ca_topic_score_gemma":0.036100946,"teacher_disagreement_score":0.015095666,"about_ca_system_score_codex":0.00039858377,"about_ca_system_score_gemma":0.00071306987,"threshold_uncertainty_score":0.030015588},"labels":[],"label_agreement":null},{"id":"W4388757355","doi":"10.1007/s12293-023-00402-2","title":"An optimization method for pruning rates of each layer in CNN based on the GA-SMSM","year":2023,"lang":"en","type":"article","venue":"Memetic Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"National Natural Science Foundation of China","keywords":"Pruning; Complex system; Layer (electronics); Computer science; Artificial intelligence; Algorithm; Pattern recognition (psychology); Biological system; Materials science; Nanotechnology; Biology; Botany","score_opus":0.08347497233303464,"score_gpt":0.3928723355189769,"score_spread":0.30939736318594224,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388757355","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":[0.022436297,0.0008030551,0.97122264,0.00031938925,0.00016498314,0.000099017605,0.000066628585,0.00068554987,0.0042026006],"genre_scores_gemma":[0.4804458,0.00054327084,0.5094428,0.00041260628,0.00015361846,0.0005827217,0.00024733203,0.00027184945,0.007899932],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996755,0.00008243401,0.000026374195,0.000082611885,0.000081945145,0.000051127707],"domain_scores_gemma":[0.99946433,0.00025810182,0.000044044013,0.000032790846,0.00017916286,0.000021502381],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010083714,0.0009979582,0.0011422923,0.00087446393,0.0005386624,0.0007039993,0.0019124208,0.001444718,0.0030124034],"category_scores_gemma":[0.0025333285,0.00056496356,0.00090485235,0.0006186842,0.00050171703,0.0008113126,0.00064273144,0.0012178834,0.00037947862],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011421359,0.00008358292,0.0009279457,0.00012498377,0.000110202,0.00008071105,0.00006829611,0.8297932,0.004659332,0.0077088843,0.003187814,0.15314081],"study_design_scores_gemma":[0.0000076401175,0.00001702248,0.000104520834,0.000008847717,0.000014107285,0.00001512201,0.000004611847,0.9983499,0.00044341464,0.0007702416,0.00026137152,0.000003174265],"about_ca_topic_score_codex":0.009044876,"about_ca_topic_score_gemma":0.011395325,"teacher_disagreement_score":0.009044876,"about_ca_system_score_codex":0.001041567,"about_ca_system_score_gemma":0.0014506861,"threshold_uncertainty_score":0.01798445},"labels":[],"label_agreement":null},{"id":"W4388757621","doi":"10.1109/uemcon59035.2023.10315980","title":"Building a Robot to Identify Electric Vehicles from Infrared Images","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Defence Research and Development Canada; National Research Council Canada; University of Victoria; Université Laval","funders":"","keywords":"Tilt (camera); Process (computing); Computer vision; Computer science; Artificial intelligence; Robot; Identification (biology); Perspective (graphical); Mobile robot; Data collection; Engineering","score_opus":0.03662055174779088,"score_gpt":0.3521729477498939,"score_spread":0.315552396002103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388757621","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022448448,0.00007688262,0.9684143,0.0001727864,0.00010059902,0.0003074035,0.00014741957,0.005959067,0.0023730695],"genre_scores_gemma":[0.094916694,0.0001052098,0.89869547,0.00015559695,0.00003167844,0.00030529973,0.00032435672,0.000119742355,0.005345813],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965394,0.000031811298,0.000015819202,0.0001371715,0.00012293091,0.00003819355],"domain_scores_gemma":[0.99951804,0.00007723726,0.000037620364,0.000079037454,0.00024105443,0.00004696881],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006022993,0.00058137113,0.00058570603,0.00050785264,0.00045125125,0.0006042105,0.0013907823,0.0007377175,0.004437787],"category_scores_gemma":[0.0008730423,0.00047702467,0.00036704118,0.000308356,0.00043955978,0.0011194001,0.000724486,0.00075990375,0.0019030265],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026187542,0.00045730133,0.0054414566,0.0002780812,0.0001001726,0.00030040077,0.00035238534,0.0229525,0.34377924,0.0049903356,0.011975872,0.6091104],"study_design_scores_gemma":[0.00014739591,0.0012094598,0.01134964,0.00011862031,0.00014051089,0.00097578537,0.00039380015,0.6673297,0.25304726,0.0028488738,0.062262125,0.00017678064],"about_ca_topic_score_codex":0.0039090617,"about_ca_topic_score_gemma":0.0035874322,"teacher_disagreement_score":0.004437787,"about_ca_system_score_codex":0.00036391016,"about_ca_system_score_gemma":0.0009855588,"threshold_uncertainty_score":0.014845848},"labels":[],"label_agreement":null},{"id":"W4388831687","doi":"10.1186/s10033-023-00962-x","title":"End-to-End Joint Multi-Object Detection and Tracking for Intelligent Transportation Systems","year":2023,"lang":"en","type":"article","venue":"Chinese Journal of Mechanical Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Object detection; Video tracking; Artificial intelligence; Tracking (education); BitTorrent tracker; Minimum bounding box; Tracking system; Computer vision; Bounding overwatch; Pipeline (software); Vehicle tracking system; Feature extraction; Feature (linguistics); Intelligent transportation system; Object (grammar); Real-time computing; Pattern recognition (psychology); Engineering; Eye tracking; Kalman filter","score_opus":0.03832027599390821,"score_gpt":0.29328962240029793,"score_spread":0.25496934640638974,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388831687","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051088054,0.00035978458,0.9397778,0.00019821138,0.000109529865,0.000100582845,0.00034419267,0.005485149,0.0025366582],"genre_scores_gemma":[0.76921815,0.00018482591,0.22419173,0.00013855367,0.00005978325,0.00014540381,0.0011830657,0.00011483559,0.004763574],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995359,0.000080012665,0.000020270714,0.00015754398,0.00011928756,0.00008702041],"domain_scores_gemma":[0.9993223,0.00013587448,0.000053707405,0.000110515335,0.0003282218,0.000049439845],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010346145,0.0009129416,0.0006834597,0.0009229854,0.0005331277,0.0007446429,0.0010607949,0.0008809814,0.0019429964],"category_scores_gemma":[0.0013937432,0.00028801218,0.00039124,0.0008053861,0.0003252302,0.0010503317,0.00094385125,0.00074199645,0.0006894489],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000828296,0.00036250713,0.006244896,0.0001371248,0.00015886183,0.00023832322,0.00007895357,0.42859405,0.022841405,0.0036658007,0.012539588,0.5243102],"study_design_scores_gemma":[0.0000067737474,0.000037470298,0.00055515015,0.0000036097001,0.000011071827,0.000017618037,0.000008735238,0.9930769,0.0042412956,0.0010351532,0.0010003607,0.000005810156],"about_ca_topic_score_codex":0.010631534,"about_ca_topic_score_gemma":0.012146415,"teacher_disagreement_score":0.010631534,"about_ca_system_score_codex":0.0008995222,"about_ca_system_score_gemma":0.001360483,"threshold_uncertainty_score":0.021139324},"labels":[],"label_agreement":null},{"id":"W4388877655","doi":"10.1063/5.0150392","title":"Deep learning-based vehicles tracking in traffic with image processing techniques","year":2023,"lang":"en","type":"article","venue":"AIP conference proceedings","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Deep learning; Image processing; Tracking (education); Image (mathematics)","score_opus":0.030095371200214906,"score_gpt":0.29731345122077374,"score_spread":0.26721808002055886,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4388877655","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086553,0.00046966434,0.90935886,0.00019606925,0.00009031532,0.000026918635,0.00016180736,0.0006459998,0.0024973548],"genre_scores_gemma":[0.8459381,0.0005092146,0.1465638,0.00013527142,0.00009599683,0.000043063865,0.0005027937,0.000076168864,0.00613565],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997887,0.00002815827,0.000007775278,0.00006302268,0.00004941709,0.000062889514],"domain_scores_gemma":[0.9997408,0.000081648046,0.00003466602,0.000027744642,0.00009226332,0.000022901218],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00043655364,0.0005676148,0.0005896259,0.00088226295,0.00029247216,0.00072442077,0.00084531907,0.0007881446,0.0010848886],"category_scores_gemma":[0.0010527791,0.0004326989,0.0005851263,0.001265768,0.00034383274,0.0008217214,0.0007289404,0.0009661332,0.00052508037],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019965296,0.00021982318,0.0046214336,0.000073751886,0.00009888832,0.00006442387,0.000059118895,0.5179044,0.016020214,0.0060920827,0.002819965,0.45182624],"study_design_scores_gemma":[0.0000014434527,0.000008934002,0.00034826467,0.000002423182,0.00000446872,0.0000058001524,0.0000030105607,0.9977543,0.0009996532,0.00068932277,0.00018065235,0.0000018916554],"about_ca_topic_score_codex":0.010300822,"about_ca_topic_score_gemma":0.00996214,"teacher_disagreement_score":0.010300822,"about_ca_system_score_codex":0.00054533745,"about_ca_system_score_gemma":0.0007964115,"threshold_uncertainty_score":0.020481706},"labels":[],"label_agreement":null},{"id":"W4389041073","doi":"10.1109/icce-asia59966.2023.10326403","title":"A Deep Multi-Object Tracking Technique in Swimming Video Scenes","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"National Research Foundation","keywords":"Computer vision; Video tracking; Artificial intelligence; Tracking (education); Computer science; Object (grammar); Set (abstract data type); Track (disk drive); Feature (linguistics)","score_opus":0.05265429137839961,"score_gpt":0.33425381307079927,"score_spread":0.28159952169239966,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389041073","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":[0.017477801,0.0003596031,0.98023933,0.00007803589,0.00007511749,0.000051707513,0.00005517496,0.00077627687,0.0008869436],"genre_scores_gemma":[0.28541136,0.00078491884,0.7049522,0.00024066296,0.00007632591,0.00011111435,0.00055102486,0.00015196021,0.0077205626],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996039,0.000031151372,0.000021709098,0.00016198422,0.000113418784,0.000067852874],"domain_scores_gemma":[0.9997644,0.000031733176,0.000025279613,0.000049660986,0.00009854007,0.000030361756],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00070897455,0.00076986203,0.0007531734,0.0012670229,0.0007193962,0.0007877936,0.0010152744,0.00086265994,0.00093449064],"category_scores_gemma":[0.0008898011,0.00036484355,0.00089471415,0.001342448,0.00039364706,0.0014090275,0.0011292467,0.0010000003,0.00048429132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014417616,0.00010145646,0.0023978106,0.00010871431,0.000098781435,0.00016592072,0.00016173594,0.040135805,0.091866225,0.0034947458,0.0027281493,0.85859656],"study_design_scores_gemma":[0.000013460568,0.00013597467,0.0033729917,0.000022736587,0.000048751066,0.00022540694,0.000051370185,0.96139437,0.027612949,0.0019752705,0.0051156525,0.000031168707],"about_ca_topic_score_codex":0.01084739,"about_ca_topic_score_gemma":0.012775818,"teacher_disagreement_score":0.01084739,"about_ca_system_score_codex":0.0005141101,"about_ca_system_score_gemma":0.0010542293,"threshold_uncertainty_score":0.021568477},"labels":[],"label_agreement":null},{"id":"W4389144641","doi":"10.32604/cmc.2023.043168","title":"Shadow Extraction and Elimination of Moving Vehicles for Tracking Vehicles","year":2023,"lang":"en","type":"article","venue":"Computers, materials & continua/Computers, materials & continua (Print)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Saskatchewan","funders":"King Saud University","keywords":"Shadow (psychology); Artificial intelligence; Computer vision; Computer science; Tracking (education); Constant false alarm rate; Process (computing); Vehicle tracking system; Gaussian; Transformation (genetics); Intelligent transportation system; Engineering; Kalman filter","score_opus":0.030551621777477944,"score_gpt":0.2957329756156452,"score_spread":0.26518135383816727,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389144641","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":[0.081132144,0.0007530316,0.9123936,0.0001062355,0.00011820813,0.000107896834,0.00022400857,0.0017115497,0.0034533148],"genre_scores_gemma":[0.6235111,0.0010493239,0.36695123,0.00010804592,0.0000845162,0.000064271524,0.0009562006,0.00024194966,0.007033372],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999559,0.00003950936,0.000019809477,0.00011993058,0.00019501698,0.000066703695],"domain_scores_gemma":[0.99967015,0.000050033217,0.00004181217,0.00006195045,0.00015889789,0.000017193113],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037283276,0.00056709675,0.0005404302,0.0013335636,0.0003564065,0.00066579715,0.0005499342,0.00035643284,0.0011651315],"category_scores_gemma":[0.0009259512,0.00025110296,0.0007265329,0.00073601987,0.00024918502,0.0006519647,0.00048655886,0.00044724444,0.00081996684],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019087964,0.000104312894,0.007562619,0.00021719535,0.000072621224,0.00023363052,0.00018946736,0.023099735,0.12131628,0.0023272734,0.0033391474,0.8413468],"study_design_scores_gemma":[0.000029029017,0.0001839803,0.026474046,0.000050239123,0.00016154809,0.00095669046,0.000249644,0.7297464,0.2246505,0.0027584508,0.0146767115,0.00006277573],"about_ca_topic_score_codex":0.004742896,"about_ca_topic_score_gemma":0.0066487393,"teacher_disagreement_score":0.004742896,"about_ca_system_score_codex":0.00037922454,"about_ca_system_score_gemma":0.0007616179,"threshold_uncertainty_score":0.009430587},"labels":[],"label_agreement":null},{"id":"W4389164040","doi":"10.1007/978-3-031-47969-4_20","title":"Domain Generalization for Foreground Segmentation Using Federated Learning","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Forgetting; Generalization; Regularization (linguistics); Domain (mathematical analysis); Machine learning; Encoder; Object (grammar); Domain knowledge; Pattern recognition (psychology)","score_opus":0.05214545926654969,"score_gpt":0.31980614416135816,"score_spread":0.2676606848948085,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389164040","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":[0.00884259,0.00023515694,0.98722935,0.000059532114,0.000030700052,0.00003653476,0.00011831237,0.0027593395,0.00068847934],"genre_scores_gemma":[0.27931163,0.0003773183,0.7122833,0.00025371494,0.00008865692,0.00014058745,0.001886684,0.00060438435,0.0050536916],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99898726,0.00017521736,0.00005991291,0.00044365585,0.00017183015,0.00016213491],"domain_scores_gemma":[0.99842346,0.0005412494,0.00008120569,0.0005748045,0.000302474,0.000076934906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016582261,0.0013419572,0.0021398673,0.0017524384,0.0007459334,0.0013220268,0.003046524,0.0021616523,0.004521196],"category_scores_gemma":[0.002694833,0.0006882715,0.0022767587,0.0017228354,0.00094984355,0.0024099937,0.0032180913,0.002543653,0.0022800537],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004472271,0.00025635844,0.0011048244,0.00012588793,0.00012902982,0.00013314877,0.00012404549,0.18235964,0.015833095,0.007779963,0.0060621533,0.7856447],"study_design_scores_gemma":[0.000010037244,0.000028217424,0.00015032919,0.000009566067,0.000015532296,0.000041577397,0.000021136615,0.9854732,0.0041893204,0.009240903,0.00081281835,0.0000073308724],"about_ca_topic_score_codex":0.0076631615,"about_ca_topic_score_gemma":0.0076669483,"teacher_disagreement_score":0.0076631615,"about_ca_system_score_codex":0.0011810501,"about_ca_system_score_gemma":0.0012900789,"threshold_uncertainty_score":0.015237093},"labels":[],"label_agreement":null},{"id":"W4389170220","doi":"10.1007/978-3-031-47969-4_40","title":"Strategic Incorporation of Synthetic Data for Performance Enhancement in Deep Learning A Case Study on Object Tracking Tasks","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Task (project management); Variation (astronomy); Object (grammar); Tracking (education); Similarity (geometry); Machine learning; Synthetic data; Process (computing); Artificial neural network; Deep learning; Video tracking; Data mining; Pattern recognition (psychology); Image (mathematics)","score_opus":0.11178065121880408,"score_gpt":0.34066914054762276,"score_spread":0.22888848932881867,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4389170220","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":[0.7946143,0.0014762677,0.1902936,0.0009787494,0.00017772909,0.00015754877,0.00042377843,0.001582459,0.010295632],"genre_scores_gemma":[0.93629044,0.00022088087,0.059961308,0.00007709635,0.000021525126,0.00004539831,0.00045931962,0.00011109066,0.0028129239],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99928147,0.00037011845,0.00003703545,0.000090817586,0.00014443572,0.00007621586],"domain_scores_gemma":[0.996209,0.0026484267,0.00009769991,0.00040798308,0.0005270399,0.00010983638],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002121291,0.00063899223,0.00041014122,0.00028414358,0.00029801528,0.0010090782,0.0008592043,0.0009968005,0.001302694],"category_scores_gemma":[0.00604268,0.00020521421,0.00027153155,0.00065743463,0.0004888843,0.0009896655,0.000803035,0.0010633325,0.0004484028],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0022550276,0.0016168128,0.0065762345,0.00066920347,0.00015130382,0.00071566115,0.0006591621,0.3906376,0.057849623,0.007343138,0.008434552,0.5230916],"study_design_scores_gemma":[0.00008628314,0.0010212626,0.002162968,0.000044226792,0.00006358129,0.00022738738,0.00023115354,0.9355459,0.049496662,0.0057589174,0.005329674,0.000031941574],"about_ca_topic_score_codex":0.0026357472,"about_ca_topic_score_gemma":0.0039237794,"teacher_disagreement_score":0.0026357472,"about_ca_system_score_codex":0.0004351201,"about_ca_system_score_gemma":0.0005259569,"threshold_uncertainty_score":0.011218548},"labels":[],"label_agreement":null},{"id":"W4390044760","doi":"10.1109/icdacai59742.2023.00144","title":"Distance Estimation Based on Computer Vision","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Focus (optics); Monster; Computer vision; Object (grammar); Estimation; Engineering","score_opus":0.022360306490059198,"score_gpt":0.3224408758381476,"score_spread":0.3000805693480884,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390044760","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0149680665,0.0024829488,0.9741441,0.00013428016,0.00018370905,0.00014060418,0.00070355716,0.0029845198,0.004258182],"genre_scores_gemma":[0.39649263,0.0041987165,0.58060336,0.00030401192,0.00032466164,0.00023286317,0.0071561085,0.00045896333,0.010228715],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9975847,0.00024587463,0.00012633955,0.001071557,0.00079646363,0.0001751004],"domain_scores_gemma":[0.9984022,0.00037569966,0.0002120617,0.0003215191,0.0006330208,0.00005548164],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008579768,0.0016490945,0.001473334,0.0043611377,0.0005088838,0.0019307248,0.0023175043,0.0014130076,0.0031979352],"category_scores_gemma":[0.0047822567,0.00047106098,0.0012601267,0.0042705457,0.0005491878,0.0029142976,0.0011014139,0.0017896157,0.0053932113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018033122,0.00023223409,0.0056532165,0.0003755971,0.00021300637,0.000089232475,0.00006552357,0.054543845,0.014025164,0.006215574,0.012680829,0.9057254],"study_design_scores_gemma":[0.000027255484,0.00022122457,0.011342177,0.000117113595,0.000084407424,0.00070252,0.000104151564,0.92877156,0.02748404,0.0103044445,0.020748347,0.00009282498],"about_ca_topic_score_codex":0.013930615,"about_ca_topic_score_gemma":0.010576589,"teacher_disagreement_score":0.013930615,"about_ca_system_score_codex":0.0010068618,"about_ca_system_score_gemma":0.00095313665,"threshold_uncertainty_score":0.027699053},"labels":[],"label_agreement":null},{"id":"W4390099755","doi":"10.1109/robio58561.2023.10354731","title":"Reducing the Computational Cost of Transformers for Person Re-identification","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Transformer; Computer science; Computation; Computational complexity theory; FLOPS; Software deployment; Quadratic equation; Computer engineering; Artificial intelligence; Algorithm; Voltage; Parallel computing; Engineering; Electrical engineering; Mathematics","score_opus":0.0921240282857637,"score_gpt":0.35194003738919616,"score_spread":0.25981600910343244,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390099755","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.074892014,0.0019335663,0.8726321,0.0008242628,0.0008100564,0.00041935014,0.0011288889,0.037969604,0.009390115],"genre_scores_gemma":[0.54539746,0.0014056904,0.42898718,0.0004985849,0.00024997583,0.0002772063,0.0042433767,0.0010676952,0.017872743],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9988624,0.0001406109,0.000071579336,0.00035659524,0.0003920906,0.00017674846],"domain_scores_gemma":[0.9987877,0.00031271562,0.00007472059,0.00048653834,0.00028365405,0.000054702738],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089039083,0.0015033182,0.001365625,0.0015899143,0.00074309076,0.0011667901,0.0025639639,0.0007153193,0.014743581],"category_scores_gemma":[0.004503841,0.00053062104,0.0011784626,0.0015416222,0.0005764018,0.0037540419,0.0020964635,0.0012178927,0.007482731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005762344,0.00020959062,0.0019627903,0.00016258429,0.00007155807,0.00018307808,0.00010113329,0.01650101,0.02101002,0.004819771,0.028010614,0.92639166],"study_design_scores_gemma":[0.00014086731,0.0002683148,0.004183923,0.00003277524,0.00012401775,0.000980146,0.0002771001,0.9065066,0.046455905,0.013703448,0.02725801,0.0000688661],"about_ca_topic_score_codex":0.015533064,"about_ca_topic_score_gemma":0.02647791,"teacher_disagreement_score":0.015533064,"about_ca_system_score_codex":0.0010766053,"about_ca_system_score_gemma":0.0021366645,"threshold_uncertainty_score":0.049322248},"labels":[],"label_agreement":null},{"id":"W4390099882","doi":"10.1109/robio58561.2023.10354902","title":"A Joint Tracking System: Robot is Online to Access Surveillance Views","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"Youth Foundation","keywords":"Computer science; Joint (building); Robot; Tracking system; Tracking (education); Artificial intelligence; Computer vision; Engineering; Kalman filter","score_opus":0.18800307850217696,"score_gpt":0.39186722699540305,"score_spread":0.2038641484932261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390099882","genre_codex":"software","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06744057,0.0016178105,0.41360563,0.0003510731,0.0009034233,0.0014441317,0.06718149,0.4239677,0.02348814],"genre_scores_gemma":[0.35013387,0.0006207627,0.45892203,0.0007026305,0.00023657939,0.0014415921,0.15951139,0.004072745,0.024358429],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9990013,0.000072254064,0.000050593706,0.00056878204,0.00019683405,0.00011017975],"domain_scores_gemma":[0.9990165,0.000089688416,0.00008951931,0.0004484806,0.00025190035,0.00010385026],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00073216285,0.0016072638,0.0013274817,0.0011878749,0.00054935925,0.000959863,0.0016261006,0.0010737655,0.015446183],"category_scores_gemma":[0.0019490859,0.00051076495,0.0005859821,0.0008746273,0.00022414543,0.0015618303,0.0017494239,0.001072646,0.016964566],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0023429054,0.00058102794,0.009461369,0.0011015788,0.00030888815,0.00035995533,0.00035749763,0.0038333586,0.07047819,0.0023957246,0.31261128,0.5961683],"study_design_scores_gemma":[0.00039050166,0.0014176995,0.049646966,0.00024329696,0.00022902111,0.0018929765,0.00035359518,0.38375247,0.1771656,0.0052180123,0.3793139,0.00037592155],"about_ca_topic_score_codex":0.0058859773,"about_ca_topic_score_gemma":0.009313461,"teacher_disagreement_score":0.015446183,"about_ca_system_score_codex":0.00042404115,"about_ca_system_score_gemma":0.0008869675,"threshold_uncertainty_score":0.051672637},"labels":[],"label_agreement":null},{"id":"W4390116162","doi":"10.1016/j.cviu.2023.103906","title":"Learning feature contexts by transformer and CNN hybrid deep network for weakly supervised person search","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Discriminative model; Transformer; Feature (linguistics); Machine learning; Feature learning; Pattern recognition (psychology); Feature extraction; Engineering","score_opus":0.043497987542860536,"score_gpt":0.30424161875920125,"score_spread":0.2607436312163407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390116162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.086003244,0.0010984296,0.90541494,0.00025664145,0.00014370507,0.000101813275,0.0006227565,0.0029061786,0.003452257],"genre_scores_gemma":[0.83886224,0.0006157707,0.14821115,0.00037186118,0.00013986202,0.00008288629,0.002139352,0.00020894704,0.009367943],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951243,0.000061659666,0.000019555982,0.00020958779,0.000092761395,0.00010401532],"domain_scores_gemma":[0.9997552,0.00005125599,0.000025783207,0.00006864043,0.00006749415,0.000031694053],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038745988,0.00090319803,0.0013837974,0.00089807075,0.00047604917,0.00069868733,0.0014776419,0.0010651934,0.002907097],"category_scores_gemma":[0.0010278411,0.0004440552,0.00085595006,0.0010210259,0.00042745622,0.0016760988,0.0016595735,0.0011871035,0.0014532905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009380767,0.0006397811,0.005780348,0.00019790161,0.00018997143,0.00033335362,0.00014562308,0.103581496,0.0441645,0.009742299,0.014227306,0.82005936],"study_design_scores_gemma":[0.0000127726935,0.000078156845,0.00070463156,0.000010183201,0.000029470622,0.00010615371,0.000029866625,0.98871636,0.004669748,0.0044251876,0.0012078019,0.000009568066],"about_ca_topic_score_codex":0.010568694,"about_ca_topic_score_gemma":0.018574229,"teacher_disagreement_score":0.010568694,"about_ca_system_score_codex":0.00056942547,"about_ca_system_score_gemma":0.0009886237,"threshold_uncertainty_score":0.021014392},"labels":[],"label_agreement":null},{"id":"W4390224930","doi":"10.1109/dtpi59677.2023.10365413","title":"Robust Learning for Autonomous Driving Perception Tasks in Cyber-Physical-Social Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Robustness (evolution); Cyber-physical system; Computer science; Perception; Constrained optimization problem; Resilience (materials science); Optimization problem; Domain (mathematical analysis); Artificial intelligence; Algorithm; Mathematics","score_opus":0.05702244591090063,"score_gpt":0.31704647362094773,"score_spread":0.2600240277100471,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390224930","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.014869697,0.0001218789,0.9842158,0.000109298744,0.000011383825,0.000019630732,0.000018725881,0.00015302148,0.00048047854],"genre_scores_gemma":[0.9198061,0.00016600761,0.07854999,0.00008543082,0.00004004868,0.00010221191,0.00009939898,0.000062945,0.0010879339],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999278,0.0001974587,0.000036261783,0.00024809752,0.00014264756,0.00009765616],"domain_scores_gemma":[0.9989517,0.00056361855,0.00018354371,0.00011419595,0.0001308,0.000056165034],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013744896,0.00089749286,0.00094418356,0.00041402335,0.00042055902,0.0008585185,0.0012201815,0.0010208917,0.00091104815],"category_scores_gemma":[0.0041481145,0.00045547175,0.0007967611,0.00035323348,0.0013400096,0.0014905666,0.0021563189,0.0016123734,0.00019484192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056860412,0.000027896393,0.00035858832,0.000048995942,0.000031055923,0.000037681093,0.000065969165,0.9618922,0.0026658608,0.008019656,0.00026706103,0.026528195],"study_design_scores_gemma":[0.000002197765,0.000016444836,0.000060227154,0.0000017738058,0.0000018624409,0.0000039998426,0.0000050188933,0.9967675,0.00039307022,0.0026680666,0.000076655604,0.0000031622596],"about_ca_topic_score_codex":0.0037359225,"about_ca_topic_score_gemma":0.0022665004,"teacher_disagreement_score":0.0037359225,"about_ca_system_score_codex":0.00088584964,"about_ca_system_score_gemma":0.0009889317,"threshold_uncertainty_score":0.007428348},"labels":[],"label_agreement":null},{"id":"W4390412297","doi":"10.18280/ts.400647","title":"Enhancing Drowning Surveillance with a Hybrid Vision Transformer Model: A Deep Learning Approach","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Ministry of Education of the People's Republic of China","keywords":"Transformer; Artificial intelligence; Computer science; Deep learning; Computer vision; Machine learning; Real-time computing; Computer security; Engineering; Electrical engineering","score_opus":0.01769038214491304,"score_gpt":0.26087578150400453,"score_spread":0.2431853993590915,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390412297","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17755063,0.0015108824,0.8092847,0.0006652629,0.00017587045,0.000105883504,0.00055070303,0.0031058348,0.007050222],"genre_scores_gemma":[0.91236174,0.00063146173,0.07823634,0.00036264546,0.000055597837,0.000062731466,0.001069439,0.0000819022,0.0071381414],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9999043,0.000013097937,0.000003915888,0.00003212509,0.000022316557,0.000024321896],"domain_scores_gemma":[0.9999018,0.00002789818,0.000012501923,0.000011611583,0.000034866676,0.000011210776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002499674,0.00070563925,0.00049508695,0.00048897445,0.00013578362,0.0005283439,0.0008792174,0.0005452358,0.000890441],"category_scores_gemma":[0.0005439558,0.0002509826,0.00054024946,0.0003528163,0.00021969662,0.0006673211,0.0005843031,0.00079660735,0.0003928044],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002475864,0.00028917694,0.004676973,0.00010848154,0.0001591367,0.00015642989,0.000058250625,0.59832186,0.021394327,0.003486138,0.0059732976,0.3651283],"study_design_scores_gemma":[0.000003044677,0.00001804888,0.00020654313,0.0000035715746,0.000008728701,0.000011544268,0.0000035845721,0.99797684,0.0011281392,0.0003947714,0.0002425961,0.0000026051744],"about_ca_topic_score_codex":0.010361593,"about_ca_topic_score_gemma":0.012004426,"teacher_disagreement_score":0.010361593,"about_ca_system_score_codex":0.0006068187,"about_ca_system_score_gemma":0.0005977366,"threshold_uncertainty_score":0.020602524},"labels":[],"label_agreement":null},{"id":"W4390413994","doi":"10.1016/j.cviu.2023.103905","title":"IGMG: Instance-guided multi-granularity for domain generalizable person re-identification","year":2023,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Computer science; Granularity; Artificial intelligence; Machine learning; Overfitting; Embedding; Source code; Robustness (evolution); Identification (biology); Generalization; Normalization (sociology); Feature (linguistics); Benchmark (surveying); Artificial neural network","score_opus":0.19278077464797197,"score_gpt":0.3740160916887619,"score_spread":0.1812353170407899,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390413994","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009149819,0.00052626635,0.9413255,0.00017493035,0.00010844873,0.0001654758,0.0013255959,0.046122283,0.0011017573],"genre_scores_gemma":[0.17570485,0.0002924543,0.8116344,0.000547183,0.00008233887,0.00018053043,0.0055523114,0.002977003,0.0030288452],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99832743,0.0002503808,0.000085443426,0.0006893545,0.0004057614,0.00024171977],"domain_scores_gemma":[0.9982292,0.00041069314,0.00011947829,0.0009718868,0.00014890997,0.00011990028],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015214512,0.0021634982,0.0033735347,0.0025856884,0.00083478563,0.001871254,0.004083184,0.0026939565,0.00848747],"category_scores_gemma":[0.004423449,0.0010872921,0.0024026954,0.002269489,0.00083583506,0.003383285,0.006755185,0.0035859921,0.004755827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015297452,0.00051270984,0.0028075825,0.0005242606,0.00038893177,0.0006360085,0.00043421632,0.06341499,0.04110923,0.008754484,0.052559476,0.8273283],"study_design_scores_gemma":[0.00008113692,0.00010496574,0.00095536397,0.000044410124,0.000059857317,0.00023281336,0.0001443495,0.9528646,0.011377115,0.02458738,0.0095125735,0.00003559189],"about_ca_topic_score_codex":0.008318763,"about_ca_topic_score_gemma":0.013490862,"teacher_disagreement_score":0.00848747,"about_ca_system_score_codex":0.0008765198,"about_ca_system_score_gemma":0.0010627132,"threshold_uncertainty_score":0.028393388},"labels":[],"label_agreement":null},{"id":"W4390577888","doi":"10.1109/tmm.2023.3345180","title":"Disentangled Representation Learning for Controllable Person Image Generation","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Multimedia","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Feature learning; Encoder; Artificial intelligence; Transformer; Component (thermodynamics); Segmentation; Pattern recognition (psychology); Representation (politics); Computer vision; Machine learning","score_opus":0.053821500640456724,"score_gpt":0.3332741873070718,"score_spread":0.2794526866666151,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390577888","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":[0.00959642,0.000094849565,0.9885717,0.00006740527,0.0000195149,0.00002520101,0.00007468751,0.00069751876,0.0008526454],"genre_scores_gemma":[0.5623228,0.0002978566,0.4277247,0.0004194797,0.000062263454,0.00015732874,0.001017319,0.0003718763,0.007626318],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995975,0.00009041595,0.000011665703,0.00014886586,0.00010075506,0.0000507429],"domain_scores_gemma":[0.99954814,0.0001775536,0.000051033705,0.00011943051,0.00006302134,0.00004081906],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051564083,0.00071930606,0.0005312131,0.00034449995,0.00016371756,0.00042769912,0.0011749117,0.0007690144,0.0031047077],"category_scores_gemma":[0.0017281899,0.00036015184,0.0007124885,0.0003112588,0.0005768572,0.0010921578,0.0010699537,0.001404205,0.0009934362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003002585,0.00022392899,0.0013716248,0.00018702564,0.00010272167,0.00026125452,0.00017988516,0.44051084,0.057972915,0.02614676,0.00542047,0.46732235],"study_design_scores_gemma":[0.00001236137,0.00004867096,0.0001698981,0.000005859202,0.000009681439,0.00007195902,0.0000091338725,0.9851479,0.007850404,0.005459195,0.0012065902,0.000008394695],"about_ca_topic_score_codex":0.0016255929,"about_ca_topic_score_gemma":0.002504577,"teacher_disagreement_score":0.0031047077,"about_ca_system_score_codex":0.00046138305,"about_ca_system_score_gemma":0.00044082617,"threshold_uncertainty_score":0.010386288},"labels":[],"label_agreement":null},{"id":"W4390674622","doi":"10.1080/02681102.2023.2298876","title":"Visual imagery and the informal city: examining 360-degree imaging technologies for informal settlement representation","year":2024,"lang":"en","type":"article","venue":"Information Technology for Development","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Informal settlements; Settlement (finance); Perception; Representation (politics); Human settlement; Sociology; Photography; Geography; Computer science; Visual arts; Psychology; Archaeology; Political science; Art","score_opus":0.03970771439622448,"score_gpt":0.32237304959278895,"score_spread":0.28266533519656445,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390674622","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":[0.8807852,0.00093829515,0.016585028,0.0015218985,0.00004397573,0.00018650932,0.00006823024,0.000037898782,0.09983299],"genre_scores_gemma":[0.9957716,0.00030494586,0.0025069765,0.000045986228,0.000008355791,0.000048283782,0.000012056276,0.000011237674,0.0012905684],"study_design_codex":"qualitative","study_design_gemma":"observational","domain_scores_codex":[0.9967692,0.0022668156,0.00006647877,0.00018881667,0.00046597418,0.00024277372],"domain_scores_gemma":[0.9904578,0.0071661905,0.0010304056,0.00042786542,0.0006005241,0.00031721342],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0046180314,0.0003468989,0.00014605561,0.002071363,0.0020449278,0.0057492186,0.00070167135,0.0005933809,0.0040970896],"category_scores_gemma":[0.0129473675,0.00016693093,0.0002537731,0.0015968211,0.0066876514,0.004531999,0.004288416,0.0009121682,0.00024786778],"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":[0.00011720404,0.00009216328,0.024336169,0.00040516126,0.000014250839,0.00046294904,0.8483003,0.00041028703,0.0024275088,0.05300266,0.00085102033,0.06958038],"study_design_scores_gemma":[0.000011949572,0.00012376207,0.027600955,0.00045370453,0.000019009925,0.00046836096,0.92965674,0.0010415524,0.0010383385,0.0086947465,0.030864773,0.000026110172],"about_ca_topic_score_codex":0.0047746627,"about_ca_topic_score_gemma":0.0064971494,"teacher_disagreement_score":0.0057492186,"about_ca_system_score_codex":0.0025438925,"about_ca_system_score_gemma":0.0011562604,"threshold_uncertainty_score":0.024422765},"labels":[],"label_agreement":null},{"id":"W4390806948","doi":"10.1016/j.inffus.2024.102247","title":"Deep learning and multi-modal fusion for real-time multi-object tracking: Algorithms, challenges, datasets, and comparative study","year":2024,"lang":"en","type":"article","venue":"Information Fusion","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Royal Military College of Canada","funders":"Natural Science Foundation of Shandong Province; European Commission","keywords":"Computer science; Benchmark (surveying); Artificial intelligence; Video tracking; Context (archaeology); Sensor fusion; Deep learning; Machine learning; Tracking (education); Vehicle tracking system; Object detection; Set (abstract data type); Object (grammar); Pattern recognition (psychology); Kalman filter","score_opus":0.07873823765034899,"score_gpt":0.35777684114547426,"score_spread":0.27903860349512527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390806948","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1634578,0.017493175,0.80647355,0.0012403708,0.00047602493,0.00022463633,0.0020037345,0.002885946,0.005744789],"genre_scores_gemma":[0.7472611,0.0058761025,0.23580346,0.00029091793,0.0001937717,0.00016595349,0.0066088405,0.00016879514,0.0036310114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978916,0.0005945033,0.00013410908,0.00043024056,0.0007219112,0.00022767973],"domain_scores_gemma":[0.99711835,0.0012831505,0.00019034625,0.0005528059,0.0007494107,0.000105942745],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005174271,0.0014449072,0.0014956112,0.0020024155,0.0006194253,0.0017578889,0.0014782978,0.0016654902,0.0015498291],"category_scores_gemma":[0.0067991577,0.00044748772,0.0011687028,0.0028795733,0.0005954612,0.0029537221,0.0017648326,0.0017361466,0.000599361],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013091917,0.00086416496,0.009901594,0.00072560814,0.00057863345,0.00007488792,0.000114446426,0.12116745,0.008886365,0.0054261796,0.008276284,0.84267527],"study_design_scores_gemma":[0.000031519776,0.00028908692,0.0063987086,0.00011867847,0.00017259056,0.0001394725,0.00012618353,0.96849,0.011299434,0.008169982,0.0047114487,0.000052865693],"about_ca_topic_score_codex":0.011417467,"about_ca_topic_score_gemma":0.008050358,"teacher_disagreement_score":0.011417467,"about_ca_system_score_codex":0.0011783409,"about_ca_system_score_gemma":0.0016447135,"threshold_uncertainty_score":0.027364433},"labels":[],"label_agreement":null},{"id":"W4390891830","doi":"10.1016/j.trc.2023.104465","title":"Unlabeled scene adaptive crowd counting via meta-ensemble learning","year":2024,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Consistency (knowledge bases); Process (computing); Machine learning; Ensemble learning; Computer vision; Pattern recognition (psychology)","score_opus":0.1581512962531223,"score_gpt":0.40211775620052365,"score_spread":0.24396645994740135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4390891830","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":[0.022235457,0.00034143915,0.9741222,0.00016420423,0.00015017873,0.00005759737,0.00020532931,0.0014234892,0.0013000054],"genre_scores_gemma":[0.5159051,0.0004095793,0.47300678,0.00043326983,0.00043914886,0.00020600049,0.002476841,0.0004981201,0.0066251187],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982151,0.00037347784,0.00008093386,0.0006625601,0.00038210678,0.00028593757],"domain_scores_gemma":[0.9973515,0.0008433088,0.00016175321,0.0005573804,0.000889647,0.00019638424],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022628498,0.0022570158,0.0034914333,0.002657655,0.001331247,0.0017601246,0.004803432,0.0027704807,0.0022718648],"category_scores_gemma":[0.004334304,0.0014178765,0.0024388395,0.0022594184,0.00085592637,0.003219358,0.003744887,0.0021749248,0.0015349706],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050190283,0.0005942218,0.00496033,0.00014356228,0.0005434823,0.00019524852,0.00019762476,0.5014926,0.010534991,0.0057146135,0.0093333535,0.4657881],"study_design_scores_gemma":[0.000004558497,0.000017595645,0.00018189917,0.000005420344,0.000026195266,0.000020061387,0.0000108834765,0.996447,0.0009996372,0.0019821927,0.00029721865,0.000007389708],"about_ca_topic_score_codex":0.009384554,"about_ca_topic_score_gemma":0.01596835,"teacher_disagreement_score":0.009384554,"about_ca_system_score_codex":0.0007942319,"about_ca_system_score_gemma":0.0015730404,"threshold_uncertainty_score":0.01865983},"labels":[],"label_agreement":null},{"id":"W4391094031","doi":"10.1109/bigdata59044.2023.10386779","title":"BigData Fusion for Trajectory Prediction of Multi-Sensor Surveillance Information Systems","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Sensor fusion; Trajectory; Kalman filter; Big data; Artificial intelligence; Wireless sensor network; Computer vision; Real-time computing; Data mining","score_opus":0.0660000993394407,"score_gpt":0.30275746362834927,"score_spread":0.23675736428890856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391094031","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028896695,0.00027851536,0.9691148,0.00024563944,0.000070464186,0.000041397616,0.0001819948,0.0007468197,0.000423703],"genre_scores_gemma":[0.806314,0.00028430062,0.19192806,0.00008425721,0.000056691297,0.00009168767,0.0005393786,0.000048016143,0.0006535379],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99925035,0.00014850155,0.00006589629,0.0002186442,0.00022343316,0.00009315172],"domain_scores_gemma":[0.99859136,0.00047703518,0.00017088534,0.0002231021,0.00045831135,0.00007928246],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015521736,0.0010222187,0.0008178646,0.0011286363,0.0008024494,0.0009864898,0.0010658741,0.00080202776,0.00076320645],"category_scores_gemma":[0.0042161485,0.0004499367,0.00068605243,0.001223249,0.00049245806,0.0024074432,0.0012095862,0.0012052481,0.00021714364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026385504,0.00011235452,0.007328934,0.0001192709,0.00011562855,0.00016539777,0.00023926895,0.7950015,0.008390572,0.006341931,0.0016495378,0.18027164],"study_design_scores_gemma":[0.000002405772,0.000016672284,0.0006532396,0.000004024805,0.000005015078,0.000008035708,0.00002080721,0.99556065,0.0013799025,0.0020586876,0.00028522266,0.000005382231],"about_ca_topic_score_codex":0.014397079,"about_ca_topic_score_gemma":0.014440874,"teacher_disagreement_score":0.014397079,"about_ca_system_score_codex":0.001408572,"about_ca_system_score_gemma":0.0011613198,"threshold_uncertainty_score":0.028626561},"labels":[],"label_agreement":null},{"id":"W4391188490","doi":"10.1016/j.eswa.2024.123228","title":"MetaUSACC: Unlabeled scene adaptation for crowd counting via meta-auxiliary learning","year":2024,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"China Scholarship Council","keywords":"Computer science; Task (project management); Artificial intelligence; Adaptation (eye); Machine learning; Supervised learning; Computer vision; Pattern recognition (psychology); Artificial neural network","score_opus":0.0623486884662975,"score_gpt":0.3248349720059056,"score_spread":0.2624862835396081,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4391188490","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0076869046,0.00036193302,0.9772329,0.00011093062,0.00020918708,0.0001536245,0.0005034274,0.012211595,0.0015294934],"genre_scores_gemma":[0.16742176,0.0002418343,0.8179493,0.00055587076,0.0002639982,0.00047892076,0.0051979143,0.0021728193,0.0057176384],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9978961,0.00050338235,0.00007090868,0.00082497596,0.00043319096,0.00027133693],"domain_scores_gemma":[0.9974342,0.000752228,0.000099189434,0.0008762561,0.0006371842,0.00020091157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025388882,0.003594511,0.0035661554,0.0028122747,0.0014228238,0.0022988205,0.00703197,0.004150737,0.006466801],"category_scores_gemma":[0.006594012,0.0015875855,0.0026674124,0.0022656992,0.0013853684,0.0035460938,0.0062124864,0.003775141,0.005221971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009998328,0.00085640536,0.0016939952,0.00035869982,0.00044036887,0.0002906566,0.00025645195,0.17876546,0.017605191,0.006152048,0.033699296,0.75888157],"study_design_scores_gemma":[0.000026076597,0.00005745516,0.00021617877,0.000021578073,0.00003257576,0.000055783712,0.000023855337,0.988751,0.0040251357,0.004522721,0.0022454557,0.000022098737],"about_ca_topic_score_codex":0.009926952,"about_ca_topic_score_gemma":0.01723789,"teacher_disagreement_score":0.009926952,"about_ca_system_score_codex":0.0010107682,"about_ca_system_score_gemma":0.0020922737,"threshold_uncertainty_score":0.021633625},"labels":[],"label_agreement":null},{"id":"W4392359807","doi":"10.1016/j.buildenv.2024.111319","title":"Exploring occupant detection model generalizability for residential buildings using supervised learning with IEQ sensors","year":2024,"lang":"en","type":"article","venue":"Building and Environment","topic":"Video Surveillance and Tracking Methods","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":"European Commission","keywords":"Generalizability theory; Computer science; Architectural engineering; Environmental science; Artificial intelligence; Engineering; Machine learning; Statistics; Mathematics","score_opus":0.11034846957717165,"score_gpt":0.2871337614507357,"score_spread":0.17678529187356407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392359807","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.49500236,0.00080834475,0.49957925,0.0002926944,0.000080493955,0.00010509129,0.0007518301,0.0017090576,0.0016709439],"genre_scores_gemma":[0.9654698,0.00011277863,0.03159857,0.00008523636,0.000026691518,0.000057109406,0.0016106698,0.000064096275,0.00097498746],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99878854,0.00035578082,0.00006844001,0.00052431703,0.0001260741,0.00013689391],"domain_scores_gemma":[0.99809045,0.0010808897,0.00017814989,0.00029651792,0.00028207208,0.00007190764],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003050531,0.001145736,0.000825361,0.00073851825,0.00030777446,0.001009777,0.0015741212,0.0008158618,0.00068041263],"category_scores_gemma":[0.004733277,0.00043001754,0.0009985399,0.00052153115,0.0005420232,0.0010604176,0.0011467077,0.0011783572,0.00037175117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00034808807,0.00031861855,0.03244535,0.00015693644,0.00026260086,0.0000845059,0.00020212686,0.8645211,0.0032322663,0.0010229229,0.0014423386,0.09596319],"study_design_scores_gemma":[0.000005763475,0.000036768848,0.0035671527,0.000008480595,0.000012379009,0.000014762927,0.000037940827,0.9947465,0.0007471638,0.00059305533,0.00022350438,0.0000064839437],"about_ca_topic_score_codex":0.015481304,"about_ca_topic_score_gemma":0.017069973,"teacher_disagreement_score":0.015481304,"about_ca_system_score_codex":0.0008645238,"about_ca_system_score_gemma":0.0007110057,"threshold_uncertainty_score":0.030782342},"labels":[],"label_agreement":null},{"id":"W4392719396","doi":"10.1109/tvt.2024.3376544","title":"STC: Spatial and Temporal Clustering for Cooperative Perception System","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Video Surveillance and Tracking Methods","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":"Royal Military College of Canada; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Perception; Artificial intelligence; Psychology","score_opus":0.017572166993444417,"score_gpt":0.27997256076428345,"score_spread":0.262400393770839,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392719396","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023690663,0.00031584303,0.96018255,0.00028615544,0.00015955821,0.00023022047,0.00035401425,0.010507663,0.0042732377],"genre_scores_gemma":[0.56176925,0.0002965004,0.42582622,0.0003884952,0.00010720054,0.00039195333,0.0016675454,0.0003321436,0.009220713],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992217,0.000085062835,0.000036933874,0.00021709583,0.00031497798,0.0001242489],"domain_scores_gemma":[0.9992112,0.00008305393,0.00006300887,0.00015131195,0.00039722846,0.00009419755],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00061918003,0.0008758603,0.00075908436,0.0010390996,0.0010777063,0.00088558876,0.0026135636,0.0010484048,0.0029684657],"category_scores_gemma":[0.0013951237,0.00031144195,0.000567989,0.0010208198,0.00046565625,0.0010904196,0.0016583019,0.0008410669,0.001938455],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.001009477,0.0004578737,0.003279976,0.00024845777,0.0001874712,0.0004268186,0.00054999476,0.19489485,0.099003665,0.009428412,0.044549037,0.64596397],"study_design_scores_gemma":[0.000041100542,0.00014959027,0.0011228074,0.00000861,0.00002839308,0.00016993254,0.00011618093,0.9686835,0.019035844,0.0025262784,0.008069306,0.000048607755],"about_ca_topic_score_codex":0.016386386,"about_ca_topic_score_gemma":0.014536562,"teacher_disagreement_score":0.016386386,"about_ca_system_score_codex":0.0014506018,"about_ca_system_score_gemma":0.001978594,"threshold_uncertainty_score":0.032582045},"labels":[],"label_agreement":null},{"id":"W4392903121","doi":"10.1109/icassp48485.2024.10446305","title":"Ranking of Visual Trackers Using Robust Error Norms","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"BitTorrent tracker; Outlier; Computer science; Enhanced Data Rates for GSM Evolution; Artificial intelligence; Estimator; Ranking (information retrieval); Computer vision; Video tracking; Tracking (education); Measure (data warehouse); Mathematics; Statistics; Eye tracking; Object (grammar); Data mining","score_opus":0.07036390172944297,"score_gpt":0.3578511518061807,"score_spread":0.28748725007673775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4392903121","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052231707,0.0014088929,0.9397948,0.00025271383,0.00026617522,0.00019470006,0.0006909688,0.0031185984,0.0020414612],"genre_scores_gemma":[0.60353184,0.000892636,0.38482535,0.00025065866,0.00047765626,0.00038364914,0.005493346,0.0011090637,0.0030358045],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9828918,0.003977512,0.0018670105,0.0035571097,0.007009066,0.00069746625],"domain_scores_gemma":[0.95817196,0.014956001,0.0058313455,0.004699762,0.014550806,0.0017902304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.016454171,0.002897632,0.0031485972,0.0076972824,0.0011917419,0.005990069,0.0022175827,0.0023851104,0.0016488087],"category_scores_gemma":[0.06278903,0.0006204566,0.0016966292,0.0032639804,0.0017444554,0.0040393127,0.0027063552,0.0018633202,0.0018862097],"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":[0.0011808388,0.00036018374,0.049327884,0.00074814196,0.0012366012,0.00024130703,0.00034206227,0.29126424,0.018678395,0.009906678,0.013531135,0.61318254],"study_design_scores_gemma":[0.000078006306,0.00085363135,0.011408485,0.00009982755,0.00014587346,0.0003418898,0.00016271218,0.9567858,0.01464583,0.011670361,0.0036540902,0.00015351457],"about_ca_topic_score_codex":0.003444084,"about_ca_topic_score_gemma":0.0035246962,"teacher_disagreement_score":0.016454171,"about_ca_system_score_codex":0.0015506055,"about_ca_system_score_gemma":0.0022238407,"threshold_uncertainty_score":0.08701903},"labels":[],"label_agreement":null},{"id":"W4393033666","doi":"10.1109/access.2024.3380192","title":"VD-Net: An Edge Vision-Based Surveillance System for Violence Detection","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":47,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo; University of Ottawa","funders":"Zayed University","keywords":"Computer science; Computer vision; Enhanced Data Rates for GSM Evolution; Edge detection; Artificial intelligence; Computer security; Image processing; Image (mathematics)","score_opus":0.034708152886569595,"score_gpt":0.3602657280698424,"score_spread":0.32555757518327283,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393033666","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17558135,0.0017573902,0.72997725,0.0005060724,0.00074554153,0.00089658785,0.012400224,0.05575652,0.022379113],"genre_scores_gemma":[0.5550321,0.0007200132,0.40786532,0.0007394264,0.00012230837,0.000458445,0.018779676,0.00037124273,0.01591139],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976534,0.000028869066,0.0000139024005,0.00008556934,0.00007512178,0.000031218984],"domain_scores_gemma":[0.99982685,0.000027017353,0.000025373725,0.000024444345,0.00006940791,0.000027020134],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000336983,0.0006077147,0.00059403223,0.0012995141,0.00024408406,0.00045096787,0.0010307,0.0006036855,0.0025067995],"category_scores_gemma":[0.0005330543,0.00024740995,0.00035008663,0.00042528415,0.0001708912,0.0006328042,0.0008422775,0.0004993258,0.0011883362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0016940783,0.00090493035,0.01660955,0.0006018577,0.00031852804,0.00066939055,0.00013926372,0.02000008,0.10401569,0.0033017981,0.074210234,0.7775346],"study_design_scores_gemma":[0.0001222963,0.00062278187,0.022312345,0.000091584516,0.00014711065,0.0011877032,0.00007814225,0.8486065,0.088248275,0.0035014164,0.03498327,0.000098514625],"about_ca_topic_score_codex":0.0031725832,"about_ca_topic_score_gemma":0.00745404,"teacher_disagreement_score":0.0031725832,"about_ca_system_score_codex":0.0005744406,"about_ca_system_score_gemma":0.00043147104,"threshold_uncertainty_score":0.0083860755},"labels":[],"label_agreement":null},{"id":"W4393143200","doi":"10.3233/atde240128","title":"E2 Net: Efficient and Effective Dense Pedestrian Detection Network Based on YOLOv8","year":2024,"lang":"en","type":"book-chapter","venue":"Advances in transdisciplinary engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Pedestrian; Net (polyhedron); Computer science; Geography; Mathematics; Archaeology; Geometry","score_opus":0.006083498907762914,"score_gpt":0.24370238094390476,"score_spread":0.23761888203614184,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393143200","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.08449572,0.0005027202,0.8994678,0.00015294361,0.00017612435,0.0001424674,0.00036781054,0.009305926,0.005388457],"genre_scores_gemma":[0.5752694,0.00043747635,0.4086561,0.00029155632,0.000066702494,0.0001900558,0.0020276003,0.00024685235,0.012814274],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996792,0.000035554076,0.000013192422,0.00009749544,0.000109126835,0.000065313485],"domain_scores_gemma":[0.9997451,0.000043286705,0.000028164268,0.000036511665,0.0001125324,0.000034519726],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003765539,0.0008123051,0.0006925981,0.0011224893,0.0004060928,0.0005280053,0.0012586946,0.00042204274,0.0028394705],"category_scores_gemma":[0.00078284775,0.00046698158,0.00041978955,0.000595522,0.0003297699,0.0011730962,0.0012765932,0.0003695042,0.0011696278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0014923413,0.00029257295,0.006666873,0.00025356543,0.00013836852,0.0005620329,0.00022284348,0.08597663,0.078367785,0.007562717,0.024163177,0.7943011],"study_design_scores_gemma":[0.00004114328,0.00018404846,0.0020752742,0.000016829033,0.000046651377,0.0003172088,0.00004990221,0.9548766,0.03263096,0.0021273454,0.007597693,0.000036409514],"about_ca_topic_score_codex":0.0062808003,"about_ca_topic_score_gemma":0.008930866,"teacher_disagreement_score":0.0062808003,"about_ca_system_score_codex":0.00078106864,"about_ca_system_score_gemma":0.00085573964,"threshold_uncertainty_score":0.012488484},"labels":[],"label_agreement":null},{"id":"W4393181685","doi":"10.3390/s24072107","title":"Object Detection and Tracking with YOLO and the Sliding Innovation Filter","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Alberta Oil Sands Technology and Research Authority; McMaster University; University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Kalman filter; Computer vision; Artificial intelligence; Video tracking; Object detection; Filter (signal processing); Object (grammar); Tracking system; Trajectory; Tracking (education); Extended Kalman filter; Pattern recognition (psychology)","score_opus":0.021967897245596498,"score_gpt":0.26690382086501724,"score_spread":0.24493592361942074,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393181685","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":[0.0037597893,0.00029942265,0.99461657,0.00005360254,0.000050701005,0.000016494338,0.000009706458,0.00027710665,0.0009165055],"genre_scores_gemma":[0.45564112,0.0013057857,0.5352629,0.00030799498,0.00018842904,0.00021036851,0.00013615542,0.00010362465,0.0068435892],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99877566,0.00017523172,0.00007097416,0.00037270313,0.0004934149,0.00011200475],"domain_scores_gemma":[0.9989672,0.00040085224,0.00017778882,0.00015699059,0.0002510538,0.00004610504],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013375431,0.00082083856,0.000969442,0.00097119063,0.00048016995,0.0012078448,0.001171077,0.0015213426,0.0012101497],"category_scores_gemma":[0.003612445,0.0005267524,0.0010931559,0.0007925795,0.0009866313,0.0017058711,0.0012533803,0.0013890015,0.0006778005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048242632,0.00014292804,0.0029714352,0.00032620286,0.00023138912,0.00028569272,0.0004760355,0.31462538,0.04330015,0.072939724,0.0025632773,0.56165534],"study_design_scores_gemma":[0.00001300562,0.00013157615,0.0005360906,0.0000135808305,0.000032209762,0.00008270333,0.000013607602,0.98871905,0.004630648,0.0032043795,0.0025947285,0.000028393939],"about_ca_topic_score_codex":0.006050148,"about_ca_topic_score_gemma":0.0038503618,"teacher_disagreement_score":0.006050148,"about_ca_system_score_codex":0.00086232997,"about_ca_system_score_gemma":0.0012247995,"threshold_uncertainty_score":0.012029827},"labels":[],"label_agreement":null},{"id":"W4393225711","doi":"10.18280/mmep.110318","title":"A Visual Landforms Classification Methodology for Mobile Robot Navigation by Intelligent Double Spike Neural Network Acceleration","year":2024,"lang":"en","type":"article","venue":"Mathematical Modelling and Engineering Problems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Landform; Spike (software development); Mobile robot; Acceleration; Computer science; Artificial neural network; Artificial intelligence; Computer vision; Robot; Geography; Cartography; Physics","score_opus":0.11898625312753887,"score_gpt":0.3399184344884691,"score_spread":0.22093218136093024,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393225711","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026738001,0.00010073301,0.97148234,0.000055072625,0.000036709163,0.000028290999,0.00004107242,0.0004865009,0.0010312296],"genre_scores_gemma":[0.76722234,0.00015973426,0.22948076,0.00007552699,0.0000291308,0.00009284124,0.00022461443,0.00003735778,0.002677695],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999871,0.000015366231,0.000009900921,0.000033389075,0.00005034854,0.000019982775],"domain_scores_gemma":[0.99983835,0.000028158569,0.000022190028,0.000017124408,0.00008330531,0.0000108254935],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00025095674,0.00034714965,0.00030184523,0.00044656792,0.0001816277,0.0003631555,0.0006783385,0.00032999256,0.0008556972],"category_scores_gemma":[0.0006017823,0.00016035367,0.0003992405,0.0004527712,0.00024643296,0.00048497203,0.00046203818,0.0004251356,0.0002355087],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00012540272,0.00009899852,0.003690237,0.00008638421,0.00006158512,0.00011300284,0.00009566475,0.3681826,0.034933537,0.008174303,0.0018283343,0.58260995],"study_design_scores_gemma":[0.0000013782546,0.000018396822,0.000258404,0.0000021571343,0.0000034139825,0.000015855938,0.0000050363137,0.9969189,0.001791435,0.00072288595,0.00025942578,0.000002689656],"about_ca_topic_score_codex":0.0042296248,"about_ca_topic_score_gemma":0.004381725,"teacher_disagreement_score":0.0042296248,"about_ca_system_score_codex":0.00039431918,"about_ca_system_score_gemma":0.0004969091,"threshold_uncertainty_score":0.008410037},"labels":[],"label_agreement":null},{"id":"W4393272689","doi":"10.1007/s11042-024-19002-4","title":"TGLC: Visual object tracking by fusion of global-local information and channel information","year":2024,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Computer science; Object (grammar); Computer vision; Information fusion; Channel (broadcasting); Artificial intelligence; Tracking (education); Fusion; Information retrieval; Telecommunications","score_opus":0.013924160869487959,"score_gpt":0.2907902756813186,"score_spread":0.27686611481183065,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4393272689","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":[0.01562181,0.00025028744,0.9707444,0.00006869671,0.00012414111,0.00010227613,0.00043514863,0.011260985,0.0013923056],"genre_scores_gemma":[0.26015887,0.00038328493,0.7290016,0.000192965,0.00014074349,0.00017278838,0.0026786912,0.0008867075,0.006384382],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9995541,0.000047459725,0.000014962134,0.00012131916,0.00018739938,0.00007466249],"domain_scores_gemma":[0.9995962,0.000058703954,0.000037039696,0.00013021992,0.00012835153,0.000049495768],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087111583,0.0009834372,0.0010164876,0.0021113257,0.0004710008,0.0011891513,0.0012846948,0.00091859966,0.002756259],"category_scores_gemma":[0.0010679842,0.0003218134,0.000591203,0.00223498,0.0005809374,0.0014430151,0.0017504118,0.00064488885,0.0019488148],"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":[0.0007330514,0.00019062556,0.0025787603,0.0001834265,0.00017203904,0.00020410064,0.00014858469,0.042940766,0.0835132,0.003990759,0.017249323,0.8480955],"study_design_scores_gemma":[0.0000762936,0.00017053573,0.0026242717,0.00002110124,0.00009740241,0.00024055055,0.00004765107,0.9441116,0.04250992,0.0026294452,0.007421806,0.00004939226],"about_ca_topic_score_codex":0.0109398095,"about_ca_topic_score_gemma":0.00997898,"teacher_disagreement_score":0.0109398095,"about_ca_system_score_codex":0.0005343617,"about_ca_system_score_gemma":0.001105585,"threshold_uncertainty_score":0.021752238},"labels":[],"label_agreement":null},{"id":"W4394007169","doi":"10.48550/arxiv.2404.03110","title":"Ego-Motion Aware Target Prediction Module for Robust Multi-Object Tracking","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Mitacs","keywords":"Id, ego and super-ego; Computer science; Object (grammar); Artificial intelligence; Motion (physics); Tracking (education); Computer vision; Video tracking; Match moving; Psychology; Social psychology","score_opus":0.14018784135032925,"score_gpt":0.23885486610949935,"score_spread":0.09866702475917011,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394007169","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.095731005,0.00112893,0.84719485,0.00018700787,0.00024448853,0.00021438758,0.002414956,0.049297955,0.0035864478],"genre_scores_gemma":[0.52355695,0.00046350315,0.456859,0.00027461603,0.00010059613,0.00018882852,0.011349201,0.00069962005,0.0065078],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999721,0.0000113968645,0.000012668625,0.00010987536,0.00009826393,0.000046820278],"domain_scores_gemma":[0.9996574,0.000040991352,0.00003516953,0.0001308923,0.00010549184,0.000030106317],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037207772,0.0009347109,0.0008004736,0.0008777497,0.00040681212,0.0006447364,0.0018003021,0.00066213304,0.0025217661],"category_scores_gemma":[0.0010169627,0.00026005475,0.00051395333,0.0011435366,0.00021135448,0.0011031915,0.0010440741,0.0008317885,0.002643592],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004217787,0.00020464526,0.0067502223,0.00014382589,0.00011641712,0.00014838576,0.00009602403,0.050419796,0.03463209,0.0016970129,0.02568133,0.87968844],"study_design_scores_gemma":[0.000038013146,0.00013152693,0.005006866,0.000016254167,0.00004209,0.00021162294,0.00005370256,0.94769925,0.034131374,0.0018885292,0.010752991,0.000027843655],"about_ca_topic_score_codex":0.012432563,"about_ca_topic_score_gemma":0.016186135,"teacher_disagreement_score":0.012432563,"about_ca_system_score_codex":0.000487409,"about_ca_system_score_gemma":0.0013630755,"threshold_uncertainty_score":0.02472043},"labels":[],"label_agreement":null},{"id":"W4394862747","doi":"10.1109/wacvw60836.2024.00036","title":"Evaluating Supervision Levels Trade-Offs for Infrared-Based People Counting","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Infrared; Optics","score_opus":0.11318820545792943,"score_gpt":0.3956472231831581,"score_spread":0.28245901772522863,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394862747","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":[0.8539433,0.0029355502,0.12549129,0.0015522359,0.00025190675,0.00019028725,0.0011278968,0.0065257936,0.007981711],"genre_scores_gemma":[0.96039903,0.00030894476,0.035548322,0.00019199964,0.00004858448,0.000065943124,0.0012789392,0.00015702253,0.0020012823],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9986632,0.0003778097,0.00007680052,0.00043578766,0.00024743794,0.0001990191],"domain_scores_gemma":[0.99508166,0.0028543554,0.00046071023,0.0005887651,0.0006807548,0.00033367422],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0035553866,0.0016236029,0.0007842593,0.00072364387,0.00042823108,0.0009157643,0.0018168127,0.0017684823,0.002762854],"category_scores_gemma":[0.017244587,0.0004837089,0.0005251379,0.0005003914,0.0007763274,0.0024502538,0.0013016138,0.001932831,0.00087419647],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.005642176,0.0017021063,0.040616322,0.00071459106,0.00041369212,0.0002123448,0.00031647674,0.43555793,0.018803788,0.0038129224,0.013113212,0.47909454],"study_design_scores_gemma":[0.000094984396,0.00045863172,0.0043388642,0.00007146186,0.00006796417,0.000069408285,0.00006794252,0.9834389,0.008316942,0.0024438216,0.00061227684,0.00001870475],"about_ca_topic_score_codex":0.00810811,"about_ca_topic_score_gemma":0.012248386,"teacher_disagreement_score":0.00810811,"about_ca_system_score_codex":0.001218669,"about_ca_system_score_gemma":0.0010313272,"threshold_uncertainty_score":0.018802881},"labels":[],"label_agreement":null},{"id":"W4394984748","doi":"10.1016/j.patcog.2024.110513","title":"GCNet: Probing self-similarity learning for Generalized Counting Network","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Zhejiang Sci-Tech University; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Generality; Computer science; Benchmark (surveying); Similarity (geometry); Artificial intelligence; Limit (mathematics); Machine learning; Class (philosophy); Pattern recognition (psychology); Image (mathematics); Mathematics","score_opus":0.04770018134941861,"score_gpt":0.3003559598010574,"score_spread":0.2526557784516388,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4394984748","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":[0.027812514,0.0002444851,0.961549,0.00024818198,0.00012007581,0.00015830144,0.0004608946,0.00785783,0.0015487585],"genre_scores_gemma":[0.42683348,0.00022773306,0.56195927,0.00044493456,0.0001605271,0.00049051683,0.0032836546,0.0010443601,0.0055555147],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991586,0.0002213711,0.000039394115,0.0002801191,0.00021079126,0.00008969938],"domain_scores_gemma":[0.99753416,0.00092597003,0.00017846728,0.0006484312,0.0005346923,0.00017834443],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0016086955,0.0012206609,0.0015655794,0.0016434196,0.00078727613,0.0012454339,0.0036881543,0.0023882657,0.005174683],"category_scores_gemma":[0.008932871,0.0006183093,0.00074048486,0.0015582229,0.0008887889,0.0030071465,0.0024198534,0.0020101767,0.0013638638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060453446,0.00040104936,0.0045921416,0.0002731864,0.00020622418,0.00018842923,0.0001694223,0.41876143,0.007452876,0.037807424,0.030972967,0.49857032],"study_design_scores_gemma":[0.0000095549185,0.000021007263,0.000096258875,0.0000033257625,0.000005533571,0.000017822258,0.000005526409,0.99123436,0.00068207853,0.007401189,0.0005199072,0.000003490645],"about_ca_topic_score_codex":0.007734466,"about_ca_topic_score_gemma":0.009815386,"teacher_disagreement_score":0.007734466,"about_ca_system_score_codex":0.0013375151,"about_ca_system_score_gemma":0.0015028056,"threshold_uncertainty_score":0.017311096},"labels":[],"label_agreement":null},{"id":"W4395078898","doi":"10.1007/s11042-024-19007-z","title":"Multi-level self attention for unsupervised learning person re-identification","year":2024,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Communications Research Centre Canada","funders":"China Scholarship Council; National Natural Science Foundation of China","keywords":"Computer science; Identification (biology); Unsupervised learning; Artificial intelligence; Machine learning","score_opus":0.11165311761212961,"score_gpt":0.3388599380649256,"score_spread":0.227206820452796,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4395078898","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.061210237,0.0013604792,0.9303726,0.00018298773,0.00023340005,0.000093194685,0.00034833295,0.0031787343,0.0030199732],"genre_scores_gemma":[0.71350837,0.0010024619,0.26425812,0.00061898364,0.00039945857,0.00015306038,0.0019808016,0.0005093197,0.01756939],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991041,0.00013535366,0.0000367163,0.0003808144,0.00017755479,0.00016546327],"domain_scores_gemma":[0.99897575,0.00038727652,0.00006743876,0.00022534085,0.00027853335,0.00006564976],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0012363042,0.0010546589,0.001685489,0.0016217105,0.0005784519,0.00075210904,0.0015410542,0.0013881457,0.0027351698],"category_scores_gemma":[0.0018119031,0.00043481967,0.0010953065,0.0013553468,0.00039614618,0.0012212185,0.0019044717,0.0011724371,0.0023119212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045521918,0.0004762885,0.003388003,0.00013386547,0.00021007338,0.00014660577,0.00012148043,0.021991046,0.0552549,0.001561074,0.006020891,0.9102406],"study_design_scores_gemma":[0.0000112347425,0.00015575204,0.006068529,0.000017313245,0.00008768441,0.00021427636,0.000053797678,0.9650607,0.02356707,0.002516091,0.0022281238,0.000019363046],"about_ca_topic_score_codex":0.005043827,"about_ca_topic_score_gemma":0.009261089,"teacher_disagreement_score":0.005043827,"about_ca_system_score_codex":0.0005464754,"about_ca_system_score_gemma":0.00064802106,"threshold_uncertainty_score":0.010028958},"labels":[],"label_agreement":null},{"id":"W4396219790","doi":"10.3390/s24092828","title":"Dynamic Occupancy Grid Map with Semantic Information Using Deep Learning-Based BEVFusion Method with Camera and LiDAR Fusion","year":2024,"lang":"en","type":"article","venue":"Sensors","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"National Research Foundation of Korea; Korea Institute for Advancement of Technology; Ministry of Education, Science and Technology; Ministry of Trade, Industry and Energy; National Research Foundation","keywords":"Occupancy grid mapping; Occupancy; Lidar; Computer science; Grid; Artificial intelligence; Fusion; Computer vision; Deep learning; Sensor fusion; Information fusion; Remote sensing; Geography; Engineering; Robot; Mobile robot; Civil engineering","score_opus":0.008680686673007,"score_gpt":0.2754471658296445,"score_spread":0.26676647915663754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396219790","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.023759983,0.0003126544,0.9734152,0.00018007372,0.00006552711,0.000048915394,0.00014871043,0.0011483702,0.0009204628],"genre_scores_gemma":[0.7135231,0.0003567496,0.28161007,0.00031624956,0.000077307464,0.0001240199,0.0010398919,0.0001639272,0.0027887558],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953794,0.000050792456,0.00002609863,0.00013624692,0.00015982334,0.00008914116],"domain_scores_gemma":[0.99955994,0.000115857,0.00005646441,0.000071435854,0.00015668936,0.00003962037],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006366968,0.0008355173,0.001184944,0.0010310939,0.00036658713,0.00096775324,0.0019058099,0.0008559549,0.0013766933],"category_scores_gemma":[0.0020004257,0.00054066104,0.00084377686,0.0011659879,0.0004749301,0.0016621083,0.0017375603,0.0013727777,0.00035620865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025573737,0.00016386792,0.003264002,0.00009184848,0.00009542291,0.00014354884,0.00015881698,0.42704436,0.0054634153,0.0065168478,0.0046229637,0.5521792],"study_design_scores_gemma":[0.000005661787,0.000013669243,0.0001617424,0.0000041103463,0.0000056714766,0.000018555684,0.000009592204,0.9968046,0.00089795206,0.0017015218,0.0003723028,0.0000046853825],"about_ca_topic_score_codex":0.013783666,"about_ca_topic_score_gemma":0.010018426,"teacher_disagreement_score":0.013783666,"about_ca_system_score_codex":0.0009559828,"about_ca_system_score_gemma":0.0011847279,"threshold_uncertainty_score":0.027406871},"labels":[],"label_agreement":null},{"id":"W4396568196","doi":"10.1109/ithings-greencom-cpscom-smartdata-cybermatics60724.2023.00064","title":"Edge Computing Enabled Real-Time Video Analysis via Adaptive Spatial-Temporal Semantic Filtering","year":2023,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Enhanced Data Rates for GSM Evolution; Real-time computing; Computer vision","score_opus":0.028261657804625268,"score_gpt":0.28675121213643884,"score_spread":0.2584895543318136,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396568196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.039391164,0.00020681441,0.95727986,0.00009239139,0.000035229383,0.0000368877,0.00003857894,0.0012623926,0.0016567392],"genre_scores_gemma":[0.80693924,0.0001915721,0.18931441,0.0002460224,0.00004509731,0.00006761971,0.00014171112,0.00007582573,0.0029784697],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997532,0.000028880886,0.000010915922,0.00008575555,0.00007586407,0.000045250068],"domain_scores_gemma":[0.9997584,0.00006750717,0.00003857147,0.00003690399,0.00007148085,0.000027005717],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028734212,0.0005514491,0.0004876886,0.0004326981,0.00028965698,0.00059700327,0.0010651605,0.0005412465,0.0008746564],"category_scores_gemma":[0.0007113188,0.00020172377,0.00030066335,0.0003786729,0.00029611404,0.0010279547,0.00067171396,0.0006236563,0.00032066248],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00084759237,0.00040694347,0.003628082,0.00010114112,0.000069375325,0.0003701953,0.00017449936,0.20172386,0.14027756,0.006474526,0.0038402972,0.64208585],"study_design_scores_gemma":[0.000008536471,0.000045307876,0.00045217684,0.0000030969281,0.000009257296,0.000040156407,0.000010856866,0.9870129,0.010505393,0.0012403424,0.00066443736,0.0000074630034],"about_ca_topic_score_codex":0.0029043423,"about_ca_topic_score_gemma":0.003513426,"teacher_disagreement_score":0.0029043423,"about_ca_system_score_codex":0.00044217292,"about_ca_system_score_gemma":0.0004175022,"threshold_uncertainty_score":0.005774915},"labels":[],"label_agreement":null},{"id":"W4396812923","doi":"10.3390/robotics13050073","title":"An Aerial Robotic Missing-Person Search in Urban Settings—A Probabilistic Approach","year":2024,"lang":"en","type":"article","venue":"Robotics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Probabilistic logic; Artificial intelligence; Urban search and rescue; Computer science; Search and rescue; Robotics; Geography; Computer vision; Cartography; Engineering; Robot; Mobile robot","score_opus":0.04907279961292342,"score_gpt":0.3141441091934372,"score_spread":0.2650713095805138,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396812923","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03121193,0.0001806595,0.9673615,0.00008145898,0.000009513285,0.000022618508,0.000030618896,0.00014512522,0.00095645833],"genre_scores_gemma":[0.7866375,0.00034212894,0.21092264,0.000050752034,0.00003682,0.00006246656,0.00010973569,0.000046382353,0.0017915185],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966407,0.00009941317,0.000011438287,0.00008719714,0.00009797809,0.00003996766],"domain_scores_gemma":[0.9992668,0.00039590907,0.00012830459,0.00008393366,0.00008194204,0.000043134612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00065150944,0.0003756482,0.0005253807,0.0007986367,0.00039938002,0.00046211612,0.0013433729,0.00058764993,0.00073204],"category_scores_gemma":[0.0018961383,0.0004580454,0.00050235225,0.00070049346,0.00055500946,0.00092067535,0.0009999955,0.00046883168,0.00016585618],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000056611756,0.000032397358,0.0027191492,0.00007536765,0.00004623779,0.00017080815,0.00011933705,0.92881507,0.0024843994,0.007967577,0.000502491,0.05701051],"study_design_scores_gemma":[0.0000025665693,0.000019919187,0.0003560082,0.0000030067567,0.00000448937,0.000062745166,0.00001860125,0.9976042,0.00036091526,0.0013212377,0.00024139589,0.000004938439],"about_ca_topic_score_codex":0.004176923,"about_ca_topic_score_gemma":0.0044724145,"teacher_disagreement_score":0.004176923,"about_ca_system_score_codex":0.0004021236,"about_ca_system_score_gemma":0.00067703583,"threshold_uncertainty_score":0.008305192},"labels":[],"label_agreement":null},{"id":"W4396829261","doi":"10.1101/2024.05.09.593241","title":"One size does not fit all: a novel approach for determining the Realised Viewshed Size for remote camera traps","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Video Surveillance and Tracking Methods","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":"Okanagan University College; University of British Columbia, Okanagan Campus; University of British Columbia; Memorial University of Newfoundland","funders":"","keywords":"Remote sensing; Computer science; Environmental science; Computer vision; Computer graphics (images); Geology","score_opus":0.05965932570305272,"score_gpt":0.2906566077522842,"score_spread":0.23099728204923148,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396829261","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":[0.1347214,0.00031581827,0.8622539,0.0001141325,0.000047269736,0.00012656277,0.0003034181,0.0010514664,0.001065904],"genre_scores_gemma":[0.5985836,0.00016390202,0.3998246,0.00006849337,0.00004896415,0.00013837546,0.00038112886,0.00020459852,0.00058639114],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9987997,0.00026164067,0.00007009387,0.00044524047,0.00034628037,0.00007699289],"domain_scores_gemma":[0.99436486,0.0029398778,0.0010046194,0.00069124094,0.00083417434,0.00016530117],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018885613,0.0007645812,0.00068301824,0.001961709,0.00035528617,0.0012274209,0.0015941921,0.0008845659,0.0014140677],"category_scores_gemma":[0.0118454,0.0004899835,0.00079740514,0.00092169666,0.0004999614,0.0013396619,0.001353459,0.00077327254,0.0005487054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006619259,0.00041911151,0.1010347,0.0006516347,0.00034453778,0.0010316595,0.0009299926,0.27085528,0.07425972,0.00875791,0.0023814687,0.53867215],"study_design_scores_gemma":[0.000014738765,0.0001489112,0.020143604,0.000040880583,0.0000592512,0.00057509704,0.00014350058,0.9635847,0.010915434,0.0029797398,0.0013199263,0.00007421907],"about_ca_topic_score_codex":0.0028002,"about_ca_topic_score_gemma":0.003735626,"teacher_disagreement_score":0.0028002,"about_ca_system_score_codex":0.00066198414,"about_ca_system_score_gemma":0.0007101179,"threshold_uncertainty_score":0.0099877715},"labels":[],"label_agreement":null},{"id":"W4396936162","doi":"10.1016/j.patcog.2024.110588","title":"Prototype learning based generic multiple object tracking via point-to-box supervision","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"National Natural Science Foundation of China","keywords":"Computer science; Minimum bounding box; Benchmark (surveying); Artificial intelligence; Object (grammar); Video tracking; Computer vision; Generalization; Task (project management); Tracking (education); Point (geometry); Pattern recognition (psychology); Machine learning; Image (mathematics); Mathematics","score_opus":0.049382749991367436,"score_gpt":0.29408552318957004,"score_spread":0.2447027731982026,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4396936162","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":[0.00939033,0.00008527911,0.98857903,0.000028752389,0.000021415022,0.0000410035,0.000032768,0.0013966407,0.0004248264],"genre_scores_gemma":[0.547436,0.00017705472,0.4478668,0.00015377603,0.000051074345,0.00019926437,0.0005040713,0.0003185356,0.0032934623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99857914,0.00020066397,0.00006603603,0.0006527432,0.00036678973,0.0001345964],"domain_scores_gemma":[0.99733895,0.00077241723,0.0002920827,0.0008713694,0.0005886612,0.00013653355],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018961789,0.0010140192,0.002542695,0.0006732572,0.00057555584,0.0011817225,0.003654601,0.0022267015,0.0030906731],"category_scores_gemma":[0.005444793,0.00090511836,0.0010433979,0.0010554639,0.0012390381,0.0024852138,0.0028745157,0.0019705521,0.0015060295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008453439,0.00026683568,0.002250512,0.00026663803,0.00017932363,0.00023830654,0.00016434895,0.26276276,0.035651635,0.008118061,0.004450149,0.6848061],"study_design_scores_gemma":[0.000012842251,0.00007106643,0.00026814363,0.0000049645996,0.000010853407,0.00006221771,0.000006249133,0.99350077,0.00391941,0.0018148732,0.00032126548,0.000007304974],"about_ca_topic_score_codex":0.0037035563,"about_ca_topic_score_gemma":0.004511859,"teacher_disagreement_score":0.0037035563,"about_ca_system_score_codex":0.00067595526,"about_ca_system_score_gemma":0.0012245924,"threshold_uncertainty_score":0.010339379},"labels":[],"label_agreement":null},{"id":"W4398169558","doi":"10.3390/rs16111827","title":"GeoSparseNet: A Multi-Source Geometry-Aware CNN for Urban Scene Analysis","year":2024,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"China Postdoctoral Science Foundation; National Natural Science Foundation of China","keywords":"Computer science; Geometry; Mathematics","score_opus":0.042026129360548586,"score_gpt":0.32045894003843195,"score_spread":0.2784328106778834,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398169558","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":[0.09048471,0.0011065353,0.8680875,0.00044927053,0.00036473465,0.00024427744,0.006459924,0.022342889,0.010460198],"genre_scores_gemma":[0.5410322,0.00089166366,0.4142451,0.0006202647,0.00014272034,0.0002598324,0.024929946,0.0009032444,0.016975056],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99982303,0.000011446797,0.0000052741425,0.00006946963,0.000052326675,0.000038457234],"domain_scores_gemma":[0.9998697,0.000019854868,0.000016577422,0.00003806701,0.000042692558,0.000013119174],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00022485136,0.0015524264,0.00058635167,0.001189276,0.00031444908,0.0006532939,0.0020648383,0.0006961245,0.0034458584],"category_scores_gemma":[0.000617066,0.00055542064,0.0008002322,0.0011060265,0.00029074127,0.0015958035,0.001360501,0.0008775653,0.0011487473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028392154,0.0003052099,0.005312815,0.00026957912,0.00038729017,0.00033973475,0.000082596336,0.26586697,0.039809022,0.006674823,0.048396748,0.63227135],"study_design_scores_gemma":[0.000011508676,0.0000444106,0.0013184273,0.000012780267,0.000027968685,0.00007341383,0.000020380687,0.9841562,0.0075415126,0.0022392632,0.004541004,0.0000131086335],"about_ca_topic_score_codex":0.015932508,"about_ca_topic_score_gemma":0.038653027,"teacher_disagreement_score":0.015932508,"about_ca_system_score_codex":0.00090340205,"about_ca_system_score_gemma":0.00072961353,"threshold_uncertainty_score":0.03167951},"labels":[],"label_agreement":null},{"id":"W4398248842","doi":"10.59254/sbpo-2023-175064","title":"PATH LOSS PREDICTION FOR MESH NETWORKS IN A REAL URBAN ENVIRONMENT USING MACHINE LEARNING","year":2023,"lang":"en","type":"article","venue":"Anais do Simpósio Brasileiro de Pesquisa Operacional","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Path (computing); Mesh networking; Distributed computing; Artificial intelligence; Machine learning; Computer network; Telecommunications","score_opus":0.03638834981237157,"score_gpt":0.30270725248649166,"score_spread":0.26631890267412006,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4398248842","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":[0.6404708,0.0005125454,0.35601565,0.00038544557,0.000080793856,0.000051412782,0.0005793108,0.0006752495,0.0012287741],"genre_scores_gemma":[0.97988737,0.00013411837,0.018616347,0.000014985211,0.000019040304,0.000025055044,0.00036839352,0.00002155277,0.00091320195],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980134,0.00004768186,0.000008728453,0.00006347425,0.000037354854,0.000041519583],"domain_scores_gemma":[0.99862623,0.00096909725,0.00010406314,0.00006229807,0.0001869321,0.00005148871],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00064762105,0.00062064285,0.00053208525,0.001029799,0.0003076841,0.00056578807,0.00082692713,0.0007120666,0.0007288932],"category_scores_gemma":[0.0022641702,0.00023440296,0.00036623413,0.0007769505,0.00027422162,0.0007539579,0.0003675594,0.0005734215,0.00018042065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00006333488,0.000046335597,0.0035550934,0.00001398114,0.000013826731,0.000030916803,0.000012716456,0.97606206,0.00033972843,0.00025115616,0.00037384746,0.01923701],"study_design_scores_gemma":[6.843813e-7,0.0000036956221,0.00029627324,6.042962e-7,9.4873053e-7,0.0000024302722,0.000003381118,0.9995264,0.000053359276,0.00009976206,0.000011788797,7.2691006e-7],"about_ca_topic_score_codex":0.018987596,"about_ca_topic_score_gemma":0.013556454,"teacher_disagreement_score":0.018987596,"about_ca_system_score_codex":0.00085785496,"about_ca_system_score_gemma":0.00046971874,"threshold_uncertainty_score":0.037754178},"labels":[],"label_agreement":null},{"id":"W4399245776","doi":"10.1016/j.comcom.2024.05.021","title":"IoT video analytics for surveillance-based systems in smart cities","year":2024,"lang":"en","type":"article","venue":"Computer Communications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa; Concordia University","funders":"","keywords":"Computer science; Internet of Things; Analytics; Data science; Smart city; Computer security; Telecommunications","score_opus":0.08279356799581435,"score_gpt":0.33828400735934133,"score_spread":0.255490439363527,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399245776","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12850514,0.0024831588,0.8502607,0.0009118081,0.00033400758,0.00014124103,0.0011110114,0.0026979856,0.013555022],"genre_scores_gemma":[0.89199007,0.0015265475,0.10057354,0.00013138012,0.00016686639,0.000070370275,0.0010845944,0.00011524386,0.0043414733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99966824,0.00007635407,0.000021708547,0.00006886236,0.00012249169,0.000042411684],"domain_scores_gemma":[0.9994692,0.0001520846,0.00008151331,0.000061845865,0.0001977266,0.000037530805],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003929516,0.0005248532,0.00039535068,0.001094239,0.0003074633,0.001323913,0.00047003492,0.0005239644,0.0017987139],"category_scores_gemma":[0.0011790234,0.00017469068,0.0002312393,0.0012737614,0.0002510709,0.0014903998,0.00048410764,0.00043329885,0.0005698623],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005814981,0.00040249436,0.025520753,0.0005298182,0.0001482046,0.00041202825,0.00043277812,0.11659714,0.10327696,0.035154663,0.022962876,0.6939809],"study_design_scores_gemma":[0.0000138123205,0.00010625829,0.007478871,0.0000644408,0.000034120454,0.00014339542,0.00042861598,0.9394556,0.029962402,0.0119862445,0.01030426,0.000021860653],"about_ca_topic_score_codex":0.0030302885,"about_ca_topic_score_gemma":0.0032977925,"teacher_disagreement_score":0.0030302885,"about_ca_system_score_codex":0.0004967433,"about_ca_system_score_gemma":0.00038033622,"threshold_uncertainty_score":0.0060253143},"labels":[],"label_agreement":null},{"id":"W4399368852","doi":"10.21428/d82e957c.3da7f032","title":"POPCat: Propagation of Particles for Complex Annotation Tasks","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Artificial intelligence; Pipeline (software); Segmentation; Annotation; Margin (machine learning); Set (abstract data type); Video tracking; Computer vision; Precision and recall; Pattern recognition (psychology); Object (grammar); Generalization; Tracking (education); Object detection; Frame (networking); Machine learning; Mathematics","score_opus":0.10195501463276604,"score_gpt":0.3708137294391684,"score_spread":0.26885871480640233,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399368852","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.005303389,0.00031736548,0.96190864,0.00021999287,0.00024581235,0.00022920273,0.0011907023,0.028948596,0.001636228],"genre_scores_gemma":[0.09247668,0.00034070286,0.88185894,0.00056232675,0.00023142378,0.0007712599,0.011606334,0.004497928,0.007654451],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9983076,0.0003090921,0.00008464513,0.0006517074,0.0005160167,0.000130941],"domain_scores_gemma":[0.99606067,0.001779552,0.00021302469,0.0009050506,0.000861435,0.000180252],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0027550862,0.003729018,0.0019916252,0.0022812202,0.0016726061,0.0025591943,0.004830267,0.0030076338,0.006957471],"category_scores_gemma":[0.01083665,0.001817951,0.002038425,0.0021514057,0.0011312935,0.0027423801,0.0033139265,0.00426208,0.0053511816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007488887,0.00032961427,0.003740987,0.0004632426,0.0003633414,0.00034617877,0.0003198513,0.38893297,0.0107621625,0.012229582,0.10471818,0.47704503],"study_design_scores_gemma":[0.000043292686,0.000035065907,0.00021987413,0.00001538987,0.000013272619,0.000036147016,0.000022083565,0.98758984,0.0027541453,0.0043178042,0.0049371454,0.000015924727],"about_ca_topic_score_codex":0.03166607,"about_ca_topic_score_gemma":0.045261778,"teacher_disagreement_score":0.03166607,"about_ca_system_score_codex":0.0017958491,"about_ca_system_score_gemma":0.0031983654,"threshold_uncertainty_score":0.062963486},"labels":[],"label_agreement":null},{"id":"W4399369419","doi":"10.21428/d82e957c.276daa5a","title":"Change of Scenery: Unsupervised LiDAR Change Detection for Mobile Robots","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Change detection; Lidar; Mobile robot; Computer science; Remote sensing; Artificial intelligence; Robot; Computer vision; Environmental science; Geography","score_opus":0.1019181580807536,"score_gpt":0.3396843337038272,"score_spread":0.23776617562307362,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399369419","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18717916,0.00043324108,0.8018429,0.00021404956,0.000079176825,0.00021029497,0.00052910973,0.005415059,0.0040970864],"genre_scores_gemma":[0.73852265,0.0001494518,0.256691,0.00016972877,0.00005220603,0.00012612353,0.001278832,0.00019145261,0.0028184657],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999645,0.000033581346,0.000010139543,0.00013604845,0.000114560666,0.00006066333],"domain_scores_gemma":[0.99977344,0.000039973023,0.000045585544,0.000049043232,0.00007210112,0.000019900877],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00027576773,0.00064382487,0.0004427284,0.00095369125,0.00033356325,0.0004966307,0.0011949628,0.0005963657,0.000828687],"category_scores_gemma":[0.0007747344,0.00036727797,0.0004735213,0.0005880707,0.0004376347,0.0010267675,0.0010403318,0.0005878553,0.0004120356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024445835,0.00030015002,0.009888708,0.00012870814,0.00012166159,0.00025180157,0.00020402228,0.12830229,0.057987865,0.002577226,0.005087143,0.794906],"study_design_scores_gemma":[0.000010767308,0.00011248613,0.0057613384,0.000010490866,0.000014880255,0.00014798352,0.000069255104,0.9713572,0.01765911,0.002293673,0.0025453793,0.000017411254],"about_ca_topic_score_codex":0.004360858,"about_ca_topic_score_gemma":0.010344859,"teacher_disagreement_score":0.004360858,"about_ca_system_score_codex":0.00048248627,"about_ca_system_score_gemma":0.0004970017,"threshold_uncertainty_score":0.008670926},"labels":[],"label_agreement":null},{"id":"W4399412985","doi":"10.1109/jiot.2024.3409386","title":"CPBW: A Change-Point-Detection and Bag-of-Words-Based Mechanism Utilizing Smartphone Triaxial Accelerometer Data for Driver Identification","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of New Brunswick","funders":"National Science and Technology Council; Ministry of Science and Technology, Taiwan; Ministry of Higher Education","keywords":"Accelerometer; Computer science; Identification (biology); Mechanism (biology); Point (geometry); Real-time computing; Embedded system; Operating system","score_opus":0.11589729891258117,"score_gpt":0.34868248844222094,"score_spread":0.23278518952963978,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399412985","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06864303,0.0024540024,0.90516776,0.00034700934,0.00086345506,0.00076844206,0.0011996018,0.017249,0.0033077148],"genre_scores_gemma":[0.5875313,0.0014702954,0.39695027,0.0008550955,0.0005532074,0.0010013506,0.0027898299,0.00052523636,0.008323371],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9977639,0.00021225805,0.00017901624,0.0006506857,0.0009885352,0.00020562437],"domain_scores_gemma":[0.99669063,0.00071567035,0.00062671263,0.000629188,0.0010975349,0.00024020617],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017359367,0.0019037131,0.0014677396,0.0043518962,0.0005745879,0.001272293,0.0022742243,0.0014755523,0.0027591244],"category_scores_gemma":[0.0065843584,0.00058200094,0.0006570836,0.0024299729,0.00067815627,0.004983085,0.003961783,0.0010913621,0.0053835614],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0013148079,0.0005260706,0.008481824,0.000731023,0.0002698167,0.0004406265,0.00039093915,0.002750114,0.07015111,0.0029378978,0.011946942,0.90005887],"study_design_scores_gemma":[0.00042463368,0.003947963,0.045076538,0.0003880411,0.00096156483,0.007850025,0.0009953188,0.54685616,0.30027387,0.018135097,0.07432789,0.00076305517],"about_ca_topic_score_codex":0.0010311921,"about_ca_topic_score_gemma":0.0008431383,"teacher_disagreement_score":0.0043518962,"about_ca_system_score_codex":0.00034905237,"about_ca_system_score_gemma":0.0007853276,"threshold_uncertainty_score":0.0092301965},"labels":[],"label_agreement":null},{"id":"W4399469452","doi":"10.1016/j.autcon.2024.105486","title":"Multiscale object detection on complex architectural floor plans","year":2024,"lang":"en","type":"article","venue":"Automation in Construction","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"Mitacs","keywords":"Computer science; Object (grammar); Artificial intelligence; Engineering drawing; Computer vision; Engineering","score_opus":0.024501582330263796,"score_gpt":0.29937838099873676,"score_spread":0.274876798668473,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399469452","genre_codex":"empirical","genre_gemma":"methods","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"methods","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.6352445,0.00068014616,0.3563294,0.00013218913,0.00005801799,0.000075238546,0.00045804793,0.0027583062,0.004264227],"genre_scores_gemma":[0.9294884,0.00026293955,0.06785614,0.000047573863,0.000016762162,0.000014521156,0.00070197927,0.00006390564,0.0015478705],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978155,0.000021333193,0.000006859778,0.00006622005,0.00007639317,0.000047615886],"domain_scores_gemma":[0.99985886,0.00003234965,0.000022278055,0.00002483496,0.000042261607,0.000019302348],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026190176,0.0004933524,0.00041506765,0.0014303521,0.0001906006,0.0004803483,0.00040262454,0.00027631197,0.00094828196],"category_scores_gemma":[0.0005090233,0.00024145232,0.0004381947,0.00061741186,0.0002965446,0.00046666258,0.0006239928,0.00025580855,0.00024436417],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043976257,0.000103448445,0.014151153,0.00020353188,0.00014884023,0.0007263706,0.00023440532,0.13317278,0.23941195,0.0031097934,0.0049989135,0.6032991],"study_design_scores_gemma":[0.0000078631865,0.000057190435,0.026176449,0.000016481576,0.000042575204,0.00022021022,0.00008205302,0.9375704,0.032672595,0.0012103313,0.0019270338,0.000016821927],"about_ca_topic_score_codex":0.0075859306,"about_ca_topic_score_gemma":0.01361823,"teacher_disagreement_score":0.0075859306,"about_ca_system_score_codex":0.0003449094,"about_ca_system_score_gemma":0.0003890234,"threshold_uncertainty_score":0.015083551},"labels":[],"label_agreement":null},{"id":"W4399876326","doi":"10.1007/978-3-031-62269-4_2","title":"SSIVD-Net: A Novel Salient Super Image Classification and Detection Technique for Weaponized Violence","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Salient; Computer science; Net (polyhedron); Artificial intelligence; Computer security; Pattern recognition (psychology); Mathematics","score_opus":0.02456140408514403,"score_gpt":0.2710354966148161,"score_spread":0.24647409252967203,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4399876326","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017228816,0.0011585366,0.9712251,0.00015167547,0.00033262378,0.00014231807,0.0007297794,0.004803061,0.004228232],"genre_scores_gemma":[0.12847477,0.0012998921,0.8494611,0.00034360835,0.0002024822,0.00014598719,0.0026507103,0.0005108101,0.016910678],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99968326,0.000028835495,0.000013375891,0.000067070876,0.0001683904,0.000039135124],"domain_scores_gemma":[0.99975616,0.000050614166,0.00002418902,0.000043401393,0.00009823047,0.000027463599],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044279234,0.00094094157,0.00089852046,0.0014839245,0.00042352368,0.0007110152,0.0015119503,0.00066155806,0.004413504],"category_scores_gemma":[0.0005646408,0.0004061863,0.0007217502,0.0010113742,0.00032337543,0.00080496294,0.0010461186,0.00078578683,0.0021714475],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032490902,0.00015475025,0.0009916279,0.00015402262,0.00006020521,0.00010305553,0.000031639986,0.009477587,0.06867779,0.0024858443,0.014855352,0.9026832],"study_design_scores_gemma":[0.000031851403,0.00022050337,0.003093458,0.000035079407,0.00008046367,0.00066757546,0.00004977826,0.8552525,0.108276516,0.003943478,0.028304078,0.00004468005],"about_ca_topic_score_codex":0.0031944609,"about_ca_topic_score_gemma":0.008115518,"teacher_disagreement_score":0.004413504,"about_ca_system_score_codex":0.000466143,"about_ca_system_score_gemma":0.0006785441,"threshold_uncertainty_score":0.014764667},"labels":[],"label_agreement":null},{"id":"W4400061124","doi":"10.3390/app14135559","title":"A Multi-Stage Approach to UAV Detection, Identification, and Tracking Using Region-of-Interest Management and Rate-Adaptive Video Coding","year":2024,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"Korea Institute of Energy Technology Evaluation and Planning; National Research Foundation of Korea","keywords":"Computer science; Region of interest; Artificial intelligence; Computer vision; ENCODE; Coding (social sciences); Drone; Real-time computing","score_opus":0.20592806616059806,"score_gpt":0.34961529259937524,"score_spread":0.14368722643877718,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400061124","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008535289,0.00030303776,0.9900165,0.00005994928,0.00002946253,0.000050517898,0.000024290332,0.00040385828,0.0005770354],"genre_scores_gemma":[0.30935332,0.0006012757,0.6853246,0.0001466087,0.00007571785,0.00011399543,0.00018804906,0.000081514794,0.004114966],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99937785,0.000096148164,0.00004061281,0.00019387322,0.00021417202,0.00007729208],"domain_scores_gemma":[0.999433,0.00015804918,0.00008237678,0.00008370314,0.00020593929,0.00003689542],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078238273,0.0006478275,0.0006034226,0.0011777113,0.0002851371,0.00058061583,0.001197096,0.0007560332,0.0007731716],"category_scores_gemma":[0.0013263014,0.0003772894,0.0008809118,0.0006522798,0.00033251202,0.0009486295,0.0007031917,0.00080878654,0.0005058229],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028997991,0.00021097515,0.0028769218,0.00012217072,0.00012692313,0.0002667828,0.00019995481,0.09257108,0.17541786,0.0055395816,0.0019873725,0.7203903],"study_design_scores_gemma":[0.000006970236,0.00016849708,0.0014725892,0.0000137138195,0.000037463564,0.000239992,0.000029494518,0.96249336,0.032721013,0.0011659842,0.0016278384,0.000023168184],"about_ca_topic_score_codex":0.005035882,"about_ca_topic_score_gemma":0.004645002,"teacher_disagreement_score":0.005035882,"about_ca_system_score_codex":0.0004722864,"about_ca_system_score_gemma":0.0006506537,"threshold_uncertainty_score":0.0100131035},"labels":[],"label_agreement":null},{"id":"W4400510070","doi":"10.1007/s44212-024-00053-9","title":"Understanding pedestrian movement using urban sensing technologies: the promise of audio-based sensors","year":2024,"lang":"en","type":"article","venue":"Urban Informatics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Division of Civil, Mechanical and Manufacturing Innovation; National Science Foundation","keywords":"Pedestrian; Movement (music); Computer science; Computer vision; Human–computer interaction; Transport engineering; Engineering; Acoustics","score_opus":0.11304605499846658,"score_gpt":0.2954515601075431,"score_spread":0.1824055051090765,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400510070","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.3882907,0.007825771,0.5575745,0.003207862,0.0011392325,0.00025826594,0.008993309,0.0023949232,0.030315418],"genre_scores_gemma":[0.7998051,0.0036502054,0.18786204,0.00050336414,0.0006455013,0.00012129258,0.0043405546,0.000108056134,0.0029639842],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99965703,0.000100001635,0.000016056818,0.00008777144,0.00010278887,0.00003625693],"domain_scores_gemma":[0.9990079,0.00044175368,0.00011531255,0.00012731743,0.00025008025,0.000057710156],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004775645,0.0006555992,0.00036870138,0.0013926538,0.00026112061,0.0011035884,0.00050718436,0.0005953943,0.0015002742],"category_scores_gemma":[0.0016172142,0.00015604691,0.00033331977,0.001364794,0.0003375568,0.0011693141,0.0007439343,0.00062029576,0.0006424176],"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":[0.0007108155,0.0003969201,0.07159048,0.0015032624,0.00023806294,0.0006059355,0.00078772526,0.07852667,0.06670967,0.0072120656,0.02059399,0.7511244],"study_design_scores_gemma":[0.000099643585,0.0007628402,0.14928158,0.0008859082,0.0003395358,0.0013103755,0.0040187608,0.6706625,0.054419532,0.033015426,0.08495638,0.000247488],"about_ca_topic_score_codex":0.003458529,"about_ca_topic_score_gemma":0.006584776,"teacher_disagreement_score":0.003458529,"about_ca_system_score_codex":0.00024144562,"about_ca_system_score_gemma":0.00029799624,"threshold_uncertainty_score":0.0068768263},"labels":[],"label_agreement":null},{"id":"W4400527955","doi":"10.1109/fg59268.2024.10582018","title":"Audio-Visual Person Verification Based on Recursive Fusion of Joint Cross-Attention","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Computer Research Institute of Montréal","funders":"Government of Canada","keywords":"Joint (building); Computer science; Audio visual; Joint attention; Fusion; Speech recognition; Artificial intelligence; Human–computer interaction; Multimedia; Psychology; Engineering","score_opus":0.045260926220762034,"score_gpt":0.34349434518294847,"score_spread":0.2982334189621864,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400527955","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03946374,0.0010764957,0.95149446,0.00023608399,0.00017881994,0.00012567129,0.00031119847,0.0033977765,0.0037156653],"genre_scores_gemma":[0.7650261,0.00072913145,0.22371887,0.00050064444,0.0002099389,0.00012542494,0.0015156423,0.0002780883,0.007896177],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99862003,0.00018699959,0.000056653167,0.00055663387,0.0003538435,0.00022587046],"domain_scores_gemma":[0.9987419,0.00034951288,0.00010998991,0.0002766805,0.00042970935,0.000092266644],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019873164,0.0013753276,0.0016107077,0.0015230204,0.0005048164,0.0010863935,0.0021460413,0.0012631478,0.003448989],"category_scores_gemma":[0.0037805138,0.0005503616,0.0014553796,0.00093850575,0.00064935157,0.0021594535,0.0030691642,0.0018600064,0.0020426656],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072960014,0.00029696172,0.0030584547,0.00017377798,0.00031916212,0.00028587278,0.00024817453,0.08999478,0.05682956,0.0052083307,0.0048406115,0.8380147],"study_design_scores_gemma":[0.000014760302,0.00013926298,0.0024317843,0.000023710123,0.0000804821,0.00020258878,0.000033818324,0.97578317,0.015033757,0.004631686,0.0015957715,0.000029181645],"about_ca_topic_score_codex":0.011245367,"about_ca_topic_score_gemma":0.0126661295,"teacher_disagreement_score":0.011245367,"about_ca_system_score_codex":0.0008530348,"about_ca_system_score_gemma":0.001017201,"threshold_uncertainty_score":0.022359848},"labels":[],"label_agreement":null},{"id":"W4400872623","doi":"10.3390/electronics13142883","title":"Proposing an Efficient Deep Learning Algorithm Based on Segment Anything Model for Detection and Tracking of Vehicles through Uncalibrated Urban Traffic Surveillance Cameras","year":2024,"lang":"en","type":"article","venue":"Electronics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université Laval","funders":"Mitacs","keywords":"Computer science; Convolutional neural network; Deep learning; Robustness (evolution); Artificial intelligence; Segmentation; Traffic congestion; Precision and recall; Intelligent transportation system; Smart city; Real-time computing; Computer vision; Machine learning; Transport engineering; Computer security; Engineering","score_opus":0.017371093777433712,"score_gpt":0.27732337736496365,"score_spread":0.2599522835875299,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4400872623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.022698581,0.00030464696,0.9720061,0.00020029735,0.00007499513,0.0000725993,0.00019701965,0.0028162212,0.0016295548],"genre_scores_gemma":[0.48677447,0.0005914956,0.49857765,0.00067421235,0.000082905855,0.00025607742,0.0024859782,0.00029246436,0.01026477],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99967873,0.000025591635,0.000013791833,0.00015365734,0.00006855145,0.00005969467],"domain_scores_gemma":[0.9997998,0.00004366335,0.000030188929,0.000034187175,0.00007014218,0.00002199345],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00037032535,0.0011117216,0.00087590155,0.0008060199,0.00036219344,0.00078474334,0.0017537787,0.0011169348,0.0016118715],"category_scores_gemma":[0.0008167101,0.0005180521,0.0008028009,0.0006651905,0.00041654138,0.0013314805,0.0009933189,0.0012863268,0.0010208207],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027051038,0.00018040588,0.005436451,0.00010961355,0.00015879639,0.00016283678,0.00010436114,0.2687011,0.025562827,0.0077112154,0.007180904,0.6844211],"study_design_scores_gemma":[0.0000075761404,0.00004002045,0.00037678776,0.0000073433616,0.000016681382,0.00003956163,0.000012276689,0.99219877,0.00433665,0.0016726427,0.0012823782,0.000009364425],"about_ca_topic_score_codex":0.010757827,"about_ca_topic_score_gemma":0.01356786,"teacher_disagreement_score":0.010757827,"about_ca_system_score_codex":0.00080669526,"about_ca_system_score_gemma":0.0011910802,"threshold_uncertainty_score":0.021390438},"labels":[],"label_agreement":null},{"id":"W4401054769","doi":"10.1142/s0129065724500618","title":"Crowd Counting Using Meta-Test-Time Adaptation","year":2024,"lang":"en","type":"article","venue":"International Journal of Neural Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"China Scholarship Council","keywords":"Adaptation (eye); Computer science; Test (biology); Psychology; Biology; Neuroscience","score_opus":0.10432822033022178,"score_gpt":0.35218713034542876,"score_spread":0.247858910015207,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401054769","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":[0.038086627,0.00029303506,0.9554035,0.00024221858,0.00013199447,0.00014662565,0.00011678883,0.0033984978,0.0021808408],"genre_scores_gemma":[0.7663141,0.00018032173,0.22701354,0.00054777454,0.00012631007,0.00042271352,0.00064561464,0.00071403896,0.0040355516],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981377,0.00043638868,0.00008780243,0.0007280563,0.00040106944,0.00020891265],"domain_scores_gemma":[0.99575245,0.0015951601,0.00046781127,0.0008506568,0.0009938776,0.00034001842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0029544074,0.0025050663,0.0021147383,0.0014460083,0.0008898426,0.0018041957,0.0043878094,0.0021441928,0.0019204317],"category_scores_gemma":[0.011841024,0.0010026863,0.0014218761,0.0008570967,0.001990382,0.0036859645,0.0044935374,0.0023354047,0.00096037943],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00036516588,0.00028135497,0.0057084793,0.00013138891,0.00018242987,0.00032367828,0.00044758,0.7450866,0.00953335,0.0070199193,0.0046288385,0.22629118],"study_design_scores_gemma":[0.000010352522,0.00004642564,0.0003156239,0.000010247752,0.000014672171,0.00004145628,0.000030434458,0.9918932,0.0025287592,0.00444265,0.0006485097,0.000017785933],"about_ca_topic_score_codex":0.0042906846,"about_ca_topic_score_gemma":0.0040760934,"teacher_disagreement_score":0.0043878094,"about_ca_system_score_codex":0.0015873112,"about_ca_system_score_gemma":0.0014190511,"threshold_uncertainty_score":0.015624583},"labels":[],"label_agreement":null},{"id":"W4401070258","doi":"10.1109/jiot.2024.3435130","title":"PrFu-YOLO: A Lightweight Network Model for UAV-Assisted Real-Time Vehicle Detection Toward an IoT Underlayer","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"Foundation Research Project of Jiangsu Province; National Natural Science Foundation of China","keywords":"Computer science; Internet of Things; Real-time computing; Embedded system; Computer network","score_opus":0.048556782029088426,"score_gpt":0.31704179919802317,"score_spread":0.26848501716893475,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401070258","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.048670556,0.0004734786,0.9412244,0.0003625766,0.00015144424,0.00012597638,0.00042064395,0.0041986904,0.0043722135],"genre_scores_gemma":[0.8386192,0.00045049822,0.15252833,0.00029445806,0.00005607897,0.00031931375,0.0012422758,0.00026129992,0.006228552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99979216,0.000030605435,0.000009610207,0.00007094909,0.000054285738,0.00004247669],"domain_scores_gemma":[0.999648,0.00012813715,0.000040760504,0.000055233282,0.00010118337,0.000026717833],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004762382,0.0008640729,0.0005484808,0.0005282097,0.00040315723,0.00068174227,0.0018876087,0.00070774014,0.001650909],"category_scores_gemma":[0.001671752,0.0003060233,0.0006306063,0.00023673523,0.00040589384,0.0016065722,0.00078508403,0.0010269706,0.00056435354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025150934,0.000088097164,0.0021998116,0.000077912184,0.000045498204,0.00009835225,0.00006874558,0.9167055,0.00739446,0.0040950007,0.002702769,0.06627232],"study_design_scores_gemma":[0.000005387194,0.0000261106,0.00014501112,0.000003022942,0.0000058186324,0.000012939719,0.0000058001892,0.99766636,0.0008218322,0.00066735584,0.0006357623,0.0000045403112],"about_ca_topic_score_codex":0.018580342,"about_ca_topic_score_gemma":0.01996657,"teacher_disagreement_score":0.018580342,"about_ca_system_score_codex":0.0010004586,"about_ca_system_score_gemma":0.0009949782,"threshold_uncertainty_score":0.03694439},"labels":[],"label_agreement":null},{"id":"W4401130601","doi":"10.18280/ijsdp.190705","title":"Employing 360° Video Panorama Technology to Determine the Impact of Details on the Collective Memory of the Urban Scene","year":2024,"lang":"en","type":"article","venue":"International Journal of Sustainable Development and Planning","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Panorama; Computer science; Computer graphics (images); Computer vision","score_opus":0.025910510007313765,"score_gpt":0.32047533181916754,"score_spread":0.2945648218118538,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401130601","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":[0.97793216,0.00012259946,0.011537425,0.00003759044,0.0000095515015,0.00010326006,0.00010837435,0.00002831342,0.010120674],"genre_scores_gemma":[0.9881495,0.00014901896,0.010029431,0.000015213186,0.0000051410234,0.00006254525,0.000045758123,0.000008790068,0.001534568],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99975806,0.00008386567,0.0000070819483,0.000046619687,0.000065742344,0.00003860384],"domain_scores_gemma":[0.9990988,0.00053656666,0.0000905902,0.00006831504,0.00016547785,0.00004026665],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004753955,0.00018010526,0.00008697855,0.0011357848,0.00044752914,0.0008561694,0.00022449491,0.0002443192,0.0039508785],"category_scores_gemma":[0.002567354,0.000114119124,0.000110259985,0.00079167297,0.00056810246,0.0008095927,0.00081635104,0.00027687606,0.00024219311],"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":[0.0011521112,0.00029921663,0.18675004,0.0007410628,0.000074378186,0.0013362226,0.071988076,0.002067354,0.3408676,0.008288505,0.0013168025,0.38511872],"study_design_scores_gemma":[0.00004447549,0.002014578,0.72425604,0.00025467345,0.00015942211,0.001743931,0.11470733,0.01292076,0.12283874,0.002130953,0.01879913,0.00012983405],"about_ca_topic_score_codex":0.0036730815,"about_ca_topic_score_gemma":0.0110490285,"teacher_disagreement_score":0.0039508785,"about_ca_system_score_codex":0.0002934878,"about_ca_system_score_gemma":0.00024567053,"threshold_uncertainty_score":0.013216972},"labels":[],"label_agreement":null},{"id":"W4401211278","doi":"10.1109/tnsm.2024.3436674","title":"UAV-Employed Intelligent Approach to Identify Injured Soldier on Blockchain-Integrated Internet of Battlefield Things","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Network and Service Management","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Blockchain; Battlefield; Computer science; Internet of Things; The Internet; Computer security; Embedded system; Computer network; World Wide Web","score_opus":0.02693479638974784,"score_gpt":0.2869319036430954,"score_spread":0.25999710725334757,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401211278","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":[0.27883396,0.00097235106,0.70678085,0.00065038895,0.00016580267,0.00020343979,0.00018336081,0.0017317169,0.010478143],"genre_scores_gemma":[0.98180765,0.00016941526,0.015239619,0.00010837076,0.000020145331,0.000034914996,0.000114967435,0.000008719614,0.002496273],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997459,0.00003916771,0.000015425254,0.000058884616,0.00007526409,0.00006531099],"domain_scores_gemma":[0.99978846,0.000044229306,0.000036840498,0.00003606807,0.000066821085,0.000027589496],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028205372,0.00043505392,0.00037437765,0.0003776199,0.00034693576,0.0005042797,0.0005211552,0.00046243166,0.0009877919],"category_scores_gemma":[0.0005625726,0.00011066436,0.00020929617,0.00026632877,0.00026906532,0.0010526407,0.00082013686,0.00034080018,0.00030795997],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00094580516,0.00041697684,0.028351972,0.0002928929,0.00015102446,0.0014833388,0.0005497285,0.3621669,0.06389494,0.012402739,0.005330729,0.524013],"study_design_scores_gemma":[0.0000114938475,0.0001588651,0.0019495307,0.000011983462,0.000024742021,0.00017920054,0.00010591553,0.9856695,0.0070563504,0.0029310193,0.0018883205,0.000013082147],"about_ca_topic_score_codex":0.0029176802,"about_ca_topic_score_gemma":0.0037247564,"teacher_disagreement_score":0.0029176802,"about_ca_system_score_codex":0.00035517206,"about_ca_system_score_gemma":0.0005133508,"threshold_uncertainty_score":0.0058014393},"labels":[],"label_agreement":null},{"id":"W4401413681","doi":"10.1109/icra57147.2024.10610458","title":"UncertaintyTrack: Exploiting Detection and Localization Uncertainty in Multi-Object Tracking","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Object detection; Computer vision; Artificial intelligence; Tracking (education); Object (grammar); Video tracking; Pattern recognition (psychology)","score_opus":0.048000410167381394,"score_gpt":0.3192208509532127,"score_spread":0.2712204407858313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401413681","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":[0.010378038,0.0012294706,0.9806661,0.00020191005,0.00012814114,0.00009070092,0.00049393205,0.0055499384,0.0012617249],"genre_scores_gemma":[0.2960523,0.0010444105,0.69164413,0.0006929628,0.00035248772,0.0002662042,0.004230347,0.0011979195,0.004519286],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9979506,0.0003363008,0.000111192116,0.00077059685,0.0006603001,0.00017106583],"domain_scores_gemma":[0.9965224,0.0015939536,0.0002969745,0.00088788016,0.0005175777,0.00018121842],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034137054,0.0017793018,0.0019298209,0.0021545896,0.0009283433,0.0023742036,0.003257366,0.0020737476,0.0021403122],"category_scores_gemma":[0.011139622,0.0010173459,0.0014270358,0.0021899294,0.0011185559,0.0035235204,0.0049005384,0.0024595533,0.0012752187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005096868,0.000183073,0.0076151956,0.00030203935,0.00038553932,0.00023097175,0.00037426755,0.2456433,0.010523742,0.009958568,0.016823264,0.7074504],"study_design_scores_gemma":[0.00003678893,0.000070421556,0.0009858839,0.000041341096,0.000050822833,0.00017295465,0.000033607157,0.97854906,0.0038745548,0.011043739,0.00510164,0.000039117866],"about_ca_topic_score_codex":0.0111964615,"about_ca_topic_score_gemma":0.014278933,"teacher_disagreement_score":0.0111964615,"about_ca_system_score_codex":0.0010818029,"about_ca_system_score_gemma":0.0016818965,"threshold_uncertainty_score":0.022262573},"labels":[],"label_agreement":null},{"id":"W4401417299","doi":"10.1109/icra57147.2024.10611067","title":"SWTrack: Multiple Hypothesis Sliding Window 3D Multi-Object Tracking","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Window (computing); Computer vision; Artificial intelligence; Sliding window protocol; Video tracking; Tracking (education); Object (grammar); Psychology","score_opus":0.07589495426686521,"score_gpt":0.30791381964234593,"score_spread":0.23201886537548072,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401417299","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02022825,0.0007409399,0.92662704,0.00015623002,0.00020080432,0.00031712794,0.0052604564,0.044529743,0.0019393652],"genre_scores_gemma":[0.17587729,0.0003082275,0.7955413,0.00019522021,0.00006829087,0.0003975817,0.023100777,0.0015697317,0.0029415956],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992186,0.000088878085,0.00003463979,0.0003464159,0.00025055342,0.000060931183],"domain_scores_gemma":[0.9993736,0.00015025452,0.00007174524,0.00021486013,0.00013197442,0.000057527493],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010720096,0.0016662024,0.0012008448,0.0016669838,0.0006883797,0.0012599761,0.0033366105,0.0013285999,0.0045216936],"category_scores_gemma":[0.002298371,0.0008567766,0.0011761424,0.0017492599,0.0005037138,0.0013020787,0.0020352828,0.0013518757,0.002570072],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008979561,0.00048483763,0.006573997,0.00052673765,0.00071445486,0.00043648639,0.0002644532,0.16191374,0.024186501,0.0054961415,0.0907253,0.7077794],"study_design_scores_gemma":[0.00010114146,0.000086700085,0.0016092332,0.000017252609,0.000024740199,0.00014184242,0.000031159776,0.979878,0.0060176714,0.0030546777,0.009005742,0.000031941476],"about_ca_topic_score_codex":0.016923811,"about_ca_topic_score_gemma":0.03678662,"teacher_disagreement_score":0.016923811,"about_ca_system_score_codex":0.00049356546,"about_ca_system_score_gemma":0.0012462628,"threshold_uncertainty_score":0.033650637},"labels":[],"label_agreement":null},{"id":"W4401617669","doi":"10.1016/j.neucom.2024.128415","title":"MIMTracking: Masked image modeling enhanced vision transformer for visual object tracking","year":2024,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Social Sciences and Humanities Research Council of Canada; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer vision; Artificial intelligence; Computer science; Transformer; Tracking (education); Engineering; Psychology; Electrical engineering; Voltage","score_opus":0.028796200350374866,"score_gpt":0.35393331389843113,"score_spread":0.32513711354805624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401617669","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0034195106,0.00006611573,0.9943197,0.00002361968,0.000032514687,0.000021221284,0.00007133049,0.0014208673,0.0006251163],"genre_scores_gemma":[0.24370912,0.0002871201,0.7459084,0.00020683426,0.00005938715,0.00009657244,0.0008326681,0.0005359043,0.008364068],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99980396,0.000028828032,0.000008843704,0.000048572416,0.000086623644,0.000023209925],"domain_scores_gemma":[0.99979955,0.000041522322,0.000017703404,0.000058770238,0.00006378421,0.000018681301],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004200195,0.0005959893,0.00047150638,0.00044796063,0.00020652288,0.00063254696,0.0010585756,0.000582456,0.0051086326],"category_scores_gemma":[0.0008752229,0.0003113814,0.0005641526,0.0005102859,0.00021397832,0.0009859622,0.0009941292,0.0007529061,0.0023701782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00086514466,0.00018022557,0.0008028939,0.00018172641,0.0000861234,0.0002599811,0.000091281116,0.046659883,0.18392172,0.017737322,0.011761826,0.73745185],"study_design_scores_gemma":[0.000026564643,0.000120576566,0.00034770224,0.000008053642,0.000026447655,0.00027010075,0.000013458095,0.9369859,0.051558465,0.00457245,0.006054206,0.000016104805],"about_ca_topic_score_codex":0.0019466468,"about_ca_topic_score_gemma":0.0023675573,"teacher_disagreement_score":0.0051086326,"about_ca_system_score_codex":0.000317277,"about_ca_system_score_gemma":0.00057944184,"threshold_uncertainty_score":0.017090023},"labels":[],"label_agreement":null},{"id":"W4401887634","doi":"10.1007/s13042-024-02345-7","title":"Beyond traditional visual object tracking: a survey","year":2024,"lang":"en","type":"article","venue":"International Journal of Machine Learning and Cybernetics","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Artificial intelligence; Computational intelligence; Computer vision; Tracking (education); Object (grammar); Video tracking; Eye tracking; Psychology","score_opus":0.02870302553371047,"score_gpt":0.32823762641672416,"score_spread":0.2995346008830137,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4401887634","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":[0.010368721,0.68360543,0.28510734,0.0016359655,0.00086724455,0.00015903744,0.00043356416,0.0009764614,0.016846208],"genre_scores_gemma":[0.11031364,0.71385723,0.1568942,0.0020526964,0.002897735,0.00016195726,0.0018716091,0.00037283418,0.011578082],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.998209,0.00025447065,0.00014465368,0.00073816616,0.0005685185,0.00008508171],"domain_scores_gemma":[0.99201787,0.005070566,0.00037744586,0.00079605,0.0015450489,0.0001929806],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0034442197,0.0013878471,0.0022729859,0.003992318,0.0006709692,0.0035504103,0.0027535157,0.0023120928,0.0027621186],"category_scores_gemma":[0.007478538,0.0011320183,0.0011035496,0.0067661386,0.0009791809,0.0050209784,0.0015167377,0.0014179568,0.0022137486],"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":[0.00008751616,0.000106904794,0.0034535103,0.002015116,0.00012396443,0.000044556644,0.00011073634,0.0033180048,0.0019156213,0.00625318,0.0060208407,0.97655004],"study_design_scores_gemma":[0.000084912965,0.0012765797,0.02281803,0.004791481,0.0010617144,0.0041826433,0.00082477136,0.17708042,0.026511692,0.06407279,0.6969381,0.0003569801],"about_ca_topic_score_codex":0.0047351196,"about_ca_topic_score_gemma":0.0036894842,"teacher_disagreement_score":0.0047351196,"about_ca_system_score_codex":0.00085184065,"about_ca_system_score_gemma":0.0018358225,"threshold_uncertainty_score":0.018215},"labels":[],"label_agreement":null},{"id":"W4402351259","doi":"10.1109/ijcnn60899.2024.10650520","title":"POPD: Partial Occluded Pedestrian Detection Using A Multimodal Deep Learning Approach","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Brandon University","funders":"","keywords":"Pedestrian detection; Pedestrian; Computer science; Artificial intelligence; Deep learning; Computer vision; Machine learning; Engineering; Transport engineering","score_opus":0.042400120565358225,"score_gpt":0.315042464960406,"score_spread":0.27264234439504775,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402351259","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10624596,0.0005688943,0.8858227,0.00029587204,0.00012306923,0.0001198295,0.0003880375,0.0028067771,0.003628751],"genre_scores_gemma":[0.7564417,0.0003000196,0.23572724,0.00034074753,0.0000659629,0.000095477924,0.0009333616,0.000087527274,0.006007948],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996562,0.000057734997,0.0000109626835,0.00008369486,0.00009885214,0.00009246576],"domain_scores_gemma":[0.99975616,0.000042362215,0.000028500923,0.00004543866,0.00009126151,0.000036256515],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006379374,0.0008558832,0.0006735098,0.000925496,0.0002832367,0.00052500254,0.0010260014,0.0006950152,0.0015737113],"category_scores_gemma":[0.00097359344,0.00031533706,0.0005201894,0.00050551654,0.00037400256,0.0007490512,0.0015220874,0.0007725088,0.0005461442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00075301743,0.00040201106,0.008785496,0.0001725633,0.0001786726,0.0004894077,0.00018368514,0.13353328,0.04387462,0.003101788,0.009358339,0.7991672],"study_design_scores_gemma":[0.0000108583345,0.00010546922,0.001560687,0.000013001998,0.000026872536,0.00017693538,0.000025749383,0.9840954,0.011050435,0.0012203525,0.0016983512,0.00001589014],"about_ca_topic_score_codex":0.0037350138,"about_ca_topic_score_gemma":0.0056014163,"teacher_disagreement_score":0.0037350138,"about_ca_system_score_codex":0.00056245737,"about_ca_system_score_gemma":0.00078031316,"threshold_uncertainty_score":0.00742656},"labels":[],"label_agreement":null},{"id":"W4402475286","doi":"10.1109/jsen.2024.3454544","title":"Transformer-Based Dog Behavior Classification With Motion Sensors","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Transformer; Computer science; Artificial intelligence; Electrical engineering; Engineering; Voltage","score_opus":0.04166590432061326,"score_gpt":0.3158318087162726,"score_spread":0.27416590439565935,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402475286","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33752188,0.00135945,0.6488745,0.00047622868,0.00031984225,0.00010898879,0.0011287463,0.0026921823,0.007518179],"genre_scores_gemma":[0.956446,0.0002893297,0.03826526,0.00013249839,0.0000415558,0.000033580072,0.0011554242,0.000046544057,0.003589892],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9998344,0.000022834533,0.000008385804,0.00006689882,0.00003330155,0.000034219902],"domain_scores_gemma":[0.99987185,0.000037880982,0.000018065173,0.00001444171,0.000044255234,0.000013481736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002600069,0.000619686,0.00045642728,0.00062694214,0.0001344218,0.00037310921,0.0006326504,0.00037122547,0.0011150653],"category_scores_gemma":[0.00073839934,0.00017962146,0.0004553491,0.0005542873,0.00022576997,0.0007730334,0.00048510637,0.00051647925,0.0004413299],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007674248,0.00046715303,0.0221924,0.0002129202,0.00015623188,0.00033730452,0.00015210742,0.2427899,0.04304224,0.0046496578,0.010743884,0.67448884],"study_design_scores_gemma":[0.00000617959,0.000047272533,0.0023265472,0.000007155862,0.000017843997,0.00005946417,0.000027528476,0.99213505,0.0031425857,0.0014556586,0.00076866173,0.000006137614],"about_ca_topic_score_codex":0.0051594423,"about_ca_topic_score_gemma":0.007483392,"teacher_disagreement_score":0.0051594423,"about_ca_system_score_codex":0.00050068053,"about_ca_system_score_gemma":0.0004294493,"threshold_uncertainty_score":0.010258794},"labels":[],"label_agreement":null},{"id":"W4402593399","doi":"10.1109/icce-taiwan62264.2024.10674280","title":"3D-Multi-Hypothesis Tracker for Multi-Object Tracking Applications","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Tracking (education); Video tracking; Object (grammar); Psychology","score_opus":0.1277607459226035,"score_gpt":0.3705864801415646,"score_spread":0.2428257342189611,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402593399","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0047831675,0.00062158355,0.98371243,0.00009882228,0.00016599218,0.0000757355,0.0010580962,0.008459108,0.0010250008],"genre_scores_gemma":[0.11700597,0.0005675708,0.86904246,0.00026104992,0.00011094908,0.0002186527,0.009126561,0.0006928299,0.0029739165],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99820316,0.00029383728,0.00008913596,0.0006513187,0.0006419515,0.00012060243],"domain_scores_gemma":[0.998212,0.00035559852,0.00014879668,0.0006538524,0.00051161763,0.00011829044],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0018017602,0.0013049174,0.0013387671,0.0022670967,0.00071418117,0.001465743,0.0022692096,0.0016914182,0.0058381343],"category_scores_gemma":[0.0037619204,0.000727689,0.001529709,0.0022341998,0.00049265055,0.0017293617,0.0023519627,0.0015926484,0.0055672447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030721384,0.0002328887,0.0077315615,0.00040537497,0.00033292454,0.00034745256,0.00018711796,0.07544077,0.044121612,0.005790627,0.05674886,0.8083536],"study_design_scores_gemma":[0.000029964476,0.000094079165,0.0036936214,0.00003785189,0.00003906163,0.00036528302,0.00005185318,0.9513897,0.01760462,0.0055038473,0.021143911,0.000046259072],"about_ca_topic_score_codex":0.0054916255,"about_ca_topic_score_gemma":0.010295762,"teacher_disagreement_score":0.0058381343,"about_ca_system_score_codex":0.0007230833,"about_ca_system_score_gemma":0.0014338734,"threshold_uncertainty_score":0.019530475},"labels":[],"label_agreement":null},{"id":"W4402713103","doi":"10.1109/cvpr52733.2024.02108","title":"MTMMC: A Large-Scale Real-World Multi-Modal Camera Tracking Benchmark","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Neurosciences Research Foundation","keywords":"Benchmark (surveying); Computer science; Modal; Scale (ratio); Tracking (education); Artificial intelligence; Computer vision; Geography","score_opus":0.033590713980589944,"score_gpt":0.33730186451516575,"score_spread":0.3037111505345758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402713103","genre_codex":"dataset","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.35451287,0.016912458,0.10139529,0.003197239,0.0041054795,0.0032217747,0.42953232,0.050130945,0.036991674],"genre_scores_gemma":[0.18114775,0.0013342189,0.084839664,0.0008183068,0.00029939844,0.00079422124,0.72278035,0.00097562163,0.007010512],"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99660707,0.0005681065,0.00026059768,0.0012915053,0.0008546437,0.0004180903],"domain_scores_gemma":[0.99694985,0.0006272925,0.0002620455,0.00093753904,0.00090054487,0.00032280662],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002764664,0.004026341,0.002035831,0.0032815947,0.0018037502,0.0021962414,0.0046665864,0.0038608538,0.0047504315],"category_scores_gemma":[0.0074437107,0.00052053115,0.0021636693,0.0044742296,0.0008626139,0.0021623096,0.002035706,0.0023547972,0.0043588793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0021669536,0.002781224,0.021378214,0.0030173985,0.0013358874,0.00083448505,0.0001994587,0.12137003,0.010248559,0.0030171135,0.5956825,0.2379682],"study_design_scores_gemma":[0.00073103706,0.0012570688,0.05037104,0.0006967088,0.00039777384,0.0018765278,0.0005456554,0.79067004,0.019607106,0.006704079,0.12683426,0.00030872098],"about_ca_topic_score_codex":0.046627197,"about_ca_topic_score_gemma":0.07413424,"teacher_disagreement_score":0.046627197,"about_ca_system_score_codex":0.002860893,"about_ca_system_score_gemma":0.0024063028,"threshold_uncertainty_score":0.09271157},"labels":[],"label_agreement":null},{"id":"W4402727422","doi":"10.1109/mwscas60917.2024.10658879","title":"Pedestrian and Cyclist Object Detection Using Thermal and Dash Cameras in Different Weather Conditions","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McGill University; Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dash; Pedestrian; Computer science; Computer vision; Object detection; Object (grammar); Artificial intelligence; Pedestrian detection; Environmental science; Meteorology; Remote sensing; Computer graphics (images); Transport engineering; Engineering; Geography","score_opus":0.026723242245016706,"score_gpt":0.3123374237750437,"score_spread":0.285614181530027,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402727422","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":[0.9716495,0.0005087569,0.01624689,0.00010047018,0.0001471304,0.00010078254,0.0048131547,0.0012390596,0.0051942347],"genre_scores_gemma":[0.971219,0.000246888,0.015800726,0.00007450486,0.00004356573,0.000050867046,0.009761371,0.00005812801,0.0027450426],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.9995577,0.000050689516,0.00001813876,0.00017498636,0.00009180523,0.000106712076],"domain_scores_gemma":[0.9996743,0.0000567718,0.000039380408,0.000044702225,0.00013134355,0.000053614127],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040034478,0.0008136185,0.0005376173,0.0011100438,0.00029651774,0.00054053275,0.00040894287,0.0004831218,0.0015147799],"category_scores_gemma":[0.0009574111,0.000195161,0.00037312755,0.00051990413,0.00025114033,0.0006010764,0.00065556733,0.00047565883,0.0006330518],"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":[0.0045545585,0.001134461,0.29882526,0.0014194896,0.0007677257,0.0016130196,0.0008292936,0.059255596,0.13524462,0.0011794734,0.026195241,0.46898127],"study_design_scores_gemma":[0.00008969319,0.001134164,0.5383936,0.00021592602,0.0003258738,0.001580545,0.0012900052,0.36818522,0.076924376,0.0011046306,0.010636386,0.00011948086],"about_ca_topic_score_codex":0.009244548,"about_ca_topic_score_gemma":0.02444749,"teacher_disagreement_score":0.009244548,"about_ca_system_score_codex":0.00032959765,"about_ca_system_score_gemma":0.00031235008,"threshold_uncertainty_score":0.018381476},"labels":[],"label_agreement":null},{"id":"W4402811381","doi":"10.1109/iccc62479.2024.10681868","title":"An Efficient Privacy-preserving Logistic Regression Scheme for Aging-in-place Systems","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Research and Productivity Council; University of New Brunswick","funders":"National Research Council","keywords":"Logistic regression; Computer science; Scheme (mathematics); Information privacy; Computer security; Machine learning; Mathematics","score_opus":0.06277697137704924,"score_gpt":0.37909964061714924,"score_spread":0.3163226692401,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402811381","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03579787,0.00052524375,0.95988566,0.00072488684,0.00012980613,0.00017946726,0.00032442078,0.00074139226,0.0016912434],"genre_scores_gemma":[0.9106097,0.00042064217,0.084493056,0.00022855638,0.000118140946,0.00016815796,0.00036620162,0.00003898454,0.0035565295],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99523646,0.0017786628,0.00039167228,0.00081514515,0.00113325,0.0006448857],"domain_scores_gemma":[0.9948612,0.0018042002,0.0006963033,0.0016381904,0.0008055251,0.0001945619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037477647,0.00085596874,0.0012857273,0.0007884901,0.0012847106,0.0016640581,0.0021716035,0.0010773353,0.0020435795],"category_scores_gemma":[0.011101657,0.00032282283,0.0010794983,0.0012556304,0.0009960847,0.003385604,0.004209961,0.0019248191,0.0010001918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0030501357,0.0005312692,0.006697161,0.00054541155,0.0004997861,0.0017101383,0.0012600105,0.37851995,0.057988945,0.17573053,0.0119058015,0.36156085],"study_design_scores_gemma":[0.00008373503,0.00034029805,0.00058974884,0.000027806691,0.00006829348,0.000583917,0.00012038472,0.9549838,0.010727103,0.027421897,0.0049777287,0.00007534295],"about_ca_topic_score_codex":0.0012485023,"about_ca_topic_score_gemma":0.0008687194,"teacher_disagreement_score":0.0037477647,"about_ca_system_score_codex":0.0011760858,"about_ca_system_score_gemma":0.0019130892,"threshold_uncertainty_score":0.019820273},"labels":[],"label_agreement":null},{"id":"W4402828205","doi":"10.1145/3675095.3676623","title":"RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Universitas Brawijaya","keywords":"Trajectory; Motion (physics); Estimation; Computer science; Computer vision; Engineering; Physics; Systems engineering","score_opus":0.0474296973191163,"score_gpt":0.2927261453739245,"score_spread":0.24529644805480819,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402828205","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.11083216,0.00062055956,0.64556646,0.00033236117,0.00048594325,0.0005127114,0.013318547,0.21822149,0.010109789],"genre_scores_gemma":[0.46692452,0.00027388995,0.49508855,0.00026744173,0.00010414703,0.00026556125,0.020479245,0.0040579056,0.012538749],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996631,0.000041928553,0.0000138682735,0.00007867079,0.00014051817,0.00006200605],"domain_scores_gemma":[0.9995609,0.000071919145,0.00003878733,0.00014107091,0.00011963768,0.00006762269],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004016621,0.0011160956,0.0007682472,0.0007976955,0.0002894436,0.0007450834,0.0012103722,0.0007083022,0.0074241725],"category_scores_gemma":[0.001646251,0.0004836185,0.00038797013,0.000646495,0.00018912376,0.0010275514,0.001529511,0.00054618844,0.004716889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0028978442,0.0007557676,0.018134972,0.00044937016,0.0003675713,0.00066850364,0.00024274552,0.020554451,0.052518852,0.001992761,0.19826193,0.7031552],"study_design_scores_gemma":[0.0002888619,0.00041473322,0.020040294,0.000041905307,0.0000543478,0.00054927665,0.0002235441,0.9256952,0.029224468,0.0020050197,0.021355575,0.00010679913],"about_ca_topic_score_codex":0.009001833,"about_ca_topic_score_gemma":0.025436249,"teacher_disagreement_score":0.009001833,"about_ca_system_score_codex":0.00029142338,"about_ca_system_score_gemma":0.0007255348,"threshold_uncertainty_score":0.024836361},"labels":[],"label_agreement":null},{"id":"W4402896990","doi":"10.1109/iwqos61813.2024.10682870","title":"OAVS: Efficient Online Learning of Streaming Policies for Drone-sourced Live Video Analytics","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China","keywords":"Drone; Computer science; Analytics; Video streaming; Online video; Multimedia; Online learning; Human–computer interaction; Data science; Real-time computing","score_opus":0.033894081186703416,"score_gpt":0.33232559577733906,"score_spread":0.29843151459063566,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402896990","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.051699467,0.0005791253,0.9419404,0.0002824782,0.000080536076,0.00014289533,0.00011743606,0.0037453915,0.0014122415],"genre_scores_gemma":[0.92323357,0.00019380661,0.0748858,0.00016406464,0.000054810887,0.00012358729,0.00018260402,0.00011228716,0.0010494732],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999471,0.00010431578,0.000029267036,0.00017748994,0.00013513672,0.00008283541],"domain_scores_gemma":[0.9986112,0.00075082056,0.00016694108,0.00012029912,0.00021744813,0.00013320064],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010518463,0.0009684376,0.0008823554,0.00038407653,0.00027856507,0.0006446332,0.0017045537,0.00071620476,0.001283105],"category_scores_gemma":[0.004470716,0.0003632407,0.0003819684,0.00024012101,0.0006163612,0.0010084473,0.0011526863,0.0016911912,0.00031866023],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00025807667,0.00030842196,0.003040337,0.00012422088,0.00005541818,0.00012158726,0.00013721769,0.82011414,0.007677604,0.0034587327,0.0025628856,0.16214146],"study_design_scores_gemma":[0.000009207616,0.000023521825,0.00010125776,0.000002621409,0.0000023873185,0.000006616091,0.0000064856868,0.99844533,0.0004869675,0.00076795515,0.00014482251,0.000002849605],"about_ca_topic_score_codex":0.0074033784,"about_ca_topic_score_gemma":0.0059177633,"teacher_disagreement_score":0.0074033784,"about_ca_system_score_codex":0.0007373369,"about_ca_system_score_gemma":0.0014578472,"threshold_uncertainty_score":0.014720559},"labels":[],"label_agreement":null},{"id":"W4402916206","doi":"10.1109/cvprw63382.2024.00279","title":"Zero-Shot Monocular Motion Segmentation in the Wild by Combining Deep Learning with Geometric Motion Model Fusion","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer vision; Motion (physics); Monocular; Zero (linguistics); Segmentation; Fusion; Computer science; Shot (pellet); Materials science","score_opus":0.029679201949488206,"score_gpt":0.28659299482963657,"score_spread":0.2569137928801484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4402916206","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.043771386,0.0005765408,0.94913447,0.0001910033,0.000081081,0.000069245594,0.0002849825,0.0040118624,0.0018794555],"genre_scores_gemma":[0.49665076,0.00045148705,0.4950374,0.0004441867,0.000103705635,0.00008965941,0.0027860121,0.0005708729,0.0038659298],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994549,0.0000614968,0.000023064667,0.00023877712,0.00012507215,0.00009662357],"domain_scores_gemma":[0.9995772,0.000095407326,0.000069346526,0.00012338786,0.000084373016,0.000050297273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005539023,0.0016020217,0.0012729221,0.00149759,0.00035926313,0.00084478926,0.0017843227,0.0010869446,0.00118338],"category_scores_gemma":[0.0012793075,0.00064273755,0.0009475477,0.0010867469,0.0007024723,0.0017101966,0.0015451824,0.0011620579,0.00070742285],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003615589,0.00022943516,0.0024447471,0.00022182378,0.00021804718,0.000209107,0.00020725763,0.24082586,0.063838005,0.004438736,0.0058455775,0.6811598],"study_design_scores_gemma":[0.000008556135,0.0000509171,0.00071666326,0.000012645136,0.000021844106,0.00007807062,0.00002851395,0.984403,0.0099332165,0.0035081476,0.001226602,0.000011817131],"about_ca_topic_score_codex":0.0069550136,"about_ca_topic_score_gemma":0.014151329,"teacher_disagreement_score":0.0069550136,"about_ca_system_score_codex":0.0009092587,"about_ca_system_score_gemma":0.0010710694,"threshold_uncertainty_score":0.013829112},"labels":[],"label_agreement":null},{"id":"W4403024551","doi":"10.1109/pacrim61180.2024.10690207","title":"Human Fall Detection Based on ResNet and LSTM Network in Surveillance Cameras","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Residual neural network; Computer science; Artificial intelligence; Computer vision; Deep learning","score_opus":0.01773959653255722,"score_gpt":0.2921449307949989,"score_spread":0.2744053342624417,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403024551","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":[0.55057544,0.0031274518,0.4306182,0.0007916034,0.00047035777,0.00018407323,0.001142046,0.007251526,0.005839337],"genre_scores_gemma":[0.9309934,0.00045906447,0.06417626,0.0001772209,0.000080943035,0.0000552092,0.0007864577,0.000052478103,0.0032190979],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99975854,0.000034812412,0.000013637963,0.0000964658,0.000054356537,0.00004223968],"domain_scores_gemma":[0.99981767,0.000046401212,0.000028570987,0.000019237508,0.0000695914,0.000018557379],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039984228,0.000919159,0.0006493807,0.0008823953,0.00020224936,0.0003585679,0.0007419541,0.00057999056,0.0010507382],"category_scores_gemma":[0.000882278,0.0002972049,0.00044910217,0.00048576895,0.00021343285,0.00071951543,0.00036324206,0.0005359895,0.0003434167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011472469,0.00057597586,0.0083849495,0.00021014594,0.00020740293,0.0006550913,0.00012618265,0.15852275,0.02849825,0.0012218879,0.009731652,0.79071844],"study_design_scores_gemma":[0.000007895228,0.00007130871,0.00213893,0.0000075443927,0.000015708396,0.000056454985,0.000015999347,0.99314994,0.0037113633,0.0005125592,0.00030543708,0.000006936645],"about_ca_topic_score_codex":0.014725278,"about_ca_topic_score_gemma":0.01700654,"teacher_disagreement_score":0.014725278,"about_ca_system_score_codex":0.00060962094,"about_ca_system_score_gemma":0.00038822985,"threshold_uncertainty_score":0.029279113},"labels":[],"label_agreement":null},{"id":"W4403096679","doi":"10.1145/3698399","title":"Motion-Aware Self-Supervised RGBT Tracking with Multi-Modality Hierarchical Transformers","year":2024,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"National Natural Science Foundation of China","keywords":"Computer science; Modality (human–computer interaction); Artificial intelligence; Computer vision; Transformer; Human–computer interaction","score_opus":0.04030909776654987,"score_gpt":0.32650423709373416,"score_spread":0.2861951393271843,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403096679","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.010643719,0.000100727346,0.98758405,0.00003168602,0.000020928725,0.00002238837,0.00003191511,0.00067754934,0.0008870231],"genre_scores_gemma":[0.5827905,0.00033359675,0.41114512,0.00020544353,0.00005882991,0.00011851657,0.00028760126,0.0002501706,0.0048101586],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997067,0.000044562523,0.000014023638,0.000101004574,0.00009963865,0.00003400331],"domain_scores_gemma":[0.9996939,0.00006777658,0.00004631227,0.00007333507,0.00009344305,0.000025288608],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00056062476,0.00056411745,0.00046795502,0.00040229535,0.00022410484,0.0005953562,0.0010135223,0.00045911202,0.0015743574],"category_scores_gemma":[0.0013137939,0.0003250941,0.00058032345,0.00046190518,0.0003844324,0.001369902,0.0011941015,0.00061574613,0.0005618666],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003308735,0.000115922216,0.0021595669,0.00017752148,0.0001074154,0.00014176697,0.0002608317,0.10507175,0.19405314,0.012560213,0.003743566,0.6812774],"study_design_scores_gemma":[0.000014796854,0.00006653853,0.00089893176,0.000009739988,0.000029622413,0.00013046747,0.000020664596,0.9589704,0.033013284,0.0043133376,0.0025118121,0.000020482988],"about_ca_topic_score_codex":0.0020096563,"about_ca_topic_score_gemma":0.0029185333,"teacher_disagreement_score":0.0020096563,"about_ca_system_score_codex":0.00035386576,"about_ca_system_score_gemma":0.00053012406,"threshold_uncertainty_score":0.0052667856},"labels":[],"label_agreement":null},{"id":"W4403558712","doi":"10.1016/j.imavis.2024.105303","title":"Multi-object tracking using score-driven hierarchical association strategy between predicted tracklets and objects","year":2024,"lang":"en","type":"article","venue":"Image and Vision Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Science and Technology Program of Guizhou Province; Petroleum Technology Research Centre; Guizhou Science and Technology Department; National Natural Science Foundation of China","keywords":"Association (psychology); Artificial intelligence; Tracking (education); Object (grammar); Computer science; Data association; Computer vision; Psychology","score_opus":0.047592910789067855,"score_gpt":0.37163663137624225,"score_spread":0.3240437205871744,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403558712","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.017302731,0.00015385021,0.9810628,0.000068280904,0.000054440363,0.000036893918,0.000057489568,0.00071112957,0.0005523311],"genre_scores_gemma":[0.5285953,0.00023839278,0.4640961,0.00019580801,0.00011713013,0.0001082567,0.0007758263,0.00016970096,0.0057034576],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9984164,0.00018481679,0.00008633505,0.00054038817,0.0005847895,0.00018727161],"domain_scores_gemma":[0.9981406,0.00043541656,0.00020234185,0.00028555247,0.00077237154,0.00016368275],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001987689,0.0009925691,0.0017286752,0.0016602839,0.0008894407,0.0013576924,0.0030055572,0.0016635868,0.0016893968],"category_scores_gemma":[0.0035343391,0.0006887659,0.0010967432,0.0024365066,0.00058938906,0.0017091191,0.0021537582,0.0014536697,0.0011765778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00069284014,0.0004809633,0.008527091,0.00012918597,0.00039971768,0.00027649626,0.00018027972,0.2136722,0.03622552,0.00883094,0.005174456,0.72541034],"study_design_scores_gemma":[0.000010224964,0.000057709298,0.00089659763,0.0000033060917,0.000033554355,0.00005525055,0.000007999342,0.99390996,0.0032622768,0.0013827215,0.0003697252,0.0000107154665],"about_ca_topic_score_codex":0.010075718,"about_ca_topic_score_gemma":0.015255245,"teacher_disagreement_score":0.010075718,"about_ca_system_score_codex":0.0008115867,"about_ca_system_score_gemma":0.0020810557,"threshold_uncertainty_score":0.020034134},"labels":[],"label_agreement":null},{"id":"W4403582447","doi":"10.1145/3627673.3679678","title":"Low Carbon Footprint Training for 1D-CNNs with Temporal Max-Pooling","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Pooling; Footprint; Carbon footprint; Computer science; Artificial intelligence; Training (meteorology); Pattern recognition (psychology); Carbon fibers; Algorithm; Geology; Meteorology; Geography; Greenhouse gas; Oceanography","score_opus":0.054725876632729244,"score_gpt":0.3129582060470761,"score_spread":0.2582323294143468,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403582447","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":[0.20386088,0.0022557857,0.7603737,0.0010943215,0.00039028615,0.00020969733,0.0013138033,0.0151972,0.015304304],"genre_scores_gemma":[0.7332928,0.00066303316,0.25123364,0.00076641253,0.000068963374,0.00028203224,0.0030493531,0.0007327024,0.009910986],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99973005,0.000033400956,0.000015520716,0.000093934825,0.00006710269,0.00005996334],"domain_scores_gemma":[0.99956304,0.00014649934,0.00004355387,0.00012997353,0.00008974116,0.000027127655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00051408145,0.0015546852,0.00059012265,0.00043424207,0.00036403714,0.00077703083,0.0023929668,0.0010101906,0.0051573575],"category_scores_gemma":[0.002130183,0.00055938,0.0007899638,0.0005442226,0.00049861026,0.0021844348,0.0009945935,0.001232817,0.0012372148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00046251633,0.00029845219,0.0049578967,0.00042990164,0.0002876261,0.0003127889,0.000088422086,0.48556086,0.03828018,0.009167042,0.017831873,0.44232243],"study_design_scores_gemma":[0.000018512459,0.000072131676,0.0005062836,0.000014645637,0.000020577218,0.00006216745,0.00001797341,0.98423135,0.009953384,0.0031826352,0.0019103442,0.000009987341],"about_ca_topic_score_codex":0.009260695,"about_ca_topic_score_gemma":0.02506854,"teacher_disagreement_score":0.009260695,"about_ca_system_score_codex":0.0013869255,"about_ca_system_score_gemma":0.0012520608,"threshold_uncertainty_score":0.018413544},"labels":[],"label_agreement":null},{"id":"W4403600886","doi":"10.1016/j.heliyon.2024.e39537","title":"RETRACTED: MobVGG: Ensemble technique for birds and drones prediction","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":10,"is_retracted":true,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Moncton","funders":"Deanship of Scientific Research, King Saud University; King Saud University","keywords":"Drone; Artificial intelligence; Computer science; Biology; Genetics","score_opus":0.0245959222148393,"score_gpt":0.3019121658990041,"score_spread":0.2773162436841648,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403600886","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.17108418,0.003270931,0.7676434,0.0016171442,0.0037157037,0.00027956578,0.006739399,0.036022138,0.009627641],"genre_scores_gemma":[0.74405,0.001016111,0.20710827,0.00066311384,0.00047608136,0.00018912824,0.0196588,0.0015760845,0.025262484],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.999706,0.000035466637,0.0000142753925,0.000105792875,0.00007160407,0.00006686025],"domain_scores_gemma":[0.9994684,0.0000827807,0.000024726638,0.00018132565,0.00020502866,0.000037652233],"candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.0007357888,0.0016566213,0.000846427,0.0010154626,0.0005202214,0.0007327195,0.0020190466,0.0013625744,0.006066069],"category_scores_gemma":[0.0020919095,0.00039417055,0.00091302855,0.00079677126,0.00024514314,0.0012944438,0.001081859,0.0018882765,0.003212081],"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":[0.00035244183,0.00013295251,0.012425674,0.00011537826,0.00018001637,0.00037783806,0.00014664987,0.11058653,0.0146853505,0.0017557727,0.06990459,0.7893368],"study_design_scores_gemma":[0.000012617762,0.000079329286,0.0031205816,0.000030943735,0.000032529828,0.00012276454,0.00006540109,0.9757085,0.0070936885,0.0018139313,0.011897896,0.000021697537],"about_ca_topic_score_codex":0.022142814,"about_ca_topic_score_gemma":0.027374413,"teacher_disagreement_score":0.99863744,"about_ca_system_score_codex":0.00038238248,"about_ca_system_score_gemma":0.00072298554,"threshold_uncertainty_score":0.044027865},"labels":[],"label_agreement":null},{"id":"W4403653270","doi":"10.21608/ijicis.2024.309762.1347","title":"Enhancement Online Multi _Object Tracking In Dynamic Environment","year":2024,"lang":"en","type":"article","venue":"International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences","topic":"Video Surveillance and Tracking Methods","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Object (grammar); Tracking (education); Computer vision; Artificial intelligence; Psychology","score_opus":0.03516765759120243,"score_gpt":0.36913881428473116,"score_spread":0.3339711566935287,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403653270","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02250215,0.00036207392,0.97293776,0.000066469416,0.00009722947,0.000022727856,0.000083828156,0.0024202818,0.0015075218],"genre_scores_gemma":[0.4348461,0.0006126415,0.55543923,0.00023749426,0.00009898506,0.000064381085,0.0010451801,0.0004958428,0.0071600913],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994875,0.00004009398,0.000020627986,0.0001783979,0.00020623709,0.00006714606],"domain_scores_gemma":[0.999443,0.00016121323,0.00006871324,0.00012778632,0.00015429179,0.000044999808],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00072291255,0.0008255115,0.0008411326,0.0008838532,0.0005112662,0.00083032076,0.0012058285,0.0007926399,0.001785552],"category_scores_gemma":[0.0016926015,0.0004449047,0.00063170237,0.0010215796,0.0003914797,0.0014715645,0.001810286,0.000875836,0.0013691214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00024475894,0.00012754058,0.0032517856,0.0001174914,0.00008569565,0.00021575876,0.00018893639,0.11303475,0.047789454,0.005738382,0.005676217,0.82352924],"study_design_scores_gemma":[0.000011289763,0.000076956356,0.0011323758,0.000011278098,0.00001921036,0.00020067178,0.000025088173,0.9768098,0.014035219,0.003241566,0.0044217617,0.000014803279],"about_ca_topic_score_codex":0.0036139626,"about_ca_topic_score_gemma":0.0052892053,"teacher_disagreement_score":0.0036139626,"about_ca_system_score_codex":0.00041990398,"about_ca_system_score_gemma":0.0010002218,"threshold_uncertainty_score":0.0071858168},"labels":[],"label_agreement":null},{"id":"W4403675078","doi":"10.1109/case59546.2024.10711592","title":"On the Robustness and Real-time Adaptation of UAV-based Crowd Localization in Complex and High-density Scenes","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Robustness (evolution); Computer science; Computer vision; Adaptation (eye); Artificial intelligence; Real-time computing; Psychology","score_opus":0.03892705984051504,"score_gpt":0.279064483799263,"score_spread":0.240137423958748,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403675078","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.07403712,0.0005296758,0.9206239,0.00021257777,0.00011935431,0.000059434267,0.00008482081,0.001768751,0.002564329],"genre_scores_gemma":[0.83299947,0.00041096477,0.16272776,0.00022209501,0.00010274226,0.0000778263,0.00040499694,0.00021888729,0.0028352272],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995492,0.00008506897,0.00001581118,0.0001813609,0.00009743907,0.000070985116],"domain_scores_gemma":[0.99933845,0.00031762567,0.00006607471,0.000099827324,0.00013351916,0.000044367534],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000715884,0.000986261,0.00063176587,0.00055155024,0.0004264834,0.0006875945,0.0010412972,0.0006498159,0.00088632916],"category_scores_gemma":[0.0033986564,0.0003146701,0.00052820164,0.0004439083,0.00060045486,0.0008697954,0.0011924948,0.00080540637,0.00047093726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000386304,0.00013235783,0.0041843024,0.00016233347,0.00012223181,0.00030149697,0.0004310996,0.5739894,0.03958793,0.0033739873,0.003451704,0.37387684],"study_design_scores_gemma":[0.000007045955,0.000051217536,0.0011581816,0.00001100179,0.000011596581,0.000066043925,0.00006038372,0.9899678,0.0062613273,0.0011894865,0.0012033248,0.0000125605575],"about_ca_topic_score_codex":0.0076808627,"about_ca_topic_score_gemma":0.006105896,"teacher_disagreement_score":0.0076808627,"about_ca_system_score_codex":0.00039155097,"about_ca_system_score_gemma":0.0006925124,"threshold_uncertainty_score":0.015272319},"labels":[],"label_agreement":null},{"id":"W4403744578","doi":"10.1007/s10846-024-02178-0","title":"Semi-Supervised Online Continual Learning for 3D Object Detection in Mobile Robotics","year":2024,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Artificial intelligence; Robotics; Computer science; Object (grammar); Object detection; Computer vision; Machine learning; Human–computer interaction; Pattern recognition (psychology); Robot","score_opus":0.034467232355753025,"score_gpt":0.31777487445760394,"score_spread":0.2833076421018509,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403744578","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.045789342,0.0009853295,0.9424584,0.00033674078,0.00008305814,0.00014541612,0.0006853735,0.007996717,0.0015196571],"genre_scores_gemma":[0.6624034,0.00037143196,0.3275883,0.0004526161,0.00012849866,0.00033672867,0.0039240425,0.00059032097,0.004204609],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982772,0.0003111387,0.000080406506,0.00083985017,0.00031552074,0.00017590355],"domain_scores_gemma":[0.9964483,0.001454331,0.00029777625,0.0010598536,0.00052015774,0.00021953502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025186064,0.0016028313,0.0019568577,0.0012520524,0.00084182015,0.0015365076,0.0057755485,0.0021041026,0.0021454429],"category_scores_gemma":[0.0056182807,0.0014169343,0.0014202942,0.0014107286,0.0019114091,0.0032378032,0.003365915,0.0029315879,0.0015725138],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004961804,0.0005292725,0.004640869,0.00034393725,0.0001884059,0.00020806518,0.00030154476,0.45585346,0.008329865,0.0029892572,0.0089147,0.5172045],"study_design_scores_gemma":[0.00001195306,0.00006921368,0.0005304353,0.000013451855,0.000008241434,0.000057371675,0.00003591995,0.9921205,0.0018864725,0.004242518,0.0010090048,0.000014927481],"about_ca_topic_score_codex":0.0072790063,"about_ca_topic_score_gemma":0.014095406,"teacher_disagreement_score":0.0072790063,"about_ca_system_score_codex":0.0012390706,"about_ca_system_score_gemma":0.0015145909,"threshold_uncertainty_score":0.014473259},"labels":[],"label_agreement":null},{"id":"W4403939347","doi":"10.1007/978-3-031-73004-7_24","title":"Accelerating Online Mapping and Behavior Prediction via Direct BEV Feature Attention","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Computer science; Feature (linguistics); Artificial intelligence; Machine learning","score_opus":0.03279462464547239,"score_gpt":0.2853607720970689,"score_spread":0.2525661474515965,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4403939347","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.037961714,0.00047412104,0.9512925,0.00019402438,0.00025607433,0.00006573699,0.0002585148,0.0063737994,0.0031234093],"genre_scores_gemma":[0.71244866,0.0004042837,0.2729065,0.0002970181,0.00014347113,0.000116409494,0.0010669676,0.00041499824,0.012201741],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996301,0.000031739935,0.000009050462,0.00013583289,0.00010473015,0.00008847729],"domain_scores_gemma":[0.99941516,0.00024062906,0.000035410427,0.0001187626,0.00014664525,0.00004344634],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00039878275,0.0011101913,0.0015974005,0.0009417815,0.00039705524,0.00078639324,0.0015018486,0.00092079694,0.0042307237],"category_scores_gemma":[0.0019896708,0.000539885,0.0005839407,0.0010347215,0.00026853173,0.0013878563,0.0016377955,0.001318288,0.0024015424],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027629963,0.0002931307,0.0024351391,0.00005474446,0.00006218458,0.00010224239,0.00004980482,0.053516783,0.020770703,0.0020363338,0.009199573,0.91120315],"study_design_scores_gemma":[0.000006929351,0.00004293693,0.0010008473,0.0000053117474,0.00001166048,0.000073233816,0.0000181604,0.98988557,0.005024321,0.0029643506,0.0009601273,0.000006555918],"about_ca_topic_score_codex":0.014804382,"about_ca_topic_score_gemma":0.014869111,"teacher_disagreement_score":0.014804382,"about_ca_system_score_codex":0.00046424623,"about_ca_system_score_gemma":0.0007773001,"threshold_uncertainty_score":0.02943647},"labels":[],"label_agreement":null},{"id":"W4404355462","doi":"10.18280/ts.410516","title":"Pedestrian Re-Identification and Tracking Algorithm Based on Cross-Domain Adaptation","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian; Identification (biology); Tracking (education); Computer science; Adaptation (eye); Domain adaptation; Domain (mathematical analysis); Algorithm; Artificial intelligence; Computer vision; Mathematics; Engineering; Transport engineering; Psychology; Biology","score_opus":0.04167720896367055,"score_gpt":0.31605587823929254,"score_spread":0.274378669275622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404355462","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":[0.02264115,0.00028351942,0.9745462,0.000056531208,0.00014927918,0.000043856788,0.000029671335,0.0009047538,0.0013451036],"genre_scores_gemma":[0.45133653,0.0005999548,0.53175217,0.00028415595,0.00015539087,0.0001578906,0.00057128404,0.00021726126,0.014925429],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993357,0.000091171896,0.000027867765,0.0002715064,0.00019240443,0.00008128384],"domain_scores_gemma":[0.9993554,0.00009723251,0.00003986884,0.00013574246,0.00033300498,0.00003876268],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00090949825,0.00075748237,0.0013709312,0.0009236086,0.0005181173,0.00072284363,0.0011181479,0.0009801773,0.0015302743],"category_scores_gemma":[0.0013831345,0.00038613408,0.0007916586,0.0008724881,0.00032756306,0.0009230065,0.0010244977,0.0010716353,0.0016378345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00050303276,0.0002808008,0.0038112472,0.00007947461,0.0001900325,0.00018855366,0.00010981834,0.056415625,0.07456904,0.0029425218,0.004401933,0.85650784],"study_design_scores_gemma":[0.0000134796455,0.0000871354,0.0034154328,0.0000066861962,0.000051134306,0.0003292755,0.000025345154,0.97653234,0.016815502,0.00067345443,0.0020229036,0.000027282658],"about_ca_topic_score_codex":0.0029606349,"about_ca_topic_score_gemma":0.0033375875,"teacher_disagreement_score":0.0029606349,"about_ca_system_score_codex":0.0002959221,"about_ca_system_score_gemma":0.00067827804,"threshold_uncertainty_score":0.005886793},"labels":[],"label_agreement":null},{"id":"W4404387165","doi":"10.1016/j.patcog.2024.111169","title":"Learning data association for multi-object tracking using only coordinates","year":2024,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Data association; Computer vision; Association (psychology); Artificial intelligence; Object (grammar); Computer science; Tracking (education); Video tracking; Pattern recognition (psychology); Psychology","score_opus":0.21182501339409193,"score_gpt":0.3907927057223367,"score_spread":0.17896769232824478,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404387165","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009310715,0.00013109017,0.98663354,0.000050047514,0.0000322595,0.000038836235,0.00010124416,0.003337101,0.0003651846],"genre_scores_gemma":[0.395416,0.00026835577,0.5969132,0.00026767227,0.00009906511,0.00021534323,0.0021695222,0.0004783671,0.0041723982],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9980363,0.00020419671,0.000104872655,0.0009441965,0.0005091251,0.00020142012],"domain_scores_gemma":[0.9975648,0.00058548554,0.0002626133,0.0009730441,0.0004621128,0.00015204125],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002761164,0.0012993965,0.001962683,0.0015182262,0.00067938305,0.0018421182,0.0044949003,0.0015031112,0.0027080611],"category_scores_gemma":[0.0056832875,0.0011275407,0.0017105637,0.0024361021,0.0009711277,0.003733031,0.0045041167,0.0024768324,0.003223008],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00064440555,0.00040938062,0.012112958,0.00023349507,0.0002827719,0.0001543292,0.00024615775,0.1482471,0.02431844,0.009951413,0.0061140596,0.7972855],"study_design_scores_gemma":[0.000028251196,0.00014049691,0.0011339922,0.000010460143,0.000047734015,0.00016001883,0.00002694856,0.97358096,0.015795732,0.0061230846,0.0029328645,0.000019446525],"about_ca_topic_score_codex":0.0038440644,"about_ca_topic_score_gemma":0.0048495545,"teacher_disagreement_score":0.0044949003,"about_ca_system_score_codex":0.0010004506,"about_ca_system_score_gemma":0.001755658,"threshold_uncertainty_score":0.014602661},"labels":[],"label_agreement":null},{"id":"W4404387829","doi":"10.18280/ts.41051","title":"Pedestrian Re-Identification and Tracking Algorithm Based on Cross-Domain Adaptation","year":2024,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pedestrian; Identification (biology); Tracking (education); Computer science; Domain adaptation; Artificial intelligence; Adaptation (eye); Domain (mathematical analysis); Computer vision; Algorithm; Engineering; Mathematics; Psychology; Transport engineering","score_opus":0.04167720896367055,"score_gpt":0.31605587823929254,"score_spread":0.274378669275622,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404387829","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028887743,0.00017900973,0.96755844,0.00009119706,0.00007572582,0.000050539657,0.000030411937,0.0013217855,0.0018050873],"genre_scores_gemma":[0.7280423,0.00030968906,0.26067576,0.00032614087,0.00006341896,0.00010321535,0.00033212622,0.00013623077,0.010011182],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995147,0.00007057941,0.000017941305,0.00021965666,0.000108404434,0.000068700145],"domain_scores_gemma":[0.99952734,0.0000790452,0.00006648477,0.0001237018,0.00016194323,0.00004155581],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007917627,0.0006951725,0.00084234483,0.00067700003,0.000360719,0.00046804288,0.0012925995,0.0008239736,0.0012850349],"category_scores_gemma":[0.0014166152,0.00032857643,0.0007314849,0.00046254534,0.0005225908,0.00086371234,0.0011028685,0.0010666131,0.0010157214],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039027908,0.00019996185,0.0059254062,0.00006960825,0.00013226317,0.0003689373,0.00022178977,0.33908743,0.038080174,0.007081111,0.0055883336,0.6028547],"study_design_scores_gemma":[0.0000048029765,0.000031156236,0.0007011514,0.0000037088553,0.000012806227,0.00015384209,0.000013114244,0.99163544,0.0055985623,0.0009727595,0.00086192694,0.000010771732],"about_ca_topic_score_codex":0.0030205287,"about_ca_topic_score_gemma":0.002568387,"teacher_disagreement_score":0.0030205287,"about_ca_system_score_codex":0.00045324338,"about_ca_system_score_gemma":0.00054117665,"threshold_uncertainty_score":0.006005883},"labels":[],"label_agreement":null},{"id":"W4404563527","doi":"10.1007/978-3-031-73650-6_15","title":"Visual Relationship Transformation","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Transformation (genetics); Computer graphics (images); Artificial intelligence; Computer vision","score_opus":0.03189432167681258,"score_gpt":0.30936090393403537,"score_spread":0.27746658225722276,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404563527","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0062406324,0.0009582384,0.80575234,0.00037788606,0.00048395252,0.0003108139,0.0018497021,0.013381755,0.17064467],"genre_scores_gemma":[0.16890644,0.0023819534,0.5503333,0.0006081777,0.00019805344,0.00035162544,0.012028047,0.0053169252,0.2598755],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99963987,0.000033257715,0.000012745745,0.00015253999,0.00012229971,0.000039294646],"domain_scores_gemma":[0.9997987,0.00002009419,0.000010261386,0.00009332728,0.00005959216,0.000018094992],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023315498,0.0008964975,0.00039327348,0.0013876957,0.00049946696,0.002026091,0.0010487839,0.0005649129,0.077905074],"category_scores_gemma":[0.0008260963,0.00036835394,0.0006823429,0.0013325003,0.00041009547,0.0014453366,0.001591275,0.0010857551,0.036050245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00011850584,0.000070143244,0.00018372547,0.0001550466,0.000021199563,0.00008849409,0.00009648828,0.0023562463,0.043771073,0.044976678,0.048408955,0.85975343],"study_design_scores_gemma":[0.00004767686,0.00015812523,0.0016791085,0.00014024504,0.00006947164,0.0010446981,0.00036167342,0.08138917,0.121037215,0.064505905,0.72951204,0.00005460385],"about_ca_topic_score_codex":0.0027025188,"about_ca_topic_score_gemma":0.0026728522,"teacher_disagreement_score":0.077905074,"about_ca_system_score_codex":0.00046328426,"about_ca_system_score_gemma":0.00052021444,"threshold_uncertainty_score":0.26061845},"labels":[],"label_agreement":null},{"id":"W4404614534","doi":"10.1016/b978-0-44-324770-5.00014-3","title":"Detecting moving objects with machine learning","year":2024,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Herzberg Institute of Astrophysics","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Computer vision","score_opus":0.01689417901871552,"score_gpt":0.24884554444300108,"score_spread":0.23195136542428557,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404614534","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":[0.0024988933,0.017206598,0.90544116,0.00069807994,0.00096194213,0.00005274605,0.00051642134,0.0060051302,0.066619106],"genre_scores_gemma":[0.035436437,0.02043986,0.6230634,0.0006326062,0.0009892758,0.000111717556,0.002475351,0.0013307357,0.31552067],"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99964714,0.000035012952,0.000017717763,0.00010710306,0.00017419759,0.000018702662],"domain_scores_gemma":[0.99961215,0.00019531568,0.000020828227,0.00006535166,0.00009137471,0.000014964729],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00045810718,0.0013075904,0.0008888105,0.0017664303,0.00023705054,0.0019175146,0.0012114852,0.0012065087,0.030674923],"category_scores_gemma":[0.0010509709,0.00062248914,0.00055283366,0.0023483427,0.000406477,0.0021401718,0.0007407692,0.0011295581,0.027236843],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016993163,0.00003484866,0.00015229822,0.00020199262,0.000022498612,0.000026678987,0.000021929718,0.0059500122,0.0044481247,0.0056697037,0.051288277,0.9321666],"study_design_scores_gemma":[0.0000127372095,0.000106892134,0.002461792,0.00046425537,0.00009347532,0.0008414375,0.00008750613,0.44250214,0.023842754,0.079590455,0.44992226,0.00007429418],"about_ca_topic_score_codex":0.0017951406,"about_ca_topic_score_gemma":0.002554787,"teacher_disagreement_score":0.030674923,"about_ca_system_score_codex":0.00038090502,"about_ca_system_score_gemma":0.00030872985,"threshold_uncertainty_score":0.10261786},"labels":[],"label_agreement":null},{"id":"W4404770784","doi":"10.1016/j.neunet.2024.106948","title":"TENet: Targetness entanglement incorporating with multi-scale pooling and mutually-guided fusion for RGB-E object tracking","year":2024,"lang":"en","type":"article","venue":"Neural Networks","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Pooling; Artificial intelligence; Computer science; Scale (ratio); Object (grammar); Computer vision; Tracking (education); Fusion; Quantum entanglement; RGB color model; Video tracking; Pattern recognition (psychology); Geography; Psychology; Cartography; Physics; Philosophy","score_opus":0.036548053792754505,"score_gpt":0.30582021933906234,"score_spread":0.26927216554630784,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4404770784","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":[0.015126351,0.00040793425,0.97984195,0.00018375923,0.0001213456,0.00005867884,0.00019381985,0.001761262,0.0023049738],"genre_scores_gemma":[0.43532324,0.00042325427,0.55112034,0.00060145033,0.00016919468,0.00017775776,0.0011304333,0.00043833206,0.010615978],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99953103,0.000085917716,0.000022142054,0.00014235708,0.0001364893,0.000082103514],"domain_scores_gemma":[0.99962974,0.00009555685,0.00003579802,0.00010406901,0.00010070346,0.000034167148],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015223817,0.00110796,0.0011290486,0.0006422609,0.00067148195,0.001122058,0.002237244,0.0015988864,0.00295418],"category_scores_gemma":[0.0021006465,0.0005031972,0.0010587822,0.0009840586,0.00080021896,0.0024148033,0.0031417874,0.0014360598,0.000811577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006514399,0.00025608868,0.001419897,0.00017179179,0.00037141528,0.00019715777,0.0001765702,0.23199919,0.03205075,0.028687684,0.014854671,0.6891634],"study_design_scores_gemma":[0.000009920901,0.000044484364,0.0002667062,0.000009365094,0.000026044088,0.000039519015,0.000011892236,0.9845168,0.0060212547,0.007676225,0.0013639439,0.000013815695],"about_ca_topic_score_codex":0.0062407977,"about_ca_topic_score_gemma":0.008900402,"teacher_disagreement_score":0.0062407977,"about_ca_system_score_codex":0.0007626597,"about_ca_system_score_gemma":0.0011582943,"threshold_uncertainty_score":0.012408912},"labels":[],"label_agreement":null},{"id":"W4405022500","doi":"10.1007/s11760-024-03713-0","title":"DMTrack: learning deformable masked visual representations for single object tracking","year":2024,"lang":"en","type":"article","venue":"Signal Image and Video Processing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer vision; Object (grammar); Artificial intelligence; Tracking (education); Eye tracking; Computer science; Video tracking; Communication; Psychology","score_opus":0.03362111510912802,"score_gpt":0.34891554708914385,"score_spread":0.31529443198001583,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405022500","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0070881518,0.0002490903,0.9837458,0.000082742416,0.000094687486,0.000095031166,0.0005492934,0.007579465,0.0005156622],"genre_scores_gemma":[0.11951301,0.00037986838,0.86632055,0.0003515732,0.0000855909,0.00032319388,0.0047143986,0.0009795416,0.00733222],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993556,0.00007695967,0.00002359031,0.00027736853,0.0001880171,0.000078465884],"domain_scores_gemma":[0.9991848,0.00028122094,0.00006505676,0.00024847762,0.00014785225,0.00007259752],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011874269,0.0016758811,0.00148221,0.0011822387,0.0004287276,0.0010977723,0.0028694936,0.002293014,0.0057326383],"category_scores_gemma":[0.0033795568,0.0008988613,0.0013103249,0.0012786265,0.00050116854,0.0015670052,0.0023345284,0.0021003804,0.0036398426],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00058014685,0.00020567492,0.0007189537,0.00013693457,0.000182414,0.00010691431,0.00005207825,0.080698445,0.025223617,0.0035569668,0.017418442,0.8711193],"study_design_scores_gemma":[0.000033107004,0.000085647625,0.00021262011,0.000009786524,0.000017192731,0.0000524535,0.000009110254,0.98744875,0.0069905156,0.003102298,0.0020265437,0.000012001866],"about_ca_topic_score_codex":0.007935032,"about_ca_topic_score_gemma":0.010795301,"teacher_disagreement_score":0.007935032,"about_ca_system_score_codex":0.00070494454,"about_ca_system_score_gemma":0.0012280828,"threshold_uncertainty_score":0.019177556},"labels":[],"label_agreement":null},{"id":"W4405936960","doi":"10.1109/iccais63750.2024.10814505","title":"3D Multi-Object Tracking Employing MS-GLMB Filter for Autonomous Driving","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Regina","funders":"","keywords":"Computer science; Tracking (education); Artificial intelligence; Computer vision; Filter (signal processing); Object (grammar); Psychology","score_opus":0.06190721451015047,"score_gpt":0.3417801232540114,"score_spread":0.27987290874386095,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405936960","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.03471633,0.0009873651,0.94322807,0.0002182124,0.00016479376,0.00010171645,0.0029964214,0.014825966,0.0027611307],"genre_scores_gemma":[0.2990821,0.0006251116,0.67510134,0.00024784563,0.00008344174,0.00017406995,0.019388879,0.0009842027,0.004313081],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994404,0.000041950803,0.00002073565,0.00018161286,0.00023532237,0.0000800067],"domain_scores_gemma":[0.99965715,0.000048649563,0.00002684896,0.00010579689,0.00013635222,0.000025280253],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007231392,0.0010946154,0.0010366007,0.0019664855,0.0005309221,0.0011415776,0.0015268073,0.0010566843,0.0021328349],"category_scores_gemma":[0.0013076377,0.0005938901,0.0010677091,0.0015235955,0.00026585287,0.0009420086,0.0012118737,0.0011234323,0.0017034782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004806445,0.00024063482,0.005578116,0.000316952,0.00033841873,0.00019947045,0.00019782135,0.12978108,0.035834897,0.003208795,0.0306174,0.7932058],"study_design_scores_gemma":[0.000021771915,0.000030734845,0.0024304176,0.000028924747,0.000025394304,0.00012598126,0.000033541648,0.9759266,0.010151465,0.0018539286,0.009341299,0.000029989795],"about_ca_topic_score_codex":0.030347457,"about_ca_topic_score_gemma":0.04800441,"teacher_disagreement_score":0.030347457,"about_ca_system_score_codex":0.000694696,"about_ca_system_score_gemma":0.0014273308,"threshold_uncertainty_score":0.060341656},"labels":[],"label_agreement":null},{"id":"W4405949737","doi":"10.1007/s12652-024-04895-8","title":"IoT-based human detection for fast earthquake response and rescue","year":2024,"lang":"en","type":"article","venue":"Journal of Ambient Intelligence and Humanized Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"Tamilnadu State Council For Science And Technology","keywords":"Computer science; Search and rescue; Drone; Frame (networking); Focus (optics); Property (philosophy); Artificial intelligence; Cloud computing; Situation awareness; ALARM; Computational intelligence; Rescue robot; Real-time computing; Internet of Things; Robot; Computer security; Mobile robot; Computer network","score_opus":0.04617718621210105,"score_gpt":0.3366498247394884,"score_spread":0.29047263852738736,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4405949737","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.39738065,0.0018546642,0.57439375,0.00043290594,0.0010964811,0.0001494641,0.0015068358,0.0028205237,0.020364733],"genre_scores_gemma":[0.95013565,0.00041193006,0.043832712,0.00016216324,0.00011690878,0.000056886827,0.0005969149,0.000045426594,0.0046414183],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99987185,0.000021603644,0.0000073730794,0.000027431699,0.000047940248,0.00002382571],"domain_scores_gemma":[0.99985456,0.00004153086,0.000020087495,0.000016107142,0.000050632716,0.000016956486],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016384906,0.00042834808,0.00039036453,0.00070118404,0.00021938598,0.00041333877,0.00030136044,0.0004987671,0.0019152837],"category_scores_gemma":[0.00037971872,0.00015635075,0.0002788744,0.0004144342,0.00012701904,0.00044335285,0.0005177398,0.00024516415,0.00075065123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0015963026,0.000500187,0.03197993,0.0005168323,0.00019270027,0.0011719557,0.00022491733,0.03331236,0.23690373,0.004422204,0.020288134,0.6688907],"study_design_scores_gemma":[0.00005281302,0.000506237,0.034490097,0.00006415264,0.00011438714,0.0008666788,0.0002950892,0.89971054,0.04957583,0.004429951,0.00984037,0.00005386442],"about_ca_topic_score_codex":0.00064416276,"about_ca_topic_score_gemma":0.0015163227,"teacher_disagreement_score":0.0019152837,"about_ca_system_score_codex":0.00010220848,"about_ca_system_score_gemma":0.00019646794,"threshold_uncertainty_score":0.006407261},"labels":[],"label_agreement":null},{"id":"W4406016098","doi":"10.18280/ijsse.140611","title":"An Efficient and Robust Night-Time Surveillance Object Detection System Using YOLOv8 and High-Performance Computing","year":2024,"lang":"en","type":"article","venue":"International Journal of Safety and Security Engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer science; Real-time computing; Computer security","score_opus":0.007134385154417264,"score_gpt":0.23205745210339812,"score_spread":0.22492306694898084,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406016098","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":[0.36209184,0.0014699505,0.60758394,0.00030729658,0.0005378699,0.0003222754,0.00054711156,0.019332025,0.007807612],"genre_scores_gemma":[0.599548,0.000374736,0.384686,0.00027874266,0.00011446222,0.00020153931,0.0017019482,0.00029210144,0.01280251],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99976534,0.000019402616,0.000013147763,0.00005866592,0.00009875032,0.00004472207],"domain_scores_gemma":[0.9998037,0.000019443929,0.000018202116,0.000025128908,0.00010221904,0.000031251373],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023232306,0.000546696,0.0007794077,0.00063924474,0.0004603175,0.00049397495,0.0010427585,0.00041743135,0.0027926897],"category_scores_gemma":[0.00035095564,0.00029945554,0.0003048896,0.00040039804,0.00012278694,0.00064703217,0.00065174414,0.00036603885,0.0014309414],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0012489195,0.00032858146,0.006057583,0.00021256348,0.000117382886,0.00030479432,0.00013819482,0.0057230582,0.4938846,0.0012709862,0.011546783,0.47916654],"study_design_scores_gemma":[0.0003114473,0.0013894642,0.012132592,0.00004180114,0.00022262604,0.0008447361,0.00014618725,0.60599726,0.33979946,0.00096220965,0.037997715,0.0001544005],"about_ca_topic_score_codex":0.002275038,"about_ca_topic_score_gemma":0.00431394,"teacher_disagreement_score":0.0027926897,"about_ca_system_score_codex":0.00031483153,"about_ca_system_score_gemma":0.00082103995,"threshold_uncertainty_score":0.009342492},"labels":[],"label_agreement":null},{"id":"W4406110370","doi":"10.1109/tmc.2025.3526573","title":"FastTuner: Fast Resolution and Model Tuning for Multi-Object Tracking in Edge Video Analytics","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Mobile Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"China Scholarship Council","keywords":"Computer science; Analytics; Video tracking; Enhanced Data Rates for GSM Evolution; Tracking (education); Object (grammar); Computer vision; Artificial intelligence; Data mining","score_opus":0.05133420868989664,"score_gpt":0.3437616184360394,"score_spread":0.2924274097461428,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406110370","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":[0.02858255,0.0005879021,0.9537627,0.00016237018,0.00008918032,0.0000993479,0.00014831111,0.01517761,0.0013900283],"genre_scores_gemma":[0.48594254,0.00041101806,0.5086015,0.000391432,0.000062964726,0.00021705923,0.00088452076,0.0014315182,0.0020574904],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994318,0.00007721727,0.000023272552,0.00020457157,0.00018109627,0.00008203556],"domain_scores_gemma":[0.9988305,0.0005034449,0.00010646414,0.00029171217,0.00017770077,0.000090267786],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0013036552,0.00142151,0.0009768277,0.0007580897,0.00060805475,0.0011898776,0.002310881,0.001151902,0.002124284],"category_scores_gemma":[0.006132001,0.0006657928,0.00057723944,0.0006507505,0.00054963294,0.0024300108,0.0019599725,0.0021511775,0.0012294088],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004881906,0.0004531034,0.004646835,0.00020657052,0.00013221105,0.00023101771,0.00029241125,0.45655358,0.03679041,0.0042048274,0.013113883,0.48288697],"study_design_scores_gemma":[0.000013785778,0.000030264959,0.00026520694,0.000006413771,0.000005238513,0.000034038745,0.000015766645,0.9932827,0.0043401746,0.0011593977,0.00083624787,0.000010770247],"about_ca_topic_score_codex":0.0072649443,"about_ca_topic_score_gemma":0.009256356,"teacher_disagreement_score":0.0072649443,"about_ca_system_score_codex":0.00070001493,"about_ca_system_score_gemma":0.0012613359,"threshold_uncertainty_score":0.014445305},"labels":[],"label_agreement":null},{"id":"W4406403737","doi":"10.1007/s40747-024-01776-7","title":"View adaptive multi-object tracking method based on depth relationship cues","year":2025,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Petroleum Technology Research Centre; Guizhou Science and Technology Department; National Natural Science Foundation of China","keywords":"Computational intelligence; Computer vision; Artificial intelligence; Object (grammar); Tracking (education); Computer science; Sensory cue; Video tracking; Psychology","score_opus":0.19457053506285454,"score_gpt":0.40051535518522097,"score_spread":0.20594482012236642,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406403737","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":[0.016823327,0.000546504,0.9801822,0.00006104841,0.00007883212,0.00005177782,0.00011737643,0.0011512466,0.0009877316],"genre_scores_gemma":[0.22355428,0.00076067436,0.76944846,0.00018259058,0.00010378378,0.000105442174,0.0012004145,0.0002191996,0.004425121],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991598,0.00006666446,0.00004177178,0.0003159895,0.00032522727,0.00009053588],"domain_scores_gemma":[0.99938214,0.00011362041,0.00006189394,0.00014378986,0.00023522248,0.000063249296],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00069220486,0.00090128486,0.0011402213,0.001589489,0.0005030327,0.0010664226,0.0017125672,0.0008155082,0.0014983062],"category_scores_gemma":[0.0014188571,0.00050016347,0.0011115845,0.001213122,0.00023021878,0.0015142441,0.0014036295,0.0010792008,0.0009649581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003172409,0.00010558682,0.0025324507,0.00010340033,0.00011725778,0.00008869098,0.00014374153,0.025219692,0.056707498,0.002192523,0.0030129433,0.90945894],"study_design_scores_gemma":[0.00003892273,0.00019707847,0.0032427385,0.000023103263,0.000100535704,0.00048026297,0.00007282272,0.94862884,0.039139,0.0018358307,0.0061940267,0.00004680296],"about_ca_topic_score_codex":0.0035900478,"about_ca_topic_score_gemma":0.0053260457,"teacher_disagreement_score":0.0035900478,"about_ca_system_score_codex":0.00048418355,"about_ca_system_score_gemma":0.0008400781,"threshold_uncertainty_score":0.007138312},"labels":[],"label_agreement":null},{"id":"W4406616011","doi":"10.1016/j.patcog.2025.111356","title":"Optimizing domain-generalizable ReID through non-parametric normalization","year":2025,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Alliance de recherche numérique du Canada","keywords":"Normalization (sociology); Computer science; Artificial intelligence; Parametric statistics; Domain (mathematical analysis); Pattern recognition (psychology); Algorithm; Mathematics; Statistics","score_opus":0.032902615247862886,"score_gpt":0.2982469833051253,"score_spread":0.2653443680572624,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406616011","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":[0.031681273,0.00076342287,0.953487,0.0004578649,0.000118424134,0.00009607809,0.0005084198,0.00807847,0.004808977],"genre_scores_gemma":[0.54821444,0.00094333634,0.4266176,0.0013726982,0.0001291594,0.00028311426,0.0033014687,0.0017582427,0.017380023],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99886084,0.00022821623,0.000044726377,0.00053324894,0.00019693085,0.00013600916],"domain_scores_gemma":[0.9985696,0.00036700178,0.00014037758,0.0006168134,0.00024061633,0.0000656222],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025162103,0.0022408362,0.0014898132,0.00088068045,0.0006340028,0.0013641118,0.0036119286,0.001880489,0.0037078501],"category_scores_gemma":[0.0065659974,0.00076871435,0.0018679348,0.0012733897,0.0014197574,0.004029828,0.003183759,0.003251539,0.002709539],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00014190684,0.00012290411,0.0020885796,0.00014602573,0.00013779395,0.00014863853,0.000109307955,0.6476802,0.008431582,0.0102189295,0.011362821,0.31941125],"study_design_scores_gemma":[0.000009473911,0.000042347267,0.00035994526,0.00001749052,0.000024826015,0.00010874098,0.00004242601,0.9793276,0.005610463,0.012281451,0.002156647,0.000018551564],"about_ca_topic_score_codex":0.007373943,"about_ca_topic_score_gemma":0.010720926,"teacher_disagreement_score":0.007373943,"about_ca_system_score_codex":0.0015862263,"about_ca_system_score_gemma":0.001605579,"threshold_uncertainty_score":0.014662027},"labels":[],"label_agreement":null},{"id":"W4406812170","doi":"10.1109/lra.2025.3533457","title":"3DGS-CD: 3D Gaussian Splatting-Based Change Detection for Physical Object Rearrangement","year":2025,"lang":"en","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Reach Technologies (Canada)","funders":"MIT Portugal","keywords":"Computer science; Object (grammar); Gaussian; Artificial intelligence; Computer vision; Physics","score_opus":0.027433489322985227,"score_gpt":0.2986896547707165,"score_spread":0.2712561654477313,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406812170","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.025972238,0.0003114557,0.96275306,0.00011332042,0.00006330891,0.00009705769,0.00049811817,0.008465191,0.0017262846],"genre_scores_gemma":[0.18952127,0.00030334335,0.80371404,0.00018927209,0.000044413497,0.00009575099,0.0022851885,0.001073733,0.002772998],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99913764,0.00006392876,0.000028911702,0.00017441376,0.0005053919,0.00008972932],"domain_scores_gemma":[0.99925584,0.00012300105,0.000079980564,0.00022609705,0.00023794404,0.000077174234],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005432081,0.0010612337,0.0007889438,0.002448931,0.0005168018,0.0014134854,0.0017167878,0.0010154397,0.0029203421],"category_scores_gemma":[0.0014128454,0.00071675784,0.0009169207,0.0014333315,0.0010048097,0.0014501964,0.002400141,0.0009918186,0.0015291666],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045318322,0.00013700064,0.008723627,0.00027292964,0.00014926848,0.00037582126,0.0004269507,0.03874222,0.17852451,0.0064271493,0.0137758665,0.75199145],"study_design_scores_gemma":[0.000039631115,0.00012982862,0.0077028307,0.0000313972,0.000057057976,0.00072508503,0.00014278616,0.8027841,0.1586199,0.004178908,0.025476845,0.00011164317],"about_ca_topic_score_codex":0.008380061,"about_ca_topic_score_gemma":0.013684618,"teacher_disagreement_score":0.008380061,"about_ca_system_score_codex":0.0009297396,"about_ca_system_score_gemma":0.0010721193,"threshold_uncertainty_score":0.016662538},"labels":[],"label_agreement":null},{"id":"W4406825864","doi":"10.1145/3711861","title":"Similarity Regulation and Calibration Alignment for Weakly Supervised Text-Based Person Re-Identification","year":2025,"lang":"en","type":"article","venue":"ACM Transactions on Multimedia Computing Communications and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Government of Jiangsu Province; Six Talent Peaks Project in Jiangsu Province; China University of Mining and Technology; National Natural Science Foundation of China","keywords":"Computer science; Similarity (geometry); Identification (biology); Artificial intelligence; Calibration; Natural language processing; Information retrieval; Pattern recognition (psychology); Machine learning; Data mining; Image (mathematics); Statistics; Mathematics","score_opus":0.04686502359457606,"score_gpt":0.32761663172264605,"score_spread":0.28075160812807,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406825864","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016193425,0.000255977,0.97936475,0.00013430584,0.000070550064,0.00007780434,0.00011258697,0.0024154205,0.0013752509],"genre_scores_gemma":[0.41183674,0.00041928302,0.573047,0.0006455366,0.00023459074,0.0003547786,0.0015244127,0.00069397525,0.011243696],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99705553,0.0007311192,0.00011812049,0.0012544087,0.00063349475,0.00020733067],"domain_scores_gemma":[0.9976121,0.00059137476,0.00029165656,0.0007804033,0.0006001499,0.00012446138],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0017195115,0.0013068053,0.0012815057,0.0010356831,0.0008235925,0.00088203134,0.0024774217,0.0015840085,0.003638871],"category_scores_gemma":[0.005337825,0.0004994441,0.0012761647,0.0011229712,0.0010492261,0.0027619714,0.002422628,0.0021801582,0.0037674233],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005806602,0.00033971152,0.0014203097,0.00024816496,0.000111972106,0.00026487635,0.00054450997,0.04914937,0.068765745,0.00862653,0.008645523,0.86130255],"study_design_scores_gemma":[0.000027781312,0.00013658605,0.001236837,0.00002207244,0.000054046057,0.00036769442,0.00016715271,0.9359793,0.043278713,0.013310966,0.0053561456,0.00006259201],"about_ca_topic_score_codex":0.0026423992,"about_ca_topic_score_gemma":0.0032525687,"teacher_disagreement_score":0.003638871,"about_ca_system_score_codex":0.0006531588,"about_ca_system_score_gemma":0.0011407862,"threshold_uncertainty_score":0.012173235},"labels":[],"label_agreement":null},{"id":"W4406858424","doi":"10.1109/apsipaasc63619.2025.10849276","title":"Confidence-Aware Learning for Person Re-identification with Noisy Labels","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Identification (biology); Artificial intelligence; Natural language processing; Machine learning","score_opus":0.04887065991874492,"score_gpt":0.3203677311272856,"score_spread":0.2714970712085407,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406858424","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.032286383,0.00043900937,0.9642562,0.00024690913,0.00006869737,0.00005415634,0.000115790644,0.001714353,0.0008185267],"genre_scores_gemma":[0.5471322,0.00035448564,0.44741744,0.0004896791,0.00020217506,0.000111354886,0.0009758629,0.00038924665,0.0029275552],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99615175,0.0010328937,0.00020134811,0.001370931,0.0009136734,0.00032937067],"domain_scores_gemma":[0.991655,0.0033543971,0.00088611373,0.0020504599,0.0017256207,0.0003283742],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004415799,0.0012956976,0.0020425713,0.0016217722,0.00093928224,0.0015487005,0.002959214,0.002005991,0.0011997427],"category_scores_gemma":[0.019359166,0.00065713614,0.0010029959,0.0015008786,0.0010662961,0.003419771,0.0028556038,0.0033174339,0.0014973596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00072308717,0.0005161451,0.016334336,0.00022531403,0.00024623444,0.00033983012,0.00095818896,0.16342326,0.0194839,0.007086313,0.0086849695,0.7819784],"study_design_scores_gemma":[0.000013050777,0.00009095332,0.0018195141,0.00002541499,0.00003582793,0.00019967518,0.0001399883,0.9764786,0.009776912,0.009541695,0.0018397378,0.00003867839],"about_ca_topic_score_codex":0.0035936811,"about_ca_topic_score_gemma":0.0047554686,"teacher_disagreement_score":0.004415799,"about_ca_system_score_codex":0.00080148,"about_ca_system_score_gemma":0.0009460033,"threshold_uncertainty_score":0.023353219},"labels":[],"label_agreement":null},{"id":"W4406864303","doi":"10.1016/j.neucom.2025.129534","title":"ATPTrack: Visual tracking with alternating token pruning of dynamic templates and search region","year":2025,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Hong Kong Polytechnic University; Columbus State University","keywords":"Security token; Pruning; Computer science; Template; Eye tracking; Tracking (education); Artificial intelligence; Pattern recognition (psychology); Computer vision; Biology; Computer network","score_opus":0.01676145472659725,"score_gpt":0.31719636601607304,"score_spread":0.3004349112894758,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4406864303","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004586589,0.00019679568,0.9885097,0.000054197008,0.000080636564,0.000068940804,0.00021400304,0.0052419677,0.0010472018],"genre_scores_gemma":[0.09506709,0.00016107316,0.8970341,0.00013998362,0.00006531241,0.00020687607,0.0013917716,0.0010972095,0.004836623],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9990067,0.00012741322,0.00007000123,0.0003276669,0.00033897048,0.00012924158],"domain_scores_gemma":[0.99889743,0.0003850523,0.00007789197,0.00033329998,0.00021595613,0.000090331436],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009805887,0.0011619123,0.0015995068,0.0015178656,0.00076692685,0.0020285163,0.003808912,0.0016986949,0.00745682],"category_scores_gemma":[0.003858555,0.00077360234,0.0009217414,0.0019249868,0.00064023526,0.0026225646,0.0027963452,0.0014140881,0.003033787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007197533,0.00015733,0.00072401,0.00015741613,0.00009925158,0.00015831721,0.0000862594,0.036332127,0.019540783,0.011489223,0.014110955,0.9164246],"study_design_scores_gemma":[0.00011020697,0.00013534633,0.00035992658,0.00002747971,0.00006229348,0.00025953195,0.000038576643,0.9523841,0.023744997,0.013199297,0.009650018,0.000028149167],"about_ca_topic_score_codex":0.007781354,"about_ca_topic_score_gemma":0.012088871,"teacher_disagreement_score":0.007781354,"about_ca_system_score_codex":0.0006979576,"about_ca_system_score_gemma":0.0023518754,"threshold_uncertainty_score":0.024945498},"labels":[],"label_agreement":null},{"id":"W4407130634","doi":"10.1109/aivrv63595.2024.10860194","title":"Review of VREED in Eye Tracking-Based VR Emotion Detection","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Eye tracking; Emotion detection; Human–computer interaction; Computer vision; Artificial intelligence; Emotion recognition","score_opus":0.02904701518338827,"score_gpt":0.3398462460010775,"score_spread":0.31079923081768923,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407130634","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":[0.0018450401,0.9840305,0.009373931,0.0008368366,0.0005778709,0.00005556437,0.00019490658,0.00010833483,0.0029769985],"genre_scores_gemma":[0.018365972,0.9650676,0.010841385,0.0010142395,0.0011398037,0.00013810855,0.00072734134,0.00007771021,0.0026279653],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99866736,0.0003677812,0.00019102615,0.0003966278,0.00032089496,0.00005632862],"domain_scores_gemma":[0.99135333,0.0061890683,0.000372206,0.00026339196,0.0016961234,0.00012587995],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00365836,0.0011034933,0.0013777596,0.0026881068,0.00032291334,0.0022915397,0.0016860001,0.0013416032,0.0039571323],"category_scores_gemma":[0.010245381,0.0006795304,0.0016099863,0.0029455295,0.0005161483,0.001669371,0.000781043,0.0011360586,0.0019490859],"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":[0.00018022576,0.00006874665,0.0011528505,0.012801317,0.00033771648,0.000055669443,0.000117098,0.00095391454,0.0016535458,0.0017949634,0.014443246,0.96644074],"study_design_scores_gemma":[0.00008840181,0.0011047651,0.019534763,0.02275768,0.0022964352,0.0019428583,0.0005168394,0.009331396,0.009201443,0.005095918,0.9278411,0.00028830487],"about_ca_topic_score_codex":0.0052487487,"about_ca_topic_score_gemma":0.004472321,"teacher_disagreement_score":0.0052487487,"about_ca_system_score_codex":0.00088051776,"about_ca_system_score_gemma":0.0012935855,"threshold_uncertainty_score":0.01934743},"labels":[],"label_agreement":null},{"id":"W4407385589","doi":"10.3390/jimaging11020052","title":"SAVE: Self-Attention on Visual Embedding for Zero-Shot Generic Object Counting","year":2025,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University; Université TÉLUQ; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Object (grammar); Embedding; Shot (pellet); Computer vision; Feature (linguistics); Class (philosophy); Pattern recognition (psychology); Zero (linguistics); Matching (statistics); Mathematics; Statistics","score_opus":0.02050528559219349,"score_gpt":0.35715748667030855,"score_spread":0.33665220107811505,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407385589","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":[0.04426664,0.0008858927,0.93574065,0.00016872154,0.00023784973,0.00017850687,0.00062981277,0.015317026,0.0025748822],"genre_scores_gemma":[0.5149858,0.0007030443,0.46456942,0.0005248217,0.00016010615,0.00021872806,0.0041972864,0.0013208098,0.013319954],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.999255,0.000107636704,0.000030407906,0.0002988794,0.00020238974,0.00010567408],"domain_scores_gemma":[0.99920577,0.00020779484,0.000080045604,0.00026857283,0.00018029906,0.0000575479],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00071390823,0.0018029783,0.0014674669,0.0013967069,0.00038456786,0.0011934265,0.0026637071,0.0012227276,0.0048194327],"category_scores_gemma":[0.002450347,0.0005073098,0.00091034966,0.0010157761,0.0007899891,0.0027987051,0.0026501857,0.0012994664,0.0020515546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00032806044,0.00025653126,0.0011611696,0.00020521287,0.00009303377,0.00010376767,0.000116660216,0.027498662,0.025592677,0.0045814845,0.012179193,0.9278835],"study_design_scores_gemma":[0.000032037555,0.00020611717,0.0010596482,0.00003213529,0.000044784156,0.00021308311,0.00006813747,0.95736104,0.0255222,0.009703268,0.005725354,0.000032217406],"about_ca_topic_score_codex":0.0065347003,"about_ca_topic_score_gemma":0.008498231,"teacher_disagreement_score":0.0065347003,"about_ca_system_score_codex":0.00080271857,"about_ca_system_score_gemma":0.00087786425,"threshold_uncertainty_score":0.01612258},"labels":[],"label_agreement":null},{"id":"W4407575086","doi":"10.1109/tifs.2025.3541969","title":"Adaptive Generation of Privileged Intermediate Information for Visible-Infrared Person Re-Identification","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Information Forensics and Security","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Polytechnique Montréal","funders":"","keywords":"Computer science; Identification (biology); Infrared; Artificial intelligence; Optics; Physics","score_opus":0.027943136538506403,"score_gpt":0.2756919817106899,"score_spread":0.2477488451721835,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407575086","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":[0.07371737,0.00021039342,0.91961443,0.00025317082,0.00006833693,0.00008811718,0.00021635446,0.0025162697,0.003315496],"genre_scores_gemma":[0.72694814,0.00016697869,0.2663836,0.00034754333,0.000037331465,0.00014521714,0.0013651702,0.00024109085,0.0043649296],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992035,0.00021267582,0.000025368574,0.00026693873,0.00014747381,0.00014409344],"domain_scores_gemma":[0.99883336,0.00039652165,0.00009485901,0.000430609,0.0001746439,0.00006989304],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014803269,0.0008001751,0.00072739157,0.0006538467,0.0005428005,0.0007921582,0.002456593,0.0013220394,0.0029646775],"category_scores_gemma":[0.0041975155,0.0005748135,0.0010224808,0.00052084285,0.0009675131,0.0028690202,0.0033678608,0.0022702115,0.0014217707],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006910486,0.00072065287,0.006755342,0.00012734358,0.00012074046,0.00030603,0.0005818777,0.30998042,0.036415584,0.02199424,0.009494025,0.6128127],"study_design_scores_gemma":[0.000013285115,0.0001250439,0.0009135852,0.000019884938,0.000018059725,0.000113028516,0.00007560363,0.9586206,0.026510326,0.010713518,0.0028537696,0.000023341887],"about_ca_topic_score_codex":0.0018170512,"about_ca_topic_score_gemma":0.0025604812,"teacher_disagreement_score":0.0029646775,"about_ca_system_score_codex":0.0005999071,"about_ca_system_score_gemma":0.00087408157,"threshold_uncertainty_score":0.009917855},"labels":[],"label_agreement":null},{"id":"W4407742664","doi":"10.1007/s00530-025-01722-8","title":"Multi-object tracking with scale-aware transformer and enhanced association strategy","year":2025,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Computer science; Cryptography; Transformer; Scale (ratio); Tracking (education); Artificial intelligence; Computer vision; Computer security; Engineering; Cartography; Electrical engineering; Geography","score_opus":0.018567057021270195,"score_gpt":0.28420193188756515,"score_spread":0.26563487486629495,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4407742664","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00599833,0.0001262624,0.9930508,0.000028298917,0.000022590348,0.000010846403,0.000011802415,0.00017869848,0.00057231745],"genre_scores_gemma":[0.46504557,0.00058971235,0.52945316,0.0001215028,0.00007779028,0.00006494881,0.00015989714,0.00013536932,0.0043521663],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99941957,0.000102408245,0.00003130784,0.00017401448,0.00021144697,0.000061234205],"domain_scores_gemma":[0.999474,0.00013643142,0.00006275159,0.00011688209,0.00016876073,0.000041180356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00079942663,0.00064598315,0.00088831346,0.000729867,0.00042680817,0.0009109596,0.0010861203,0.0006847046,0.0012052779],"category_scores_gemma":[0.0017329917,0.0003899747,0.0006867301,0.001249604,0.00043205798,0.0016674268,0.0014087335,0.00072124,0.00074364437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00060903927,0.00019589404,0.002466215,0.00015741805,0.00016607673,0.00019210894,0.00019204136,0.10373986,0.12938067,0.023126828,0.0023585104,0.7374154],"study_design_scores_gemma":[0.000019418587,0.000090344314,0.0007862751,0.0000067356914,0.000056687193,0.0003501523,0.000023048358,0.9716224,0.020704767,0.0044307006,0.0018880034,0.000021433743],"about_ca_topic_score_codex":0.0019449652,"about_ca_topic_score_gemma":0.0019942124,"teacher_disagreement_score":0.0019449652,"about_ca_system_score_codex":0.00039643134,"about_ca_system_score_gemma":0.0007027793,"threshold_uncertainty_score":0.0042278767},"labels":[],"label_agreement":null},{"id":"W4408062962","doi":"10.5220/0013153500003905","title":"Silhouette Segmentation for Near-Fall Detection Through Analysis of Human Movements in Surveillance Videos","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Silhouette; Segmentation; Computer vision; Artificial intelligence; Computer science; Image segmentation","score_opus":0.02555296787024709,"score_gpt":0.3525611330323719,"score_spread":0.32700816516212483,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408062962","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1782889,0.0013721405,0.812873,0.00013154715,0.00019506892,0.00022389196,0.0010445481,0.0031532664,0.002717635],"genre_scores_gemma":[0.69856066,0.0011610942,0.29239658,0.00010364898,0.00015980558,0.00011946261,0.0024322486,0.00038198713,0.004684565],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995117,0.000042017706,0.000021933121,0.00016711968,0.00016312089,0.00009410447],"domain_scores_gemma":[0.9995833,0.00009961771,0.00006180552,0.000043227603,0.000159209,0.000052845502],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00040290153,0.0009766212,0.0010914281,0.0034031041,0.00049634883,0.00090576237,0.00077893987,0.0007484051,0.0018261194],"category_scores_gemma":[0.0011435786,0.00040194055,0.000630524,0.0015249387,0.00031940936,0.0005981449,0.0005552079,0.0005481016,0.001296102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00107346,0.0002814648,0.012505224,0.00037509587,0.00014851091,0.0006181131,0.0003519562,0.023377968,0.2057119,0.0012983697,0.0078561455,0.7464018],"study_design_scores_gemma":[0.000020492073,0.0002409775,0.035230547,0.00007013841,0.000089224195,0.0008109071,0.0002137556,0.89510155,0.06158959,0.0017245371,0.00487205,0.0000362584],"about_ca_topic_score_codex":0.005807629,"about_ca_topic_score_gemma":0.010498761,"teacher_disagreement_score":0.005807629,"about_ca_system_score_codex":0.00040696067,"about_ca_system_score_gemma":0.00053402537,"threshold_uncertainty_score":0.011547685},"labels":[],"label_agreement":null},{"id":"W4408127911","doi":"10.1007/978-3-031-82377-0_2","title":"A Novel Bounding Box Regression Method for Single Object Tracking","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Okanagan University College; University of British Columbia, Okanagan Campus","funders":"","keywords":"Minimum bounding box; Computer science; Bounding overwatch; Object (grammar); Tracking (education); Artificial intelligence; Video tracking; Regression; Computer vision; Statistics; Mathematics; Psychology","score_opus":0.04367194458511749,"score_gpt":0.313939509381973,"score_spread":0.27026756479685554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408127911","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":[0.0006741466,0.0002632107,0.99729437,0.00003048917,0.00007236762,0.000018950266,0.000037014008,0.001221643,0.00038773834],"genre_scores_gemma":[0.020984411,0.0005997056,0.9689672,0.00015558829,0.00014396223,0.00011434853,0.0005566119,0.0008335554,0.0076446026],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99839646,0.00021126194,0.00005705245,0.0004357926,0.00078182167,0.00011762682],"domain_scores_gemma":[0.9986884,0.0004666132,0.000073758754,0.0002242095,0.00048280004,0.000064239866],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001460799,0.0017571119,0.0032461595,0.0014935994,0.00075833715,0.0015400486,0.0038133636,0.0021191593,0.0058185584],"category_scores_gemma":[0.0031811635,0.0014395762,0.0014441238,0.0023274138,0.0006093947,0.0019984408,0.0022392888,0.0030058431,0.007563853],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018456278,0.00012507617,0.00039492454,0.00017248813,0.00015355815,0.00011615189,0.000060642586,0.07935447,0.037607506,0.0056841453,0.012281969,0.8638645],"study_design_scores_gemma":[0.000008663939,0.000018862722,0.00016329177,0.000011121844,0.000021495052,0.00009313956,0.000005643753,0.9877049,0.0061823013,0.0013643049,0.004409254,0.0000171806],"about_ca_topic_score_codex":0.0071725715,"about_ca_topic_score_gemma":0.0069283783,"teacher_disagreement_score":0.0071725715,"about_ca_system_score_codex":0.0005833257,"about_ca_system_score_gemma":0.0013712837,"threshold_uncertainty_score":0.01946497},"labels":[],"label_agreement":null},{"id":"W4408327618","doi":"10.1007/978-981-97-8602-2_25","title":"Smart Traffic Analyser Using Deep Learning","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Analyser; Computer science; Artificial intelligence; Chemistry; Chromatography","score_opus":0.024182737585434145,"score_gpt":0.269892305619252,"score_spread":0.24570956803381783,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408327618","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.027553704,0.00034042375,0.9254553,0.00014979731,0.0003007014,0.00012991314,0.0020575006,0.03639838,0.007614234],"genre_scores_gemma":[0.35775727,0.0003969855,0.59467477,0.00055756734,0.00024396264,0.0003145296,0.0073131234,0.0014040688,0.03733778],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99981433,0.000012120125,0.0000074422596,0.00006794331,0.000067568464,0.000030671534],"domain_scores_gemma":[0.99978334,0.000050156938,0.000014373602,0.000038683538,0.000089564695,0.000023915703],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00029941645,0.00082977937,0.00078059395,0.0012760467,0.00026512562,0.0007514593,0.0008772614,0.000643697,0.00995834],"category_scores_gemma":[0.00052289746,0.00042566744,0.0004420524,0.00061713567,0.0001611547,0.00073727715,0.00065529556,0.00084565306,0.007173183],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004530062,0.00021466805,0.002212091,0.000105281055,0.00010995555,0.00014014659,0.00004163702,0.022550154,0.06559116,0.0024496503,0.03566318,0.8704691],"study_design_scores_gemma":[0.000036848847,0.00008715749,0.0028154687,0.000022444154,0.00004419341,0.000163924,0.000024101277,0.9465271,0.03263974,0.0033635087,0.014237477,0.00003800377],"about_ca_topic_score_codex":0.0031315607,"about_ca_topic_score_gemma":0.0053986316,"teacher_disagreement_score":0.00995834,"about_ca_system_score_codex":0.00043650102,"about_ca_system_score_gemma":0.00051233335,"threshold_uncertainty_score":0.03331393},"labels":[],"label_agreement":null},{"id":"W4408352797","doi":"10.1109/icassp49660.2025.10890784","title":"One-Shot Learning for Pose-Guided Person Image Synthesis in the Wild","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Computer vision; Shot (pellet); One shot; Image (mathematics); Engineering; Materials science","score_opus":0.10893956664917079,"score_gpt":0.36015008750042593,"score_spread":0.25121052085125517,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408352797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.019110603,0.00023940303,0.9711691,0.00008404638,0.000118901284,0.00012587568,0.00033814632,0.006877322,0.0019366252],"genre_scores_gemma":[0.43345353,0.0002493118,0.551213,0.000526171,0.0001484333,0.00030775106,0.0044020917,0.0012383634,0.008461295],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993273,0.00010228869,0.00001987929,0.0003316231,0.00013851793,0.0000803184],"domain_scores_gemma":[0.99932337,0.0002233287,0.000038613118,0.0002308609,0.000113578615,0.000070330476],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010872005,0.0013274103,0.0011380235,0.00071882294,0.00035821187,0.00077922794,0.0021633809,0.0012227276,0.006634057],"category_scores_gemma":[0.0027795078,0.00075451884,0.0010716945,0.00050437346,0.00069948076,0.0012940968,0.0016700967,0.0019471862,0.0034306096],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005797761,0.00043063352,0.001458525,0.00022701395,0.0001876751,0.00023873361,0.0001590248,0.20403472,0.044174653,0.004568633,0.012817252,0.73112327],"study_design_scores_gemma":[0.00002621727,0.00012575147,0.0004945133,0.000012274376,0.000017327402,0.00013860644,0.0000311453,0.9779351,0.014005827,0.0053064288,0.001891636,0.000015085105],"about_ca_topic_score_codex":0.0036685958,"about_ca_topic_score_gemma":0.0057309666,"teacher_disagreement_score":0.006634057,"about_ca_system_score_codex":0.0005628877,"about_ca_system_score_gemma":0.00066260487,"threshold_uncertainty_score":0.022193134},"labels":[],"label_agreement":null},{"id":"W4408383341","doi":"10.1111/2041-210x.70008","title":"One size does not fit all: A novel approach for determining the Realised Viewshed Size for remote camera traps","year":2025,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Calgary; University of British Columbia, Okanagan Campus; University of British Columbia; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Parks Canada; Manitoba Hydro","keywords":"Viewshed analysis; Remote sensing; Environmental science; Computer science; Geography; Computer graphics (images)","score_opus":0.07617462499194565,"score_gpt":0.39160317312925813,"score_spread":0.31542854813731247,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408383341","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":[0.11747818,0.00025224028,0.87969565,0.00011211877,0.0000504228,0.000119913515,0.00026347395,0.000962404,0.0010656428],"genre_scores_gemma":[0.59175235,0.00013752788,0.40648088,0.00008041469,0.00005576999,0.0001596164,0.00042385794,0.00023405606,0.000675483],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9979467,0.0005242304,0.00012625312,0.00073012925,0.0005516295,0.00012113885],"domain_scores_gemma":[0.9885129,0.006711563,0.0016703327,0.0014543643,0.0014122939,0.00023857612],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0030413826,0.000761361,0.0007289165,0.0017617564,0.00035915463,0.0013588888,0.0017328473,0.0009459133,0.0015869009],"category_scores_gemma":[0.02243355,0.00052831543,0.00086532976,0.00090980896,0.0005696114,0.0015302146,0.0016317773,0.0008412143,0.0005316802],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00083619624,0.00038673825,0.11301722,0.0005942099,0.0003676982,0.0009071654,0.0010998786,0.22205035,0.0529713,0.011749203,0.0025347148,0.5934853],"study_design_scores_gemma":[0.000024765128,0.00023424836,0.02913142,0.000065461456,0.00008606214,0.00079837174,0.00022063551,0.94844776,0.0130912475,0.0055885585,0.0022077067,0.00010380997],"about_ca_topic_score_codex":0.0028633645,"about_ca_topic_score_gemma":0.0031287174,"teacher_disagreement_score":0.0030413826,"about_ca_system_score_codex":0.00064361194,"about_ca_system_score_gemma":0.0007260923,"threshold_uncertainty_score":0.016084611},"labels":[],"label_agreement":null},{"id":"W4408575268","doi":"10.1007/s11263-025-02396-5","title":"Fusion for Visual-Infrared Person ReID in Real-World Surveillance Using Corrupted Multimodal Data","year":2025,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Mila - Quebec Artificial Intelligence Institute","funders":"","keywords":"Artificial intelligence; Computer science; Pattern recognition (psychology); Computer vision; Sensor fusion; Infrared","score_opus":0.06860061145468761,"score_gpt":0.4256542456178662,"score_spread":0.3570536341631786,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408575268","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.19022308,0.0011085839,0.8025442,0.0003162659,0.00030525812,0.00008797828,0.0005752239,0.0018409491,0.0029984505],"genre_scores_gemma":[0.84838253,0.0004402605,0.14650166,0.00012723831,0.000111546695,0.00006797176,0.0010869444,0.000094660136,0.0031871842],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998782,0.00023106526,0.000059603477,0.00031065693,0.00035721084,0.00025943512],"domain_scores_gemma":[0.99918085,0.0001302074,0.0001137941,0.00027818195,0.0002580867,0.00003889283],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015232293,0.0009311785,0.0014340415,0.001475114,0.0007370467,0.0010232638,0.0012341435,0.0013080294,0.0016127472],"category_scores_gemma":[0.0030228803,0.0004628219,0.0014042705,0.0014970987,0.0005797048,0.001815751,0.0021749495,0.0009544725,0.0012592654],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020239237,0.0005044719,0.009506353,0.00030629063,0.00047809357,0.00045298436,0.00046243792,0.107970886,0.09582325,0.0030928594,0.0070142597,0.7723642],"study_design_scores_gemma":[0.000020164829,0.00034910237,0.01381961,0.000037951027,0.00020775538,0.00048601086,0.00023939286,0.9293932,0.04888275,0.0037233785,0.0027745056,0.00006634235],"about_ca_topic_score_codex":0.002792606,"about_ca_topic_score_gemma":0.004226409,"teacher_disagreement_score":0.002792606,"about_ca_system_score_codex":0.0004394551,"about_ca_system_score_gemma":0.00082329905,"threshold_uncertainty_score":0.008055687},"labels":[],"label_agreement":null},{"id":"W4408712132","doi":"10.1109/itsc58415.2024.10919608","title":"An Efficient Approach to Generate Safe Drivable Space by LiDAR-Camera-HDmap Fusion","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta; University of Waterloo","funders":"","keywords":"Lidar; Computer vision; Computer science; Artificial intelligence; Fusion; Space (punctuation); Sensor fusion; Image fusion; Computer graphics (images); Remote sensing; Image (mathematics); Geography","score_opus":0.018289521196368168,"score_gpt":0.2880712065972417,"score_spread":0.26978168540087355,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408712132","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009254936,0.00008396091,0.98870826,0.00004998606,0.000022172742,0.000031162985,0.0001539682,0.0011803932,0.00051523384],"genre_scores_gemma":[0.2919228,0.00016209483,0.70412844,0.00017357481,0.000047485486,0.0001174262,0.0015261783,0.00031735928,0.0016046737],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995271,0.000044506447,0.000016571601,0.0001703687,0.00018006281,0.00006141965],"domain_scores_gemma":[0.99963987,0.00004153533,0.000040491526,0.0001304114,0.00012513992,0.000022486776],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00038830607,0.0010204463,0.00058739405,0.0009959366,0.0003314328,0.00073044415,0.0014343371,0.00069145655,0.0016950817],"category_scores_gemma":[0.0011262542,0.0004605903,0.0008783027,0.0010142308,0.00046185433,0.0013645269,0.002050803,0.0010887868,0.001077515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001640386,0.00012466792,0.0021653965,0.00012559058,0.00009994837,0.00012879026,0.00020268084,0.23870973,0.044299446,0.006312671,0.005935507,0.70173144],"study_design_scores_gemma":[0.000010614905,0.00007693446,0.0012021636,0.000013705329,0.000021479578,0.00012883324,0.00007383469,0.9649589,0.021459159,0.006877134,0.005149925,0.000027314694],"about_ca_topic_score_codex":0.0026473955,"about_ca_topic_score_gemma":0.0037409395,"teacher_disagreement_score":0.0026473955,"about_ca_system_score_codex":0.00038125282,"about_ca_system_score_gemma":0.00071444287,"threshold_uncertainty_score":0.0056706667},"labels":[],"label_agreement":null},{"id":"W4408712297","doi":"10.1109/itsc58415.2024.10919958","title":"Pedestrian Crossing Intent Prediction Using Vision Transformers","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Pedestrian; Computer science; Transformer; Artificial intelligence; Computer vision; Transport engineering; Engineering; Electrical engineering; Voltage","score_opus":0.04660567227130682,"score_gpt":0.35378257851707123,"score_spread":0.30717690624576444,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408712297","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":[0.5499952,0.0011485418,0.43079978,0.0002843423,0.0002889989,0.00017001329,0.001692482,0.0067439913,0.008876652],"genre_scores_gemma":[0.9737819,0.0002408483,0.021980619,0.000052671792,0.000025460482,0.000019078496,0.0013968287,0.000037160276,0.0024655163],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99986696,0.000010758492,0.000005519322,0.000050863862,0.000033412667,0.000032513963],"domain_scores_gemma":[0.99981934,0.000030128716,0.000022466245,0.00001646628,0.000086495456,0.000025209618],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021601345,0.00088905654,0.0004940197,0.0013301433,0.0001688848,0.00051347783,0.0005888857,0.00037066688,0.0010911168],"category_scores_gemma":[0.00052194117,0.00029207845,0.0006313219,0.00050253427,0.00017799393,0.0006611159,0.0005042076,0.0005446759,0.0006715882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0011654375,0.00075538206,0.044382505,0.0001904158,0.00016925193,0.00058826467,0.00013694198,0.21542735,0.036804333,0.0023153676,0.014084765,0.68398005],"study_design_scores_gemma":[0.000007948028,0.000100488156,0.004949426,0.00001006526,0.000025135196,0.0000911761,0.000028132881,0.9889705,0.0044855643,0.0007817168,0.0005406623,0.000009244941],"about_ca_topic_score_codex":0.012539309,"about_ca_topic_score_gemma":0.01582451,"teacher_disagreement_score":0.012539309,"about_ca_system_score_codex":0.000494769,"about_ca_system_score_gemma":0.0005998262,"threshold_uncertainty_score":0.024932623},"labels":[],"label_agreement":null},{"id":"W4408897724","doi":"10.1109/ism63611.2024.00036","title":"Sliding Window Check: Repairing Object Identities","year":2024,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"Mitacs","keywords":"Window (computing); Computer science; Object (grammar); Sliding window protocol; Computer vision; Artificial intelligence; World Wide Web","score_opus":0.02364966595937328,"score_gpt":0.29984890893746813,"score_spread":0.27619924297809484,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408897724","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034692507,0.0008556388,0.95570797,0.000118475255,0.00029030332,0.00015276973,0.00026860656,0.0066294777,0.0012842413],"genre_scores_gemma":[0.3498558,0.00039136774,0.640771,0.00023225218,0.0001434683,0.00017408025,0.0014912648,0.0009927307,0.005948122],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9967694,0.00038694116,0.00022080784,0.0010564858,0.0012632789,0.00030322076],"domain_scores_gemma":[0.9894974,0.0026899336,0.001091993,0.004354305,0.0018489354,0.000517287],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004094991,0.0015746392,0.0020963526,0.0022133011,0.00133843,0.0017526153,0.0041541504,0.0018793957,0.0043500187],"category_scores_gemma":[0.015507798,0.0007455752,0.0012163856,0.0019917712,0.0009924434,0.0031702318,0.0036464042,0.001999891,0.0023799492],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0010899868,0.00019447698,0.0075413315,0.00028970852,0.0002074412,0.00038943015,0.00040040517,0.03770538,0.026108112,0.0104533555,0.0108395275,0.90478086],"study_design_scores_gemma":[0.00013662742,0.00052295387,0.004409223,0.00007335771,0.00016287665,0.0011906106,0.00021316952,0.8831837,0.07661871,0.012212489,0.02117405,0.000102161306],"about_ca_topic_score_codex":0.0060077915,"about_ca_topic_score_gemma":0.0058527705,"teacher_disagreement_score":0.0060077915,"about_ca_system_score_codex":0.00070019346,"about_ca_system_score_gemma":0.0022411325,"threshold_uncertainty_score":0.021656632},"labels":[],"label_agreement":null},{"id":"W4408934297","doi":"10.1016/j.trc.2025.105112","title":"A benchmark for cycling close pass detection from video streams","year":2025,"lang":"en","type":"article","venue":"Transportation Research Part C Emerging Technologies","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Australian Research Council; Compute Canada","keywords":"STREAMS; Benchmark (surveying); Cycling; Computer science; Real-time computing; Engineering; Geography; Computer network; Cartography","score_opus":0.05752209421769096,"score_gpt":0.40101897328873704,"score_spread":0.3434968790710461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4408934297","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":[0.577849,0.018362472,0.21724044,0.0047506723,0.0036962354,0.002166144,0.10262524,0.043559555,0.029750332],"genre_scores_gemma":[0.5983353,0.0024160314,0.16164804,0.0010330933,0.00038878928,0.0006507561,0.22607398,0.00081632455,0.0086376155],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9971162,0.0005254304,0.00023790622,0.001026343,0.0007111168,0.00038299954],"domain_scores_gemma":[0.9971163,0.0009846984,0.00025030208,0.00042109468,0.000983199,0.00024430596],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026085132,0.0033590293,0.0012899578,0.003359748,0.00087384216,0.002653284,0.0039883116,0.0029792462,0.0021056312],"category_scores_gemma":[0.010122401,0.00043567855,0.0014227363,0.002435553,0.0008469042,0.0022279464,0.0018924564,0.0022767978,0.0017720795],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0020241614,0.0017596495,0.03615485,0.0020162633,0.0007078266,0.00090946176,0.00023078012,0.37253854,0.012276216,0.0044404687,0.15148538,0.41545638],"study_design_scores_gemma":[0.000081093174,0.00041176268,0.008149086,0.0001512195,0.00007493723,0.00029489805,0.0002332128,0.9652057,0.009572163,0.0027436046,0.013027256,0.000055084303],"about_ca_topic_score_codex":0.056339584,"about_ca_topic_score_gemma":0.051931717,"teacher_disagreement_score":0.056339584,"about_ca_system_score_codex":0.0029981364,"about_ca_system_score_gemma":0.002271772,"threshold_uncertainty_score":0.112023294},"labels":[],"label_agreement":null},{"id":"W4409253236","doi":"10.1007/978-3-031-87364-5_51","title":"Integrating Privacy-Preserving Occupancy Estimation Using Thermal Camera with a Digital Twin Platform","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in civil engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Occupancy; Computer science; Digital camera; Estimation; Computer vision; Computer graphics (images); Engineering; Architectural engineering; Systems engineering","score_opus":0.017226113885404924,"score_gpt":0.25195426219342326,"score_spread":0.23472814830801833,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409253236","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.024610123,0.0002091655,0.9721342,0.00006681526,0.00010283755,0.000025652884,0.000070408496,0.0005969519,0.0021838262],"genre_scores_gemma":[0.6804015,0.00031913762,0.3140705,0.00012966365,0.000107215026,0.0000654066,0.0002538209,0.000113682894,0.0045390795],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9992761,0.000116473646,0.000024346962,0.00020982159,0.00025482313,0.00011847009],"domain_scores_gemma":[0.9996314,0.00006901557,0.000028595836,0.00015160415,0.00009252204,0.000026875832],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004164786,0.00066797144,0.0009721807,0.00035823477,0.00040952882,0.0009913117,0.0012776891,0.0005464152,0.0015353946],"category_scores_gemma":[0.001458527,0.00039857064,0.0006350829,0.00082355406,0.0003710984,0.0019361142,0.0019514821,0.0007865105,0.00057172234],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009044293,0.00023069348,0.0038854866,0.0002249769,0.00019405675,0.00050718547,0.0003130479,0.17402142,0.12687536,0.0213595,0.0054008826,0.666083],"study_design_scores_gemma":[0.000016262025,0.00012219047,0.00092914666,0.000010374081,0.000041260468,0.000328551,0.000059890095,0.9628196,0.026835652,0.0057250247,0.0030797,0.00003237518],"about_ca_topic_score_codex":0.0023346292,"about_ca_topic_score_gemma":0.0031914846,"teacher_disagreement_score":0.0023346292,"about_ca_system_score_codex":0.00028212066,"about_ca_system_score_gemma":0.00086444715,"threshold_uncertainty_score":0.0051363707},"labels":[],"label_agreement":null},{"id":"W4409404996","doi":"10.1155/atr/2728376","title":"An Improved Kernelized Correlation Filter for Extracting Traffic Flow in Satellite Videos","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Satellite; Traffic flow (computer networking); Computer science; Filter (signal processing); Flow (mathematics); Correlation; Artificial intelligence; Computer vision; Engineering; Mathematics; Computer security","score_opus":0.013555882495021553,"score_gpt":0.3135912305815995,"score_spread":0.30003534808657795,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409404996","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":[0.04022394,0.00023696934,0.9576475,0.00005161717,0.000053350122,0.000045680023,0.00016233229,0.0010747991,0.00050375727],"genre_scores_gemma":[0.40577713,0.00069277454,0.5891466,0.00008930085,0.0000939422,0.00013420214,0.0012288397,0.00016863056,0.002668488],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995216,0.00004535584,0.000030129942,0.00014546113,0.00018178983,0.00007559735],"domain_scores_gemma":[0.99937975,0.000122268,0.00007279445,0.000053977823,0.00034712136,0.000024078843],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059034314,0.00066845544,0.000666138,0.0017645076,0.00033211854,0.0006230399,0.0005704424,0.0005033819,0.00088623335],"category_scores_gemma":[0.0020704651,0.00028402277,0.0007481239,0.0018257344,0.00022164725,0.0012911938,0.00040088594,0.0006408573,0.0005489431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00033378642,0.00016019103,0.007346254,0.00018675241,0.0000972238,0.0001648592,0.00013741331,0.07925424,0.072267845,0.0030881767,0.0041860347,0.8327772],"study_design_scores_gemma":[0.00001335584,0.00005855474,0.003956737,0.000010340149,0.00002999381,0.000116042036,0.000027084436,0.97514546,0.018239709,0.0004993284,0.0018801679,0.000023208386],"about_ca_topic_score_codex":0.014797068,"about_ca_topic_score_gemma":0.01075598,"teacher_disagreement_score":0.014797068,"about_ca_system_score_codex":0.00045598325,"about_ca_system_score_gemma":0.0012004355,"threshold_uncertainty_score":0.029421866},"labels":[],"label_agreement":null},{"id":"W4409603267","doi":"10.61091/jcmcc127b-111","title":"Human Motion Morphology Capture Based on Computer Vision and Image Segmentation Algorithms","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Computer vision; Artificial intelligence; Segmentation; Computer science; Motion capture; Morphology (biology); Motion (physics); Image (mathematics); Mathematical morphology; Image segmentation; Human motion; Computer graphics (images); Image processing; Geology","score_opus":0.012655287232040244,"score_gpt":0.30417075263600823,"score_spread":0.291515465403968,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4409603267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.021411853,0.0010123238,0.97212166,0.00013312414,0.00008498197,0.00016972332,0.000084292325,0.0010286844,0.003953393],"genre_scores_gemma":[0.22033839,0.0023130884,0.7695619,0.0002985036,0.00013844609,0.00026055274,0.00043788113,0.00017852416,0.0064727566],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9993024,0.00006570192,0.0000443151,0.00019824432,0.00034269557,0.000046568453],"domain_scores_gemma":[0.9994735,0.00012702,0.000068668625,0.00007384344,0.00023974584,0.000017262855],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00044830533,0.000630561,0.00044633215,0.0020800885,0.00031538366,0.0008569632,0.00058551284,0.0007704933,0.0016988153],"category_scores_gemma":[0.0011832076,0.00041559146,0.00073007186,0.0013658648,0.0005635585,0.0011095019,0.00047976736,0.000538052,0.00094445253],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001362407,0.000054436885,0.0023609926,0.0004491574,0.00007424882,0.00016754953,0.0002220183,0.011504902,0.24208431,0.004392277,0.0028651445,0.73568875],"study_design_scores_gemma":[0.00004925484,0.00074047106,0.03574678,0.00023168018,0.00024368023,0.0024923699,0.00035796547,0.47105384,0.42733166,0.008298782,0.053242598,0.00021085027],"about_ca_topic_score_codex":0.0024090344,"about_ca_topic_score_gemma":0.0031198112,"teacher_disagreement_score":0.0024090344,"about_ca_system_score_codex":0.00050641864,"about_ca_system_score_gemma":0.0005969876,"threshold_uncertainty_score":0.005683124},"labels":[],"label_agreement":null},{"id":"W4410216546","doi":"10.55041/isjem03425","title":"Dog Breed Prediction Using Deep Learning","year":2025,"lang":"en","type":"article","venue":"International Scientific Journal of Engineering and Management","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Breed; Artificial intelligence; Deep learning; Computer science; Biology; Animal science","score_opus":0.010979767920083336,"score_gpt":0.2672099213147186,"score_spread":0.2562301533946353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410216546","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":[0.7958345,0.0049905186,0.16916485,0.0014643561,0.0004732658,0.00013736733,0.01282191,0.005480328,0.009632784],"genre_scores_gemma":[0.95621955,0.00054968405,0.026286965,0.00026741627,0.00007956801,0.00005085111,0.012768178,0.000069982256,0.0037078224],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9997631,0.00003252286,0.000013517654,0.00009785733,0.000044943015,0.000048098736],"domain_scores_gemma":[0.99961483,0.000110423454,0.000067401765,0.000036973153,0.000121901554,0.000048387927],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049618393,0.00089287077,0.0005463309,0.0021162652,0.0002650008,0.0005620645,0.0009834494,0.0007922122,0.0033480471],"category_scores_gemma":[0.0010744213,0.0003101607,0.00064265303,0.0010242176,0.00026919256,0.0006660767,0.0005557972,0.00070339034,0.0012919165],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0008672517,0.0009201215,0.16729878,0.00025517613,0.00041077725,0.0007333697,0.00007035241,0.31431004,0.007638343,0.0015112106,0.043847244,0.46213728],"study_design_scores_gemma":[0.000014298516,0.0000485253,0.0071689184,0.000028273722,0.00003004193,0.0001118913,0.00002713555,0.98852855,0.0011774545,0.0011512405,0.0017037485,0.0000098856835],"about_ca_topic_score_codex":0.011434412,"about_ca_topic_score_gemma":0.01799532,"teacher_disagreement_score":0.011434412,"about_ca_system_score_codex":0.00062985043,"about_ca_system_score_gemma":0.00053609087,"threshold_uncertainty_score":0.022735715},"labels":[],"label_agreement":null},{"id":"W4410738508","doi":"10.1007/978-3-031-91767-7_24","title":"The Second Visual Object Tracking Segmentation VOTS2024 Challenge Results","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Ontario Tech University; University of British Columbia; University of British Columbia Hospital","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Segmentation; Object (grammar); Image segmentation; Tracking (education); Video tracking; Computer graphics (images)","score_opus":0.026340471859668396,"score_gpt":0.3118841803097401,"score_spread":0.2855437084500717,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410738508","genre_codex":"methods","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.14689618,0.03357506,0.49579215,0.019701062,0.03010914,0.00323909,0.08257341,0.06559825,0.12251557],"genre_scores_gemma":[0.21853098,0.003773352,0.32037008,0.005974977,0.0038052376,0.0011012212,0.32456458,0.0072874995,0.11459201],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9960395,0.0004623149,0.00017546458,0.0013078395,0.0014456032,0.0005691492],"domain_scores_gemma":[0.9958341,0.0008623712,0.00009980701,0.001042934,0.0015113624,0.0006494008],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0037488113,0.0031968683,0.0037479962,0.0016742273,0.002229344,0.0046164384,0.0031295042,0.0051844716,0.010546244],"category_scores_gemma":[0.007087826,0.00079561694,0.0019873676,0.0018353577,0.00095579383,0.0022397384,0.0034746574,0.0034677482,0.012968886],"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":[0.0014404744,0.0006417023,0.0011885655,0.00092833623,0.00034191678,0.00047667083,0.00018640741,0.01005785,0.022022383,0.0037965926,0.51483893,0.44408023],"study_design_scores_gemma":[0.0011983288,0.0019942066,0.012977116,0.00041772064,0.00056259934,0.0022120753,0.0006831727,0.2868499,0.0948054,0.030100653,0.5679742,0.00022453595],"about_ca_topic_score_codex":0.026714459,"about_ca_topic_score_gemma":0.032992046,"teacher_disagreement_score":0.026714459,"about_ca_system_score_codex":0.0020833702,"about_ca_system_score_gemma":0.003212547,"threshold_uncertainty_score":0.05311787},"labels":[],"label_agreement":null},{"id":"W4410749204","doi":"10.1155/atr/2728315","title":"Research on Machine Vision–Based Intelligent Tracking System for Maintenance Personnel","year":2025,"lang":"en","type":"article","venue":"Journal of Advanced Transportation","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"National Key Research and Development Program of China","keywords":"Tracking (education); Computer science; Machine vision; Tracking system; Systems engineering; Engineering; Artificial intelligence; Psychology; Kalman filter","score_opus":0.04913162442548397,"score_gpt":0.39455903760574657,"score_spread":0.3454274131802626,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410749204","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.16895454,0.028654017,0.76470685,0.0012781939,0.0019584463,0.00042568814,0.0026564652,0.015838243,0.015527476],"genre_scores_gemma":[0.6819563,0.010600034,0.27937827,0.00094031455,0.00060385594,0.00031401412,0.008890039,0.00018102718,0.017136134],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9989041,0.00011418049,0.000074311705,0.0004504064,0.00032534348,0.00013170502],"domain_scores_gemma":[0.9993492,0.00010733644,0.00006872704,0.000103688166,0.00033139985,0.000039632574],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010956685,0.0009483172,0.0010015435,0.002039051,0.00043556662,0.0010494615,0.0013520696,0.0010089644,0.0018685511],"category_scores_gemma":[0.0017397542,0.0003151793,0.0009746532,0.0015680336,0.00027134005,0.0013136256,0.00045858536,0.00076164654,0.001577329],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027225763,0.0002643925,0.00494621,0.00029878962,0.00011073375,0.000112572634,0.0000684484,0.013816432,0.031274073,0.0014889585,0.016453315,0.9308939],"study_design_scores_gemma":[0.000053180345,0.00064193853,0.020405825,0.00010864393,0.0001801116,0.0003609018,0.00010576043,0.8963635,0.04645717,0.0023753077,0.032879416,0.00006833586],"about_ca_topic_score_codex":0.0108097065,"about_ca_topic_score_gemma":0.0067485687,"teacher_disagreement_score":0.0108097065,"about_ca_system_score_codex":0.00086449005,"about_ca_system_score_gemma":0.0010296523,"threshold_uncertainty_score":0.021493614},"labels":[],"label_agreement":null},{"id":"W4410907419","doi":"10.21428/d82e957c.322c472b","title":"Attention-Mamba for Multi-Object Tracking","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Object (grammar); Tracking (education); Computer science; Computer vision; Artificial intelligence; Psychology","score_opus":0.06067511469957045,"score_gpt":0.3711823347099426,"score_spread":0.3105072200103722,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4410907419","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":[0.0063575446,0.00088406866,0.98782676,0.0001590797,0.00007827732,0.000039827923,0.000110626796,0.0035464456,0.0009974207],"genre_scores_gemma":[0.4599511,0.0008461107,0.5297981,0.00064412766,0.00016682546,0.00033005574,0.00093965826,0.00040478344,0.006919272],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99959046,0.00007224278,0.000016823456,0.00016410727,0.000101472935,0.000054822307],"domain_scores_gemma":[0.999481,0.00022437399,0.00005218814,0.00009816945,0.00010954769,0.000034770765],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00089095946,0.0011332186,0.0010872739,0.00077713974,0.00058943883,0.0008062269,0.002746249,0.0013857973,0.0037593227],"category_scores_gemma":[0.0023355503,0.0005547001,0.00075755676,0.0010225553,0.00047802582,0.001354797,0.0016158521,0.00199881,0.0015659239],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002742701,0.00016083464,0.0015076782,0.00018107612,0.0001435585,0.00007822011,0.000109801724,0.45520326,0.017802617,0.010017754,0.0075423964,0.5069786],"study_design_scores_gemma":[0.0000046800146,0.000018020235,0.0001374179,0.000004728033,0.000008252564,0.000011591224,0.0000043008895,0.99438715,0.0015512401,0.00283216,0.0010361518,0.00000432183],"about_ca_topic_score_codex":0.016949566,"about_ca_topic_score_gemma":0.020519957,"teacher_disagreement_score":0.016949566,"about_ca_system_score_codex":0.0013914899,"about_ca_system_score_gemma":0.0014028384,"threshold_uncertainty_score":0.033701837},"labels":[],"label_agreement":null},{"id":"W4411083203","doi":"10.1016/j.neunet.2025.107673","title":"LaTP: LiDAR-aided multimodal token pruning for efficient trajectory prediction of autonomous driving","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Key Research and Development Projects of Shaanxi Province; National Natural Science Foundation of China","keywords":"Pruning; Security token; Computer science; Trajectory; Inference; Artificial intelligence; Lidar; Context (archaeology); Machine learning","score_opus":0.017356968682522636,"score_gpt":0.2748433309130945,"score_spread":0.25748636223057186,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411083203","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.052221946,0.00046243757,0.9364171,0.00018426776,0.00015780238,0.00011605207,0.0012980612,0.0073767407,0.0017656763],"genre_scores_gemma":[0.6037438,0.00024238693,0.3822888,0.00016775535,0.000103790226,0.0002719789,0.0043347673,0.00046838526,0.008378355],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99971837,0.000035381807,0.000014015966,0.00008945448,0.00007833972,0.00006439129],"domain_scores_gemma":[0.99961793,0.00011630411,0.000033075303,0.00007061269,0.00013031455,0.00003159465],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00053778436,0.0010138323,0.0010096735,0.0010078317,0.0006667627,0.0007550742,0.0020504107,0.0009465047,0.0045522354],"category_scores_gemma":[0.0016547999,0.00045849444,0.0004667324,0.001047165,0.00033678563,0.0012440415,0.0016572049,0.0010201314,0.0016818102],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006197333,0.00019454298,0.0023292243,0.000114004935,0.00009323883,0.00023359654,0.000090554626,0.17585526,0.01213898,0.0037886696,0.014690973,0.7898512],"study_design_scores_gemma":[0.000012996386,0.000032871874,0.0003028589,0.0000061825594,0.000010726013,0.00003165297,0.000019753292,0.9929564,0.003984827,0.0015812077,0.0010545709,0.0000060181883],"about_ca_topic_score_codex":0.013423625,"about_ca_topic_score_gemma":0.020494025,"teacher_disagreement_score":0.013423625,"about_ca_system_score_codex":0.0006334462,"about_ca_system_score_gemma":0.001361671,"threshold_uncertainty_score":0.02669096},"labels":[],"label_agreement":null},{"id":"W4411095428","doi":"10.3233/atde250249","title":"Research on Real-Time Vehicle Detection and Tracking Algorithm Based on Dense Optical Flow","year":2025,"lang":"en","type":"book-chapter","venue":"Advances in transdisciplinary engineering","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Tracking (education); Optical flow; Computer science; Flow (mathematics); Algorithm; Real-time computing; Computer vision; Artificial intelligence; Mathematics; Geometry; Psychology","score_opus":0.021258703134073707,"score_gpt":0.3206096958122149,"score_spread":0.2993509926781412,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411095428","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0069490727,0.003122081,0.9865696,0.00010504581,0.00012545388,0.000031776657,0.000036726586,0.00055271725,0.002507567],"genre_scores_gemma":[0.20629956,0.010014611,0.77263194,0.00022068177,0.00032700208,0.00014462274,0.0006626003,0.00022556183,0.009473346],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9996412,0.000028664459,0.000019484925,0.00013613162,0.00013798886,0.000036470254],"domain_scores_gemma":[0.99970645,0.0000711726,0.000026811287,0.00002980946,0.00014919919,0.000016532958],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00049466547,0.0008615937,0.000712089,0.0017078375,0.00042031752,0.0008949886,0.0011617325,0.00067393866,0.0017009629],"category_scores_gemma":[0.00070116506,0.0004707149,0.00060403056,0.0018548428,0.00046362093,0.0030425554,0.00050211314,0.00073952257,0.00056380266],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00007844746,0.00007736394,0.0010661277,0.0003913895,0.000045229128,0.000050587554,0.00013246918,0.04558428,0.03767991,0.023928327,0.0037409137,0.8872249],"study_design_scores_gemma":[0.000030434128,0.00015481467,0.0015987585,0.00006315679,0.00007600203,0.00033055927,0.0000675985,0.91661453,0.036968406,0.011281864,0.032744683,0.0000691778],"about_ca_topic_score_codex":0.004424829,"about_ca_topic_score_gemma":0.0019479629,"teacher_disagreement_score":0.004424829,"about_ca_system_score_codex":0.0007903158,"about_ca_system_score_gemma":0.0009564392,"threshold_uncertainty_score":0.008798182},"labels":[],"label_agreement":null},{"id":"W4411533048","doi":"10.1007/s40747-025-01983-w","title":"A novel method for distracted driving behaviors recognition with hybrid CNN-BiLSTM-AM model","year":2025,"lang":"en","type":"article","venue":"Complex & Intelligent Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"National Natural Science Foundation of China","keywords":"Distracted driving; Computational intelligence; Computer science; Artificial intelligence; Psychology; Distraction; Cognitive psychology","score_opus":0.09483922658772354,"score_gpt":0.35844737844473906,"score_spread":0.2636081518570155,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411533048","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0603787,0.0013694597,0.92514396,0.00029824272,0.00043236904,0.00013677929,0.00095398596,0.0061508673,0.0051356796],"genre_scores_gemma":[0.77018577,0.00092420995,0.21148594,0.00039149527,0.0001429358,0.0001796688,0.003010781,0.0001997435,0.013479379],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997073,0.000020202062,0.000015768022,0.0001097978,0.00009308391,0.000053907628],"domain_scores_gemma":[0.99982077,0.000016536487,0.000021345304,0.000028649438,0.00009379341,0.000018909124],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00028260957,0.0010327253,0.0006025485,0.0008855349,0.00024110243,0.00053010945,0.0011743496,0.00053248834,0.0016629923],"category_scores_gemma":[0.00060748245,0.0002879098,0.00067216,0.00065396604,0.00018068148,0.00074387796,0.0007863321,0.0008667797,0.001032741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028864248,0.00023576358,0.0076331594,0.00017621659,0.00019662845,0.00023709414,0.00010196261,0.046634313,0.045626134,0.0024163509,0.011053228,0.8854005],"study_design_scores_gemma":[0.000010049801,0.00007202686,0.0033204604,0.000016888325,0.00004350971,0.00015778269,0.000028432416,0.9780392,0.013867424,0.0012510782,0.0031705273,0.000022485994],"about_ca_topic_score_codex":0.013025442,"about_ca_topic_score_gemma":0.016338343,"teacher_disagreement_score":0.013025442,"about_ca_system_score_codex":0.00048234852,"about_ca_system_score_gemma":0.00085135625,"threshold_uncertainty_score":0.025899231},"labels":[],"label_agreement":null},{"id":"W4411656657","doi":"10.51847/qfim4xjapy","title":"10.51847/QFIM4xjAPy","year":2000,"lang":"en","type":"article","venue":"Time to knit","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Pooling; Identification (biology); Artificial intelligence; Computer science; Architecture; Deep learning; Machine learning; Geography; Archaeology; Biology","score_opus":0.011846511964070449,"score_gpt":0.22575814230809235,"score_spread":0.2139116303440219,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411656657","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":[0.0048323213,0.001676693,0.03581819,0.0018539134,0.0013655013,0.0003711474,0.0059189284,0.0133837145,0.9347796],"genre_scores_gemma":[0.011644496,0.0005706372,0.010780763,0.0006048034,0.00020594978,0.00013390766,0.004892861,0.00090790784,0.97025865],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.9996445,0.000037761583,0.000024786197,0.00011924672,0.0001097884,0.000063939784],"domain_scores_gemma":[0.9992988,0.00015365446,0.000043896798,0.00018605476,0.00020941853,0.00010818027],"candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0007169938,0.0016631838,0.0007174128,0.002523807,0.001087478,0.0021038363,0.0016146178,0.0034297232,0.9197681],"category_scores_gemma":[0.0010625847,0.0005257479,0.00067568733,0.0026913777,0.00065615895,0.0017027962,0.0018541242,0.0012458153,0.9121379],"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":[0.00023472012,0.00017808285,0.00089717953,0.0002805596,0.00002566569,0.00020363803,0.000058372934,0.000999496,0.006731152,0.006373385,0.20798832,0.77602947],"study_design_scores_gemma":[0.00007683718,0.000076300326,0.0016738796,0.00015664488,0.000021758018,0.00031904702,0.00006138521,0.0053317626,0.0033643346,0.0031258143,0.9857599,0.00003233111],"about_ca_topic_score_codex":0.0062152045,"about_ca_topic_score_gemma":0.0034837122,"teacher_disagreement_score":0.080231905,"about_ca_system_score_codex":0.0010218429,"about_ca_system_score_gemma":0.00057753985,"threshold_uncertainty_score":0.11444098},"labels":[],"label_agreement":null},{"id":"W4411726574","doi":"10.1109/meco66322.2025.11049113","title":"Learnable Laplacian Embedding Decomposition for One-Stream Visual Object Trackers","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Decomposition; Embedding; Computer science; Computer vision; Artificial intelligence; Object (grammar); Computer graphics (images); Chemistry","score_opus":0.021601693481131883,"score_gpt":0.3750718147669639,"score_spread":0.353470121285832,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411726574","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.007175149,0.00010140147,0.99163306,0.000054228025,0.000018766352,0.000014192169,0.000053360676,0.00059527595,0.00035463326],"genre_scores_gemma":[0.3969969,0.00041445147,0.59598434,0.00024587972,0.000099403995,0.00012447822,0.0008481714,0.0003182163,0.0049681626],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99965835,0.00006127279,0.000017371021,0.00011430639,0.00011465422,0.00003409115],"domain_scores_gemma":[0.99934906,0.0002308479,0.00007709646,0.00012176797,0.0001646201,0.000056592104],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000676919,0.0007504059,0.0007359083,0.00069309137,0.00022460453,0.00073469436,0.0010049265,0.0006884922,0.0017455078],"category_scores_gemma":[0.0030045144,0.00034256277,0.00057471194,0.00089673593,0.00047837044,0.0014225027,0.0011004279,0.0010274803,0.0010205008],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00020979725,0.00013726507,0.001637378,0.00013598129,0.00007571964,0.00011812401,0.000164733,0.21262378,0.04346722,0.012330946,0.0049325237,0.7241666],"study_design_scores_gemma":[0.000005254198,0.00002680997,0.00025289695,0.0000037924885,0.000006084621,0.00003892633,0.000009311845,0.9906206,0.003765131,0.004488468,0.0007755984,0.000007220197],"about_ca_topic_score_codex":0.0022142015,"about_ca_topic_score_gemma":0.0028647615,"teacher_disagreement_score":0.0022142015,"about_ca_system_score_codex":0.00050191506,"about_ca_system_score_gemma":0.0005767544,"threshold_uncertainty_score":0.005839348},"labels":[],"label_agreement":null},{"id":"W4411866868","doi":"10.1109/twc.2025.3582887","title":"Multimodal Visual Image Based User Association and Beamforming Using Graph Neural Networks","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Wireless Communications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Beamforming; Artificial neural network; Artificial intelligence; Association (psychology); Graph; Computer vision; Theoretical computer science; Telecommunications","score_opus":0.02455480218986382,"score_gpt":0.3210780390093272,"score_spread":0.29652323681946335,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4411866868","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008333309,0.00014882117,0.9899693,0.00009379348,0.000022280072,0.000018372799,0.000025051042,0.0002883362,0.001100696],"genre_scores_gemma":[0.6846798,0.00031787966,0.30909666,0.0002833501,0.00009265853,0.00013372536,0.00017152393,0.0001060307,0.0051183375],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995321,0.00014520541,0.000015636562,0.00012491301,0.00010335939,0.00007891158],"domain_scores_gemma":[0.99950445,0.00022390683,0.00008302634,0.00004413135,0.00010813558,0.000036289097],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00058110966,0.0012728446,0.00087944826,0.0005540121,0.0003157483,0.00065643137,0.0012871854,0.001087964,0.0015324805],"category_scores_gemma":[0.00171689,0.00056125695,0.00067442184,0.000809852,0.0006927018,0.0013145217,0.0009633123,0.000956544,0.00038531775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00009621911,0.000053941607,0.0006520256,0.000048798174,0.000048580587,0.00007071872,0.00005333804,0.87799966,0.006371393,0.0057807965,0.00095550343,0.10786911],"study_design_scores_gemma":[0.0000033015388,0.000015358948,0.000056968467,0.000002270203,0.000005657969,0.000012056068,0.0000039984984,0.99724823,0.0007178365,0.001786746,0.00014308232,0.000004498669],"about_ca_topic_score_codex":0.006581249,"about_ca_topic_score_gemma":0.008185583,"teacher_disagreement_score":0.006581249,"about_ca_system_score_codex":0.00081100647,"about_ca_system_score_gemma":0.00080127304,"threshold_uncertainty_score":0.013085902},"labels":[],"label_agreement":null},{"id":"W4412439384","doi":"10.1167/jov.25.9.2428","title":"The contribution of motion detectors during multiple-object tracking","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Tracking (education); Computer vision; Detector; Motion (physics); Artificial intelligence; Object (grammar); Computer science; Physics; Optics; Psychology","score_opus":0.01199363457523242,"score_gpt":0.30994917480708184,"score_spread":0.29795554023184945,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412439384","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":[0.99902654,0.000068515816,0.000654065,0.000006044219,0.0000022702707,0.000005446974,0.000009228665,0.000004447972,0.0002234773],"genre_scores_gemma":[0.9986772,0.00004440461,0.001005196,0.0000073582887,0.0000020183045,0.000008008658,0.000024904739,0.000004741698,0.00022605062],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9997845,0.000035637735,0.000015757874,0.00006766369,0.000056314886,0.000040109262],"domain_scores_gemma":[0.999183,0.00040804988,0.00016663126,0.000050598177,0.00009922328,0.00009252111],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00033726793,0.00023315875,0.00024318787,0.00020330171,0.00013996173,0.0003782596,0.00017871654,0.00031493747,0.0009258258],"category_scores_gemma":[0.0027664984,0.00028296758,0.00011073634,0.00007693937,0.00026314348,0.00047839782,0.0004969533,0.0003243938,0.00010120894],"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":[0.0003787115,0.000026727717,0.0053115403,0.00004779832,0.000005306606,0.000053077318,0.00015414412,0.00010730158,0.9887651,0.00007403749,0.000008393239,0.005067775],"study_design_scores_gemma":[0.000093139344,0.001970698,0.49928275,0.000030134655,0.0000991258,0.00072370574,0.0003640541,0.007442398,0.48884797,0.00051389285,0.00059357507,0.000038589187],"about_ca_topic_score_codex":0.0006560895,"about_ca_topic_score_gemma":0.0008555097,"teacher_disagreement_score":0.0009258258,"about_ca_system_score_codex":0.00018794306,"about_ca_system_score_gemma":0.00022914767,"threshold_uncertainty_score":0.0030972362},"labels":[],"label_agreement":null},{"id":"W4412444797","doi":"10.1109/mis.2025.3588759","title":"Lightweight Attribute Localizing Models for Pedestrian Attribute Recognition","year":2025,"lang":"en","type":"article","venue":"IEEE Intelligent Systems","topic":"Video Surveillance and Tracking Methods","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":"Advanced Micro Devices (Canada)","funders":"Russian Science Foundation","keywords":"Computer science; Pedestrian; Artificial intelligence; Pattern recognition (psychology)","score_opus":0.10447858280496555,"score_gpt":0.3292500610622297,"score_spread":0.22477147825726415,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412444797","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016843405,0.00022051585,0.98001456,0.00012072421,0.000046349982,0.00002858065,0.00014880308,0.0015293921,0.0010475586],"genre_scores_gemma":[0.67019814,0.0007219601,0.31793013,0.00035679713,0.00019620727,0.00015628216,0.00135614,0.00036562703,0.008718694],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9997178,0.00005356514,0.00001241004,0.00008334365,0.00009139265,0.000041519004],"domain_scores_gemma":[0.99968696,0.00008377993,0.000037475744,0.00009290386,0.00007588777,0.000023002553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005420581,0.00078956044,0.0006767513,0.0006828643,0.00026202263,0.0006568484,0.0011182746,0.0006596932,0.0025116378],"category_scores_gemma":[0.0016626623,0.00034347712,0.00094849156,0.0007513554,0.0005089367,0.0012782308,0.0009896103,0.0014995385,0.0016158212],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026958174,0.00011199727,0.0012174401,0.00007375529,0.00007096956,0.00011764423,0.0000827067,0.56902015,0.021906028,0.012534046,0.0053037545,0.38929194],"study_design_scores_gemma":[0.0000025234679,0.000016471298,0.00012775308,0.0000032747919,0.0000064485675,0.000025409,0.0000060724215,0.99476767,0.0020770268,0.0024087864,0.0005528882,0.0000056674976],"about_ca_topic_score_codex":0.0040958077,"about_ca_topic_score_gemma":0.0052978103,"teacher_disagreement_score":0.0040958077,"about_ca_system_score_codex":0.0004980305,"about_ca_system_score_gemma":0.0006237544,"threshold_uncertainty_score":0.008402288},"labels":[],"label_agreement":null},{"id":"W4412459143","doi":"10.1167/jov.25.9.2165","title":"The Effect of Feature Changes on Multiple Object Tracking","year":2025,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Feature (linguistics); Object (grammar); Tracking (education); Video tracking; Computer science; Artificial intelligence; Computer vision; Feature tracking; Pattern recognition (psychology); Psychology; Philosophy; Linguistics","score_opus":0.01267299518903894,"score_gpt":0.33079180262092694,"score_spread":0.31811880743188803,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412459143","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":[0.9955895,0.000243487,0.0028738345,0.00005454448,0.00004677407,0.000045199864,0.000105377454,0.00008474207,0.0009565677],"genre_scores_gemma":[0.99439996,0.00015202706,0.0040711914,0.000109121,0.000023686984,0.000077147524,0.00016817545,0.00008594806,0.00091262086],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.9987238,0.00018888873,0.00015718752,0.00035664404,0.000380287,0.00019323814],"domain_scores_gemma":[0.98978883,0.007000323,0.0011745867,0.0011058134,0.00041393624,0.00051654276],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006650907,0.00069762213,0.0008467735,0.00031153433,0.0003397611,0.0006448169,0.0007010334,0.00093494786,0.0029283648],"category_scores_gemma":[0.010626741,0.0005157229,0.0005270088,0.00026431342,0.0006453681,0.0011071797,0.0014833326,0.0010182953,0.0002657421],"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":[0.0031286064,0.00033281549,0.0046555675,0.00018565747,0.000058440273,0.00014359102,0.00014897082,0.0011875948,0.9696613,0.0001383369,0.000108385095,0.020250654],"study_design_scores_gemma":[0.00039812748,0.011614369,0.32197532,0.00004980527,0.00036062684,0.0012897275,0.00015768659,0.012130379,0.64880073,0.0009079231,0.0022183498,0.00009689917],"about_ca_topic_score_codex":0.0012436357,"about_ca_topic_score_gemma":0.0011407667,"teacher_disagreement_score":0.0029283648,"about_ca_system_score_codex":0.00045777322,"about_ca_system_score_gemma":0.00030476841,"threshold_uncertainty_score":0.009796381},"labels":[],"label_agreement":null},{"id":"W4412634705","doi":"10.1007/978-981-96-2721-9_24","title":"Cross Modal Person Re-Identification Using HOG","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"York University","funders":"","keywords":"Identification (biology); Modal; Computer science; Biology; Materials science; Ecology; Composite material","score_opus":0.042636285412330235,"score_gpt":0.3029871463400998,"score_spread":0.2603508609277696,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412634705","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0617274,0.0012967901,0.9105111,0.00017632525,0.00062369165,0.0001761068,0.0014333387,0.007112407,0.01694281],"genre_scores_gemma":[0.4158198,0.0023771566,0.50872916,0.00052445283,0.00023623278,0.000132153,0.008087826,0.0014869907,0.06260621],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9994522,0.000046321566,0.000015893502,0.00021672201,0.00015034073,0.000118553915],"domain_scores_gemma":[0.99969375,0.0000244437,0.000017048545,0.00012688902,0.00012004586,0.000017918423],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00055563217,0.0011717343,0.001411484,0.0017472802,0.00042233776,0.0010716977,0.0007461677,0.0009560402,0.005908164],"category_scores_gemma":[0.00057176896,0.00055582897,0.0010330111,0.0015740433,0.00027094476,0.0014366638,0.0015309361,0.00086240564,0.011211723],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026999955,0.00018528309,0.0017117877,0.00011020478,0.00016811371,0.00019830524,0.000059051858,0.007884954,0.08402351,0.00087799964,0.0116928145,0.8928181],"study_design_scores_gemma":[0.000033639968,0.0003302216,0.023420017,0.000109513945,0.000320146,0.0027593125,0.00036701997,0.75921434,0.16802803,0.006736376,0.03855179,0.00012958738],"about_ca_topic_score_codex":0.0026757733,"about_ca_topic_score_gemma":0.0055118976,"teacher_disagreement_score":0.005908164,"about_ca_system_score_codex":0.00020931452,"about_ca_system_score_gemma":0.0003079042,"threshold_uncertainty_score":0.019764781},"labels":[],"label_agreement":null},{"id":"W4412781104","doi":"10.1145/3744199.3744635","title":"Automated Video Segmentation Machine Learning Pipeline","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"BGC Engineering (Canada)","funders":"","keywords":"Computer science; Pipeline (software); Segmentation; Artificial intelligence; Image segmentation; Computer vision; Machine learning; Operating system","score_opus":0.015860514340859683,"score_gpt":0.3173543592729597,"score_spread":0.3014938449321,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4412781104","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016332818,0.00015567242,0.9460119,0.00017063796,0.00010397042,0.0002573207,0.0011441285,0.03202108,0.003802582],"genre_scores_gemma":[0.11678141,0.00018687226,0.86652935,0.00020431024,0.000070629554,0.0004503121,0.005835341,0.0024621896,0.0074796258],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99936885,0.000055767607,0.00003391598,0.00026114593,0.00019704434,0.000083303516],"domain_scores_gemma":[0.99889356,0.00031763964,0.00006659041,0.00021264149,0.00044617688,0.00006339846],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008083686,0.0011107827,0.0007903889,0.0012475353,0.0006360991,0.0013300058,0.0015204918,0.00079434423,0.013588636],"category_scores_gemma":[0.0025044968,0.00052327965,0.00079509325,0.0006481833,0.00035993286,0.0009814112,0.0010438716,0.0011824697,0.0068465136],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00037869223,0.000097951524,0.0010729587,0.00017426013,0.000039259172,0.00017305811,0.00017136129,0.027957601,0.13427544,0.0038705238,0.028953038,0.80283576],"study_design_scores_gemma":[0.000041878924,0.00022492869,0.0030723838,0.00003391329,0.000035334502,0.00030779076,0.00014416016,0.75987005,0.18555018,0.007817406,0.042830016,0.000072083894],"about_ca_topic_score_codex":0.0041593537,"about_ca_topic_score_gemma":0.004478348,"teacher_disagreement_score":0.013588636,"about_ca_system_score_codex":0.000977025,"about_ca_system_score_gemma":0.0013580195,"threshold_uncertainty_score":0.045458496},"labels":[],"label_agreement":null},{"id":"W4413144267","doi":"10.1109/cvpr52734.2025.00313","title":"VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","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":"Kootenay Association for Science & Technology","funders":"National Research Foundation; Ministry of Health","keywords":"Computer science; Context (archaeology); Context model; Iterative learning control; Artificial intelligence","score_opus":0.06906311789322919,"score_gpt":0.34134463130667525,"score_spread":0.2722815134134461,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413144267","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.009114408,0.00068184384,0.97663444,0.00033801136,0.00007646312,0.00023415551,0.000546357,0.010636375,0.0017379019],"genre_scores_gemma":[0.25451967,0.0005540446,0.73048824,0.0013119333,0.00021117042,0.0007590128,0.0049082497,0.0014856918,0.0057620145],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9982828,0.00052986515,0.00007145196,0.00062948855,0.00034174407,0.00014465787],"domain_scores_gemma":[0.9974474,0.0014474526,0.00017664525,0.00037546744,0.00040261555,0.00015038282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0022770835,0.0022011434,0.0014284648,0.0015284381,0.0006445321,0.0014849763,0.0046557332,0.0025527852,0.0073833195],"category_scores_gemma":[0.012970679,0.00083478756,0.0014388682,0.00090368045,0.00081902667,0.003362208,0.003484476,0.0039192555,0.0022847056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00048628007,0.0005812982,0.003000334,0.00045928115,0.00020763387,0.00026769814,0.00041876448,0.14506336,0.012323624,0.012722932,0.026401717,0.798067],"study_design_scores_gemma":[0.000039512674,0.00008800058,0.0003366658,0.000032406504,0.000020903208,0.000057088644,0.000047725374,0.9830921,0.0033006344,0.010581635,0.0023852095,0.000018069184],"about_ca_topic_score_codex":0.009736537,"about_ca_topic_score_gemma":0.015801156,"teacher_disagreement_score":0.009736537,"about_ca_system_score_codex":0.0014695871,"about_ca_system_score_gemma":0.0015832139,"threshold_uncertainty_score":0.024699628},"labels":[],"label_agreement":null},{"id":"W4413146428","doi":"10.1109/sas65169.2025.11105183","title":"Impact of Image Resolution on Controlling Drones Using Remote VR Headset Visualization and a Cloud Architecture","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Carleton University","funders":"","keywords":"Headset; Drone; Cloud computing; Computer science; Visualization; Architecture; Computer graphics (images); Image resolution; Computer vision; Artificial intelligence; Operating system; Geography; Telecommunications","score_opus":0.022742860570720204,"score_gpt":0.37171645564225125,"score_spread":0.34897359507153103,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413146428","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":[0.98336536,0.0002703964,0.013803059,0.000066835455,0.00001161242,0.000042335894,0.000034446162,0.00027730144,0.002128672],"genre_scores_gemma":[0.9965036,0.00008123433,0.0031071806,0.000013399288,0.0000035933442,0.000006363766,0.000022252985,0.00002222463,0.00024023348],"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","domain_scores_codex":[0.99915504,0.00020400493,0.00004387797,0.00012468954,0.0002897068,0.00018276894],"domain_scores_gemma":[0.9951232,0.0032864267,0.000402608,0.0003684866,0.0006353406,0.00018385336],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007591477,0.00047792107,0.00024602682,0.00038450427,0.00049006706,0.00093425636,0.00048473425,0.0003095076,0.0020893638],"category_scores_gemma":[0.0069075893,0.00021656677,0.00019978665,0.00022820968,0.0004172939,0.0015051293,0.00054851896,0.00052974245,0.00018245427],"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":[0.00350338,0.00077067304,0.036439124,0.0006238402,0.00022097744,0.0014746608,0.0010384553,0.23101805,0.5984476,0.00282925,0.00082681194,0.1228071],"study_design_scores_gemma":[0.00015621781,0.0043350775,0.098801255,0.00008138762,0.0003142474,0.001915813,0.0013218453,0.53118163,0.35745803,0.0014353992,0.002850789,0.00014821086],"about_ca_topic_score_codex":0.0046761823,"about_ca_topic_score_gemma":0.004425045,"teacher_disagreement_score":0.0046761823,"about_ca_system_score_codex":0.0006281922,"about_ca_system_score_gemma":0.0003968963,"threshold_uncertainty_score":0.009297967},"labels":[],"label_agreement":null},{"id":"W4413150740","doi":"10.2139/ssrn.5390805","title":"First-Floor-Finder (F3) – A Robust Method Based on Deep Multi-View Feature Fusion for Automated First-Floor Height Estimation of Suburban Buildings","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Feature (linguistics); Artificial intelligence; Computer science; Estimation; Fusion; Computer vision; Pattern recognition (psychology); Engineering; Systems engineering","score_opus":0.021073848854554343,"score_gpt":0.31443756141058093,"score_spread":0.2933637125560266,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413150740","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04691805,0.00045814738,0.9474644,0.00009004094,0.00008242135,0.000050274546,0.0005478254,0.0032541251,0.0011347543],"genre_scores_gemma":[0.3604254,0.00035310214,0.63305926,0.00012786097,0.00010687698,0.000071773866,0.0023462563,0.00037920335,0.0031303095],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99948287,0.000050964718,0.000014323994,0.0001483051,0.00017309365,0.00013039888],"domain_scores_gemma":[0.9996816,0.00004940228,0.00003789748,0.00008002287,0.000114930146,0.000036236248],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004567195,0.0010788959,0.0011476136,0.0014647815,0.00034648558,0.0006678416,0.0012247728,0.0011543048,0.0017132129],"category_scores_gemma":[0.0008920766,0.00053227745,0.0011522191,0.0009803846,0.00031720806,0.0009045993,0.0012797791,0.0011520907,0.0018216409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00031788682,0.0002244074,0.0048829415,0.00014367388,0.00018308997,0.00016326562,0.00014447603,0.06356203,0.082812846,0.0014826626,0.006555391,0.8395272],"study_design_scores_gemma":[0.000017273827,0.00008615821,0.0056465776,0.000017832292,0.000042456893,0.00021137016,0.000041962016,0.9681092,0.021726806,0.0013814782,0.0026809552,0.000038077862],"about_ca_topic_score_codex":0.005503128,"about_ca_topic_score_gemma":0.010003034,"teacher_disagreement_score":0.005503128,"about_ca_system_score_codex":0.0002892703,"about_ca_system_score_gemma":0.0009832731,"threshold_uncertainty_score":0.010942161},"labels":[],"label_agreement":null},{"id":"W4413157673","doi":"10.1109/cvpr52734.2025.01531","title":"Pippo: High-Resolution Multi-View Humans from a Single Image","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; High resolution; Resolution (logic); Image (mathematics); Geology; Remote sensing","score_opus":0.03514322443802488,"score_gpt":0.31106785580773727,"score_spread":0.2759246313697124,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413157673","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.028655149,0.000559786,0.95846957,0.0003321433,0.00016964789,0.00014753791,0.0009821103,0.005988592,0.004695392],"genre_scores_gemma":[0.5720194,0.00056985137,0.40407702,0.00087131746,0.00014808863,0.00029158546,0.00511847,0.0016550649,0.015249194],"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99971837,0.00004726146,0.0000068042837,0.00011973061,0.00007305895,0.000034803375],"domain_scores_gemma":[0.99952054,0.00020300999,0.000028935197,0.00014235504,0.000053108277,0.000051951352],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006488403,0.00092920655,0.00056099275,0.0004321995,0.00025081896,0.00077281176,0.0017127793,0.0013127014,0.0069721397],"category_scores_gemma":[0.00225703,0.0007702899,0.0011030608,0.00029264844,0.0006378472,0.0010253091,0.0016292181,0.0016274545,0.0021423255],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004325624,0.00016084622,0.002662091,0.00024538778,0.00018672657,0.0004405722,0.0002555004,0.6887393,0.027346486,0.0105908895,0.019037163,0.24990247],"study_design_scores_gemma":[0.000021142301,0.000055160275,0.00037045646,0.000019268064,0.000012716335,0.00024308302,0.000012827637,0.9871137,0.0050200084,0.0037108979,0.0034074844,0.000013155151],"about_ca_topic_score_codex":0.00458414,"about_ca_topic_score_gemma":0.007941596,"teacher_disagreement_score":0.0069721397,"about_ca_system_score_codex":0.00055962475,"about_ca_system_score_gemma":0.0004551046,"threshold_uncertainty_score":0.023324132},"labels":[],"label_agreement":null},{"id":"W4413157696","doi":"10.1109/cvpr52734.2025.02124","title":"Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Geomechanica (Canada)","funders":"","keywords":"Computer science; Point (geometry); Tracking (education); Computer vision; Artificial intelligence; Computer graphics (images); Psychology","score_opus":0.026306296393614224,"score_gpt":0.2987000271639495,"score_spread":0.2723937307703353,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413157696","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.026994705,0.00024973485,0.9668191,0.00014363622,0.00008809848,0.00005340101,0.00018180678,0.0035815265,0.0018879853],"genre_scores_gemma":[0.6061931,0.00056177826,0.38344154,0.00027591488,0.00011748354,0.00013627342,0.0011040148,0.00071638776,0.0074535003],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99972266,0.000026435418,0.000011891596,0.000117554926,0.00008084449,0.000040578692],"domain_scores_gemma":[0.9995296,0.00012641649,0.000056550532,0.0001443062,0.00009714444,0.000046007055],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007609853,0.0009623712,0.0007033297,0.00052986975,0.00032880175,0.0011958276,0.0019838146,0.0008328981,0.002858715],"category_scores_gemma":[0.002046722,0.00054498285,0.00084748096,0.00058607536,0.0005230443,0.0021342258,0.0015075303,0.0012938973,0.0010113659],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005083701,0.00027420823,0.0062566837,0.00015419921,0.00021073315,0.0001657223,0.00024524916,0.44191882,0.053179502,0.010700992,0.008317155,0.4780684],"study_design_scores_gemma":[0.000009041927,0.00006274295,0.0004621156,0.000009087727,0.000021739868,0.0000412895,0.000012251987,0.98954344,0.0058938065,0.0024782312,0.0014547729,0.000011595458],"about_ca_topic_score_codex":0.012461859,"about_ca_topic_score_gemma":0.01444627,"teacher_disagreement_score":0.012461859,"about_ca_system_score_codex":0.0006216775,"about_ca_system_score_gemma":0.00094201893,"threshold_uncertainty_score":0.024778664},"labels":[],"label_agreement":null},{"id":"W4413166260","doi":"10.18280/ts.420447","title":"Visual Object Tracking Using Siam and TensorFlow as Hybrid Model in AI","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Tracking (education); Object (grammar); Computer vision; Computer science; Eye tracking; Video tracking; Pattern recognition (psychology); Psychology","score_opus":0.02744550123551685,"score_gpt":0.3316317808911721,"score_spread":0.30418627965565526,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413166260","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":[0.015997188,0.00082800415,0.9772883,0.00048587014,0.00011719519,0.000051792766,0.00023800078,0.0020340453,0.0029595804],"genre_scores_gemma":[0.46450642,0.0013351431,0.5223316,0.00034207755,0.0001556354,0.00020839709,0.0010801456,0.00029798542,0.009742552],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995933,0.00009328577,0.000023434812,0.00013091024,0.00011594333,0.00004307897],"domain_scores_gemma":[0.99926454,0.00023744174,0.000073972325,0.00012213505,0.00025058,0.000051232197],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011749946,0.0007189106,0.000640693,0.00092592905,0.000516014,0.0015969534,0.0010886592,0.00083374814,0.0022011413],"category_scores_gemma":[0.0021021022,0.00035462328,0.00083919923,0.0010196602,0.0006299579,0.0017583986,0.00092378375,0.0014573025,0.00062615064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00021009457,0.00009762128,0.0023577928,0.00015105163,0.00015100627,0.00009903648,0.00015079415,0.7321531,0.009301692,0.03016265,0.0049012993,0.22026384],"study_design_scores_gemma":[0.0000028456896,0.000015608768,0.00011543639,0.00000419613,0.0000053776293,0.000011878975,0.0000045710785,0.9931398,0.0007679019,0.0051411055,0.00078667165,0.000004531208],"about_ca_topic_score_codex":0.020224683,"about_ca_topic_score_gemma":0.015825385,"teacher_disagreement_score":0.020224683,"about_ca_system_score_codex":0.0013372011,"about_ca_system_score_gemma":0.0014489355,"threshold_uncertainty_score":0.040213943},"labels":[],"label_agreement":null},{"id":"W4413220738","doi":"10.22260/ccc2025/0059","title":"TRANSFORMING VISION-BASED INDOOR BUILT ENVIRONMENT MANAGEMENT WITH FEW-SHOT LEARNING","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Shot (pellet); Computer vision; Artificial intelligence","score_opus":0.014163113825993452,"score_gpt":0.2805690848219932,"score_spread":0.2664059709959998,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413220738","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06090191,0.00088976073,0.9290879,0.00032281535,0.00018453007,0.00013713175,0.000786813,0.0052236416,0.0024655133],"genre_scores_gemma":[0.68110603,0.000509143,0.30800578,0.00059120083,0.00016136596,0.0002181868,0.0045617465,0.00038192281,0.004464698],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9993349,0.00007610154,0.000024478213,0.00031503645,0.00012403388,0.00012550446],"domain_scores_gemma":[0.9994081,0.00018461721,0.00007077988,0.00010523977,0.00016675975,0.00006447582],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006004534,0.00133105,0.001302196,0.0014821663,0.0004158698,0.0010750198,0.0024637673,0.0013518744,0.0012585135],"category_scores_gemma":[0.0020415462,0.0007170782,0.0010451579,0.0009317749,0.0006554133,0.0016250607,0.0019421598,0.0015752744,0.0009270408],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00028022038,0.00045121906,0.0048255227,0.00032952725,0.00017246846,0.0003075451,0.00027546086,0.34853506,0.023095742,0.0029199733,0.010319193,0.608488],"study_design_scores_gemma":[0.000005861243,0.00003445678,0.0006410731,0.000012730713,0.000012554887,0.000044814635,0.000033842218,0.9930443,0.0031576096,0.002255691,0.00074696325,0.0000101152145],"about_ca_topic_score_codex":0.013192407,"about_ca_topic_score_gemma":0.019614136,"teacher_disagreement_score":0.013192407,"about_ca_system_score_codex":0.0009490625,"about_ca_system_score_gemma":0.0009782773,"threshold_uncertainty_score":0.02623123},"labels":[],"label_agreement":null},{"id":"W4413277986","doi":"10.1109/icip55913.2025.11084467","title":"Depth-Aware Scoring and Hierarchical Alignment for Multiple Object Tracking","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Tracking (education); Object (grammar)","score_opus":0.03637818609467472,"score_gpt":0.32525487849051604,"score_spread":0.28887669239584135,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413277986","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.011568677,0.00050739566,0.9793792,0.000066549284,0.000056466022,0.00010303385,0.00027322938,0.0063106497,0.0017348455],"genre_scores_gemma":[0.266769,0.00028713106,0.7255313,0.00015161048,0.00008348519,0.00019107726,0.0019666809,0.0007812605,0.004238424],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9976325,0.00033302873,0.0001168098,0.0008117205,0.00083585153,0.00027009845],"domain_scores_gemma":[0.99809605,0.0005148312,0.00022594514,0.000526812,0.0004914538,0.00014495586],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015159256,0.0015142305,0.0018844877,0.0020174426,0.0009327551,0.0014534115,0.0033449794,0.0013985817,0.00548489],"category_scores_gemma":[0.006177657,0.000779535,0.0009618853,0.0023252356,0.0005528616,0.002150278,0.0032614358,0.0019078754,0.0031774472],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00027699224,0.00021090193,0.003874235,0.00019379915,0.0001325085,0.00008605306,0.00015335585,0.10615352,0.016796077,0.008462951,0.010003068,0.85365665],"study_design_scores_gemma":[0.0000211756,0.0000665758,0.0012004287,0.000017697937,0.00002379686,0.00011367374,0.000033071545,0.9809661,0.0073455647,0.007427592,0.0027615924,0.000022799431],"about_ca_topic_score_codex":0.011212225,"about_ca_topic_score_gemma":0.020777965,"teacher_disagreement_score":0.011212225,"about_ca_system_score_codex":0.0012442878,"about_ca_system_score_gemma":0.0017766072,"threshold_uncertainty_score":0.022293925},"labels":[],"label_agreement":null},{"id":"W4413859685","doi":"10.1007/978-981-96-4273-1_8","title":"Object Detection on Complex Architectural Floor Plans with Efficient Attention Mechanisms","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Object (grammar); Artificial intelligence; Cognitive science; Architectural engineering; Human–computer interaction; Computer vision; Engineering; Psychology","score_opus":0.017429295414599835,"score_gpt":0.2397549681788687,"score_spread":0.22232567276426887,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413859685","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.10736932,0.00036836605,0.88676727,0.000091903144,0.000051213792,0.000046860143,0.000103086466,0.0016844413,0.003517589],"genre_scores_gemma":[0.68712306,0.0004290306,0.30635813,0.000095530224,0.00007057196,0.00004685302,0.00036171128,0.00020619774,0.0053089457],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99978274,0.000027573504,0.000006537587,0.000063288644,0.000061229766,0.000058549846],"domain_scores_gemma":[0.99973685,0.00012346242,0.000020949583,0.000043375483,0.000054076056,0.000021184605],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00026696766,0.00062561495,0.0007862475,0.0007508089,0.00022953076,0.00087128015,0.0010914525,0.0005979158,0.0025627557],"category_scores_gemma":[0.0008929219,0.0005235561,0.00052946236,0.00083603576,0.0003177868,0.0008980921,0.0010090638,0.00047734377,0.00070095825],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004173486,0.00010012221,0.00172516,0.00012516341,0.00007822433,0.00021339145,0.0001215242,0.15442042,0.08962058,0.004107268,0.0037613013,0.7453094],"study_design_scores_gemma":[0.0000060925245,0.000035322973,0.001240667,0.000004795742,0.000013166167,0.000059161168,0.000019906707,0.9905782,0.0058974773,0.0016892349,0.00045026012,0.000005774572],"about_ca_topic_score_codex":0.0069850697,"about_ca_topic_score_gemma":0.008814662,"teacher_disagreement_score":0.0069850697,"about_ca_system_score_codex":0.00041345067,"about_ca_system_score_gemma":0.00043653412,"threshold_uncertainty_score":0.013888836},"labels":[],"label_agreement":null},{"id":"W4413983003","doi":"10.2139/ssrn.5440547","title":"A Deep Dive into Generic Object Tracking: A Survey","year":2025,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Victoria","funders":"","keywords":"Tracking (education); Computer science; Object (grammar); Artificial intelligence; Computer vision; Psychology","score_opus":0.0308127199091336,"score_gpt":0.3127418006977451,"score_spread":0.2819290807886115,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4413983003","genre_codex":"methods","genre_gemma":"review","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"review","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.008672238,0.3188451,0.6532439,0.0028175327,0.0005704166,0.00010152278,0.00031158424,0.0009546824,0.014483016],"genre_scores_gemma":[0.1436258,0.4982853,0.33678392,0.003460139,0.0031676863,0.0001883259,0.0023441508,0.00080363994,0.011341102],"study_design_codex":"design_other","study_design_gemma":"not_applicable","domain_scores_codex":[0.99668103,0.000617773,0.00024651413,0.0014480284,0.000845739,0.00016091911],"domain_scores_gemma":[0.9898761,0.0061617442,0.0003932238,0.0019615146,0.0013206064,0.00028692602],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005033006,0.0014572927,0.0029838672,0.0030763142,0.0009846658,0.0038673955,0.0035486727,0.0032801211,0.0034687796],"category_scores_gemma":[0.01355532,0.0016160604,0.0015494366,0.0066077732,0.0020124307,0.008110327,0.0027677326,0.002383399,0.00301743],"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":[0.00010392041,0.00011177501,0.0027859653,0.0023295546,0.0001281844,0.000053002284,0.00022150412,0.010067582,0.0021267976,0.038117375,0.01056443,0.93338996],"study_design_scores_gemma":[0.000053136297,0.00076318515,0.010962101,0.0029002388,0.00037831382,0.0023843274,0.00075359707,0.23535828,0.008620954,0.21008642,0.52753377,0.00020577808],"about_ca_topic_score_codex":0.0038987852,"about_ca_topic_score_gemma":0.0021950337,"teacher_disagreement_score":0.005033006,"about_ca_system_score_codex":0.0013586462,"about_ca_system_score_gemma":0.0019585402,"threshold_uncertainty_score":0.026617348},"labels":[],"label_agreement":null},{"id":"W4414198623","doi":"10.1109/tsmc.2025.3604832","title":"CoMix: Collaborative Mixed Learning via Style Fuzzy Normalization for Visible–Infrared Person Re-Identification","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Systems Man and Cybernetics Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Normalization (sociology); Fuzzy logic; Modality (human–computer interaction); Consistency (knowledge bases); Discriminative model; Focus (optics); Matching (statistics); Feature vector; Modalities","score_opus":0.018872313914828964,"score_gpt":0.2697352387531869,"score_spread":0.2508629248383579,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414198623","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.01588886,0.00053905084,0.9763075,0.00015796031,0.00015865055,0.000105100095,0.00022597723,0.0037720946,0.0028447588],"genre_scores_gemma":[0.47575548,0.0005117284,0.49646127,0.00089164195,0.00025883,0.00029252307,0.002162949,0.0007125711,0.022952933],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99842715,0.00037002147,0.000035584464,0.00065764505,0.0003602795,0.00014934488],"domain_scores_gemma":[0.9989967,0.00026024977,0.000096917145,0.00039129678,0.00018502846,0.0000698062],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021156983,0.0016369271,0.001395119,0.00093693024,0.000604222,0.0009092461,0.0032555952,0.0015468458,0.0038696593],"category_scores_gemma":[0.0035938963,0.0005499765,0.0014827056,0.0007567288,0.00093327736,0.0018872909,0.002360533,0.0021542339,0.0028119725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005836161,0.0003475298,0.0022751603,0.00013776407,0.00027934593,0.0002549319,0.00018974439,0.2310841,0.017339634,0.008100838,0.012534677,0.7268727],"study_design_scores_gemma":[0.00001872812,0.00009853664,0.0004771073,0.000012214837,0.000024214549,0.00017047912,0.000024887557,0.9830445,0.0071208817,0.006296195,0.0026851292,0.000027069913],"about_ca_topic_score_codex":0.00410043,"about_ca_topic_score_gemma":0.0062283217,"teacher_disagreement_score":0.00410043,"about_ca_system_score_codex":0.00075339264,"about_ca_system_score_gemma":0.0007998004,"threshold_uncertainty_score":0.012945294},"labels":[],"label_agreement":null},{"id":"W4414405074","doi":"10.1109/iccworkshops67674.2025.11162477","title":"Visible Light Passive Indoor Tracking Using Background Subtraction","year":2025,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"McMaster University","funders":"","keywords":"Background subtraction; Tracking (education); Kalman filter; Moving target indication; Position (finance); Object detection; Impulse response; Radar tracker; Tracking system","score_opus":0.04779348385111693,"score_gpt":0.3451152234417309,"score_spread":0.297321739590614,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414405074","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.016880006,0.00014844477,0.9792346,0.000023238923,0.00003795414,0.000016535158,0.000028260694,0.0013512017,0.0022798213],"genre_scores_gemma":[0.6482458,0.0004822177,0.34415868,0.000106978696,0.000059533977,0.000083996325,0.00030174138,0.00019894744,0.0063620573],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.9995764,0.00006560597,0.000012075366,0.000117203264,0.00018447914,0.00004426756],"domain_scores_gemma":[0.99975365,0.00006496412,0.00003872199,0.000042115982,0.00008490115,0.000015648635],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003300838,0.0006203064,0.00063449057,0.00054014777,0.00030531661,0.00076895795,0.001038546,0.0005658387,0.00076566794],"category_scores_gemma":[0.00059425674,0.00030909388,0.0005519536,0.00050036283,0.0003027649,0.0006751643,0.00079838524,0.00043941257,0.00071312697],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004731447,0.00017608191,0.0039903848,0.00037986424,0.00012269824,0.00026685756,0.00026638477,0.18902732,0.26568753,0.007698908,0.0023918822,0.5295189],"study_design_scores_gemma":[0.000034858625,0.0002969365,0.003612595,0.00002933485,0.0000726476,0.00043739757,0.00004427082,0.8716001,0.1141555,0.0016256585,0.008028789,0.00006197579],"about_ca_topic_score_codex":0.002290368,"about_ca_topic_score_gemma":0.0019360672,"teacher_disagreement_score":0.002290368,"about_ca_system_score_codex":0.0003155822,"about_ca_system_score_gemma":0.0004973412,"threshold_uncertainty_score":0.004554093},"labels":[],"label_agreement":null},{"id":"W4414498470","doi":"10.1016/j.comgeo.2025.102230","title":"City guarding with cameras of bounded field of view","year":2025,"lang":"en","type":"article","venue":"Computational Geometry","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Conjecture; Bounded function; Guard (computer science); Constructive; Regular polygon; Mathematical proof; Space (punctuation)","score_opus":0.016446833491829974,"score_gpt":0.32199638321835605,"score_spread":0.3055495497265261,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4414498470","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0788121,0.00023661809,0.91571164,0.00017345064,0.000056101377,0.000057915353,0.00026158788,0.00039793234,0.004292715],"genre_scores_gemma":[0.75571185,0.0003367036,0.23572746,0.00007667805,0.000054294225,0.00007448488,0.0007730473,0.00013037556,0.0071150945],"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.99881953,0.00035640536,0.000039217415,0.0003726047,0.00021056744,0.00020180765],"domain_scores_gemma":[0.9981375,0.0008783604,0.0002603105,0.00037002208,0.00015836056,0.00019543996],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008835302,0.001149357,0.001640365,0.0007804021,0.000979123,0.001648837,0.0024466107,0.0019715128,0.002689168],"category_scores_gemma":[0.0043636705,0.001084324,0.0009833177,0.0013552016,0.0015414027,0.002561609,0.0034303162,0.0014786477,0.0005529375],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0007045878,0.00009329995,0.0022919588,0.00011213733,0.000069165566,0.0004287228,0.00026039436,0.9102367,0.0040706103,0.028802237,0.0032115309,0.04971861],"study_design_scores_gemma":[0.000014687819,0.000028387809,0.00024939698,0.000008157464,0.000007505267,0.000046835332,0.000053908578,0.9912196,0.0008090027,0.0069646747,0.00059003563,0.000007838928],"about_ca_topic_score_codex":0.022138804,"about_ca_topic_score_gemma":0.015638605,"teacher_disagreement_score":0.022138804,"about_ca_system_score_codex":0.00095512473,"about_ca_system_score_gemma":0.0011300246,"threshold_uncertainty_score":0.044019878},"labels":[],"label_agreement":null},{"id":"W4415214196","doi":"10.1016/j.neunet.2025.108206","title":"Stochastic style perturbation modelling for visible-Infrared person re-Identification with severely modality imbalance","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Modality (human–computer interaction); Discriminative model; Feature learning; Consistency (knowledge bases); Modalities; Feature vector; Contrast (vision); Perturbation (astronomy); Synthetic data","score_opus":0.031000367150892192,"score_gpt":0.28358394630298,"score_spread":0.2525835791520878,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415214196","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.04469456,0.00039246565,0.9530125,0.00019116871,0.00013270958,0.00003668539,0.00013888374,0.0002889861,0.0011120791],"genre_scores_gemma":[0.92311907,0.00048046425,0.065469265,0.0002144409,0.00018469483,0.00010349726,0.0005339268,0.00010930656,0.009785398],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99946433,0.00015367565,0.00002314136,0.00016745809,0.0001128369,0.000078579644],"domain_scores_gemma":[0.9991385,0.00043363334,0.000106666404,0.00010997147,0.00016129065,0.00004988619],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014218532,0.00080924865,0.0010019023,0.0003810661,0.0003090285,0.0006453604,0.0011505061,0.0012093828,0.0010685556],"category_scores_gemma":[0.003745127,0.0005681485,0.00091816427,0.0005750583,0.0006144128,0.001048764,0.0010818116,0.0015753988,0.0006269257],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00043193903,0.00014965251,0.002320762,0.00009134953,0.00012535228,0.00012477156,0.00011152732,0.8694593,0.010152234,0.0062025865,0.0018538759,0.10897664],"study_design_scores_gemma":[0.0000014611456,0.000009655838,0.0002853822,0.0000022690485,0.0000040819587,0.000008193146,0.0000027548572,0.998623,0.0003043103,0.00067776046,0.00007811066,0.0000030762562],"about_ca_topic_score_codex":0.00631886,"about_ca_topic_score_gemma":0.006295239,"teacher_disagreement_score":0.00631886,"about_ca_system_score_codex":0.0005906507,"about_ca_system_score_gemma":0.0004558342,"threshold_uncertainty_score":0.012564182},"labels":[],"label_agreement":null},{"id":"W4415501412","doi":"10.1038/s41598-025-21033-2","title":"Object state optimization algorithm based on Bayesian random sampling for visual object tracking","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Video Surveillance and Tracking Methods","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Object (grammar); Minimum bounding box; Video tracking; Bayesian probability; Pattern recognition (psychology); Bounding overwatch; Pose; State (computer science); Sampling (signal processing)","score_opus":0.02249189560670337,"score_gpt":0.32739075180066657,"score_spread":0.3048988561939632,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415501412","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":[0.0053512063,0.00011608781,0.99373055,0.000053610183,0.0000149019825,0.000024706103,0.000013034632,0.00032229887,0.00037357584],"genre_scores_gemma":[0.48351714,0.00031133328,0.51225364,0.0002183184,0.00007328797,0.0003476418,0.00037263543,0.00020888625,0.0026971775],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99891376,0.00029757447,0.000058881462,0.0002724428,0.00035727464,0.000100212244],"domain_scores_gemma":[0.9984627,0.0008408555,0.00017458956,0.00009227525,0.00035806544,0.00007160107],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019849222,0.0009179246,0.0018187655,0.0010405474,0.0004895887,0.0009921518,0.0017599846,0.0011705941,0.0013847064],"category_scores_gemma":[0.0048283767,0.0007203549,0.000969775,0.00087180256,0.0006951745,0.0011961602,0.0011866834,0.0012627731,0.00048154758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0001501806,0.0000675267,0.0011105096,0.000063415384,0.00005550285,0.00004812312,0.00007728873,0.87620556,0.0034581386,0.008935829,0.0010565568,0.1087713],"study_design_scores_gemma":[0.0000039138963,0.0000071699396,0.000041791245,0.0000018357154,0.0000021514536,0.000003869088,0.0000011337775,0.99920136,0.00019969628,0.00046148867,0.00007333635,0.000002289479],"about_ca_topic_score_codex":0.011041836,"about_ca_topic_score_gemma":0.007700255,"teacher_disagreement_score":0.011041836,"about_ca_system_score_codex":0.00112351,"about_ca_system_score_gemma":0.0016293477,"threshold_uncertainty_score":0.021955132},"labels":[],"label_agreement":null},{"id":"W4416726607","doi":"10.1109/mwscas53549.2025.11244452","title":"Real-Time Vehicle Speed Detection Using Existing Traffic Camera Infrastructure with OpenGL ES","year":2025,"lang":"","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Windsor","funders":"","keywords":"OpenGL; Key (lock); Pixel; Code (set theory); Raspberry pi; Track (disk drive); Object (grammar); Image processing","score_opus":0.026409376404653583,"score_gpt":0.30602067323899,"score_spread":0.27961129683433644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416726607","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.33315897,0.00016414563,0.62993085,0.00012162676,0.00008424342,0.0001458952,0.00038731168,0.026255101,0.009751763],"genre_scores_gemma":[0.75390524,0.00012028885,0.24150306,0.0000630641,0.000021239211,0.000076675075,0.0005035089,0.00074388535,0.0030631665],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995863,0.000029360686,0.000010471153,0.00008097134,0.00024257453,0.000050269966],"domain_scores_gemma":[0.99977416,0.000032916894,0.000025351492,0.000044428165,0.00010019291,0.000022894736],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00021547821,0.00049005466,0.00042551127,0.001102993,0.00020174742,0.0006699294,0.0007626765,0.00030268094,0.0024742468],"category_scores_gemma":[0.00087995623,0.00027415677,0.0002392834,0.0006134038,0.0002050632,0.0005741896,0.00056445866,0.00035750636,0.0006784323],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0005175034,0.00027438815,0.011577867,0.0001977754,0.000071918774,0.00030300772,0.0005096834,0.02338665,0.28455913,0.0019547658,0.0063505396,0.6702967],"study_design_scores_gemma":[0.00007872072,0.0003781998,0.023990087,0.00004241245,0.000058630074,0.0004580859,0.00015047134,0.74061036,0.21846972,0.0013820325,0.014293346,0.00008791475],"about_ca_topic_score_codex":0.0042213486,"about_ca_topic_score_gemma":0.0066095013,"teacher_disagreement_score":0.0042213486,"about_ca_system_score_codex":0.0005008188,"about_ca_system_score_gemma":0.0006318059,"threshold_uncertainty_score":0.008393586},"labels":[],"label_agreement":null},{"id":"W4416749162","doi":"10.1109/iros60139.2025.11246179","title":"Personalized Re-identification through Unsupervised Continual Learning and Parallel Training","year":2025,"lang":"","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Baseline (sea); Robotics; Tracking (education); Training (meteorology); Artificial neural network; Unsupervised learning; Deep learning; Object (grammar); Conjunction (astronomy)","score_opus":0.05842267128729868,"score_gpt":0.3500837630743571,"score_spread":0.29166109178705846,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4416749162","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06653157,0.00023510994,0.9275925,0.00016666688,0.00005190356,0.00011389098,0.00004610485,0.002291999,0.0029701914],"genre_scores_gemma":[0.7648153,0.00015625755,0.22817892,0.00021893447,0.0000664099,0.0001330643,0.00018149821,0.000228087,0.006021649],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99917054,0.00013284912,0.000031130294,0.00039824157,0.00016494899,0.00010229606],"domain_scores_gemma":[0.99798286,0.0005104457,0.00020587575,0.000861284,0.00034645337,0.00009305469],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0015255569,0.0010253233,0.0009910417,0.00067110354,0.0006042298,0.0006229206,0.0026512255,0.0010432155,0.0015043118],"category_scores_gemma":[0.0042213243,0.0006122531,0.00061440456,0.00070372864,0.0010654889,0.0022033134,0.0017816527,0.0017480454,0.00089086266],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00022489826,0.00052488834,0.0040800124,0.00008307173,0.00011677903,0.00015044358,0.00035905736,0.34428796,0.029850258,0.003928562,0.0030471887,0.613347],"study_design_scores_gemma":[0.000010206101,0.00009736432,0.0011190518,0.000007824438,0.000017283817,0.00011221419,0.000041072264,0.9870859,0.0067658946,0.0034947335,0.0012310392,0.000017351562],"about_ca_topic_score_codex":0.005032445,"about_ca_topic_score_gemma":0.01034756,"teacher_disagreement_score":0.005032445,"about_ca_system_score_codex":0.00072896265,"about_ca_system_score_gemma":0.00081338873,"threshold_uncertainty_score":0.010006309},"labels":[],"label_agreement":null},{"id":"W4417279024","doi":"10.5194/ica-abs-10-104-2025","title":"Waterloo Urban Scene Dataset: An Annotation-Efficient Dataset for Urban Scene Classification with Minimal Supervision","year":2025,"lang":"en","type":"article","venue":"Abstracts of the ICA","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Waterloo","funders":"University of Waterloo","keywords":"Urban planning; Field (mathematics); Feature (linguistics); Identification (biology)","score_opus":0.03222426086323529,"score_gpt":0.31304296638024853,"score_spread":0.28081870551701327,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417279024","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.02751747,0.0016992374,0.017943956,0.00058534805,0.00026114652,0.0005780251,0.92203975,0.019547185,0.00982786],"genre_scores_gemma":[0.012645954,0.00024393923,0.013052401,0.00009692005,0.000024603482,0.00023905362,0.9712233,0.00042900746,0.002044817],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9988263,0.00010476036,0.00007509883,0.00042068836,0.00036729398,0.00020577051],"domain_scores_gemma":[0.999087,0.00011733249,0.00006774525,0.00028296458,0.00034292342,0.00010202553],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005703334,0.0029621497,0.0014960214,0.0036875515,0.0013924337,0.0014478121,0.0038254191,0.0016920222,0.012073664],"category_scores_gemma":[0.0023652485,0.0007498605,0.0015383052,0.004748761,0.0008641013,0.001438082,0.0021860807,0.0017259611,0.012581767],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0002545361,0.00027364964,0.003976083,0.00095556927,0.00019412681,0.00027900698,0.00013351253,0.0038351812,0.0053872555,0.0011054158,0.91829157,0.0653141],"study_design_scores_gemma":[0.000606823,0.00023083815,0.04357993,0.0005511819,0.0002646638,0.0010654514,0.0008920758,0.069555186,0.01747244,0.0048283627,0.8606836,0.00026933078],"about_ca_topic_score_codex":0.19310705,"about_ca_topic_score_gemma":0.4210419,"teacher_disagreement_score":0.80689293,"about_ca_system_score_codex":0.0024662742,"about_ca_system_score_gemma":0.0031899135,"threshold_uncertainty_score":0.38396615},"labels":[],"label_agreement":null},{"id":"W4417280194","doi":"10.1080/01969722.2025.2590761","title":"Mobile-Le Harmonic Fusion Network for Object Recognition and SiamMoT Based Multi-Object Tracking Using Video Surveillance","year":2025,"lang":"en","type":"article","venue":"Cybernetics & Systems","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Tracking (education); Video tracking; Object (grammar); Cognitive neuroscience of visual object recognition; Sensor fusion; Artificial neural network; Pattern recognition (psychology)","score_opus":0.05726045120970017,"score_gpt":0.30516595167892735,"score_spread":0.24790550046922719,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417280194","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.12636748,0.0013452079,0.8591767,0.0004028341,0.00027467858,0.0001351922,0.0005022144,0.0053479443,0.006447737],"genre_scores_gemma":[0.7849898,0.0007120012,0.19914706,0.00028386916,0.00010837754,0.00012607867,0.0023744071,0.0001389472,0.012119567],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996592,0.000039215964,0.000015302106,0.00013241966,0.000103463666,0.000050523708],"domain_scores_gemma":[0.9997825,0.000037688067,0.000024137531,0.000036821348,0.00009952018,0.000019278412],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006738003,0.00082241185,0.0007544556,0.0012497475,0.00043850596,0.0007366152,0.0010521184,0.0006944163,0.0013752681],"category_scores_gemma":[0.0008861619,0.0002441362,0.00087724876,0.00081913633,0.00032304172,0.0011226337,0.0008777647,0.00075333385,0.00060074904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0003711787,0.0003025381,0.0048957434,0.00008125752,0.00019581393,0.00019317563,0.000078039964,0.20144199,0.023937546,0.004037856,0.0067982264,0.7576666],"study_design_scores_gemma":[0.0000053982185,0.00007834519,0.0010239043,0.0000047677336,0.000020472558,0.000054306798,0.000011872577,0.99093926,0.0057830657,0.0008670103,0.001202126,0.000009521949],"about_ca_topic_score_codex":0.012297305,"about_ca_topic_score_gemma":0.012225181,"teacher_disagreement_score":0.012297305,"about_ca_system_score_codex":0.00092242094,"about_ca_system_score_gemma":0.0008994888,"threshold_uncertainty_score":0.024451435},"labels":[],"label_agreement":null},{"id":"W4417284254","doi":"10.1109/lra.2025.3643272","title":"Low-Light Amodal Objects Tracking: A Benchmark","year":2025,"lang":"","type":"article","venue":"IEEE Robotics and Automation Letters","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Amodal perception; Benchmark (surveying); Object detection; Bounding overwatch; Video tracking; Object (grammar); Minimum bounding box; Metric (unit)","score_opus":0.013562384411396703,"score_gpt":0.2691983522466626,"score_spread":0.25563596783526593,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417284254","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":[0.52348816,0.037949346,0.20589258,0.0014113568,0.004279605,0.0026451668,0.0967067,0.085238114,0.042389043],"genre_scores_gemma":[0.49460065,0.004418114,0.1821159,0.00094242,0.00045066178,0.0007602239,0.30539036,0.0021693269,0.009152385],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99528027,0.00054619135,0.00042358963,0.0019403014,0.0013293802,0.0004803256],"domain_scores_gemma":[0.9965838,0.0011862927,0.00033846556,0.0007875934,0.0008093993,0.00029435157],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0036115595,0.0036720412,0.0019750183,0.0037902978,0.0015828363,0.003056423,0.0038945673,0.0033752539,0.0030784148],"category_scores_gemma":[0.010256862,0.00055087544,0.0015305767,0.003754568,0.00096694357,0.0025894444,0.0026801252,0.0016341684,0.0033769798],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.003577722,0.001961378,0.026024437,0.0044485657,0.0013619876,0.0008080051,0.0002964329,0.171043,0.025378706,0.0028534944,0.15002443,0.61222184],"study_design_scores_gemma":[0.0006655925,0.0021041587,0.031316567,0.00064628525,0.00048589392,0.0022019777,0.0004150114,0.8437801,0.043499973,0.0055054147,0.06915806,0.00022100427],"about_ca_topic_score_codex":0.031098124,"about_ca_topic_score_gemma":0.03964077,"teacher_disagreement_score":0.031098124,"about_ca_system_score_codex":0.0018208849,"about_ca_system_score_gemma":0.0020746032,"threshold_uncertainty_score":0.061834216},"labels":[],"label_agreement":null},{"id":"W4417313957","doi":"10.23977/jaip.2025.080404","title":"NF-Net: Crowd Counting Based on Near-Far Network and Dynamic Dual Attention Mechanism","year":2025,"lang":"","type":"article","venue":"Journal of Artificial Intelligence Practice","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"Henan Provincial Science and Technology Research Project; Henan University","keywords":"Discriminative model; Key (lock); Dual (grammatical number); Noise (video); Perspective (graphical); Curse of dimensionality; Fusion mechanism; Feature extraction; Feature (linguistics)","score_opus":0.03643556909506318,"score_gpt":0.35666828163932757,"score_spread":0.3202327125442644,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4417313957","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.05460645,0.0006034093,0.9374598,0.00034108953,0.00020560142,0.000093360184,0.0001952809,0.0014175143,0.005077597],"genre_scores_gemma":[0.8849274,0.0005069422,0.105491474,0.00028626126,0.00018658578,0.00014337372,0.00035049254,0.00013383849,0.007973598],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99951637,0.0000645543,0.000015379237,0.00019447456,0.000117709795,0.000091558904],"domain_scores_gemma":[0.999471,0.00017515414,0.000066838984,0.00005055145,0.00016343735,0.000072942035],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007602858,0.0012274612,0.0011891542,0.0013248287,0.00077671884,0.00093814376,0.002646849,0.0011075035,0.0018456619],"category_scores_gemma":[0.0019547932,0.00047782244,0.00078002695,0.0008147462,0.0007714248,0.0023696271,0.0021284453,0.0009426155,0.00043760505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000471076,0.00023465071,0.005684811,0.00015519149,0.00015010206,0.0004328391,0.00027660123,0.66136724,0.012162011,0.019515377,0.008619863,0.29093024],"study_design_scores_gemma":[0.000006488472,0.000027500173,0.00043628688,0.0000064473543,0.000017530758,0.000053331478,0.000015693337,0.9934382,0.0012485769,0.004101218,0.0006365924,0.000012116675],"about_ca_topic_score_codex":0.011448871,"about_ca_topic_score_gemma":0.00910573,"teacher_disagreement_score":0.011448871,"about_ca_system_score_codex":0.0014211702,"about_ca_system_score_gemma":0.0009197307,"threshold_uncertainty_score":0.022764444},"labels":[],"label_agreement":null},{"id":"W68432310","doi":"10.1007/978-3-642-21593-3_40","title":"Multi-camera Relay Tracker Utilizing Color-Based Particle Filtering","year":2011,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer vision; Computer science; Artificial intelligence; Zoom; Particle filter; Object (grammar); Camera auto-calibration; Video tracking; Tracking (education); Relay; Tracking system; Smart camera; Computer graphics (images); Camera resectioning; Kalman filter; Engineering","score_opus":0.06835402357566918,"score_gpt":0.2965861012071847,"score_spread":0.22823207763151554,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W68432310","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.004676393,0.00022261118,0.992093,0.0000274472,0.00010402002,0.000022730106,0.00002600027,0.0012470528,0.001580676],"genre_scores_gemma":[0.23693852,0.0006411007,0.7530087,0.00007048635,0.00011890835,0.000087210676,0.0002117154,0.00019909376,0.008724172],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99947506,0.000059042337,0.000020267025,0.00019475436,0.00021741282,0.00003349233],"domain_scores_gemma":[0.9995314,0.00009114602,0.000055121443,0.00009940244,0.00019156338,0.00003126557],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00066244457,0.00094551186,0.0014533215,0.0009943225,0.00045424228,0.0013178318,0.0013731008,0.0012708349,0.0019670676],"category_scores_gemma":[0.0010486066,0.0005199465,0.0007693245,0.0011414672,0.00029342293,0.001324958,0.00095527637,0.0009467901,0.0023110074],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006395197,0.00020515152,0.0019072013,0.00024656882,0.00017740726,0.00031109262,0.00022679486,0.08664831,0.17715819,0.009840745,0.0072213374,0.7154176],"study_design_scores_gemma":[0.000032603864,0.00013603095,0.0010501413,0.000012534716,0.00007164698,0.00030205614,0.000020824487,0.94917667,0.042217053,0.0013106144,0.0056347735,0.000034948367],"about_ca_topic_score_codex":0.002035733,"about_ca_topic_score_gemma":0.0021058857,"teacher_disagreement_score":0.002035733,"about_ca_system_score_codex":0.0005112521,"about_ca_system_score_gemma":0.00062159484,"threshold_uncertainty_score":0.0065805316},"labels":[],"label_agreement":null},{"id":"W6950814809","doi":"10.5683/sp3/ikxgxr","title":"Wingham Ontario. 1:50,000. Map Sheet 040P14, ed. 2, 1972","year":2021,"lang":"en","type":"dataset","venue":"Borealis","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Georeference; General partnership; Raster graphics; Natural (archaeology); Digital mapping; Geographic information system; Aerial photography; Orthophoto","score_opus":0.02427031722384168,"score_gpt":0.28272668085738706,"score_spread":0.2584563636335454,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6950814809","genre_codex":"dataset","genre_gemma":"dataset","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"dataset","genre_consensus":"dataset","domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.000074805066,0.000067288005,0.000036597812,0.00002440499,0.00001285106,0.000005790156,0.9977222,0.00014833089,0.0019075911],"genre_scores_gemma":[0.0004390768,0.00014415395,0.00017340512,0.000018916966,0.000006133023,0.000031206775,0.9938286,0.000099848054,0.0052585904],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993129,0.000036215355,0.000047788955,0.00017230122,0.00027833917,0.0001525375],"domain_scores_gemma":[0.9981875,0.00014440026,0.00014167282,0.00026135368,0.001046413,0.0002186194],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00042107864,0.0020139655,0.0013887006,0.004240562,0.001360117,0.0026939986,0.0017015549,0.0006326706,0.13899782],"category_scores_gemma":[0.002762276,0.0009360112,0.0006168699,0.017021215,0.0006122619,0.0010964997,0.001000138,0.0009324521,0.15895692],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000016473006,0.0000033737242,0.000469313,0.0002182199,0.0000067114042,0.000010735353,0.000026762205,0.00006104678,0.000043301865,0.00014910114,0.99627316,0.002721745],"study_design_scores_gemma":[0.00003153697,0.000003308715,0.0074549927,0.000116927135,0.000008138968,0.000021319656,0.00009605889,0.00009605339,0.00011613819,0.00018772528,0.9918549,0.000012906716],"about_ca_topic_score_codex":0.82587546,"about_ca_topic_score_gemma":0.91432667,"teacher_disagreement_score":0.17412454,"about_ca_system_score_codex":0.007249162,"about_ca_system_score_gemma":0.01213776,"threshold_uncertainty_score":0.464994},"labels":[],"label_agreement":null},{"id":"W6980590187","doi":"","title":"Climate action among Generation Z: The association between ingroup identification, collective efficacy, and collective action intentions and behaviour","year":2021,"lang":"en","type":"dissertation","venue":"UVic’s Research and Learning Repository (University of Victoria)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Collective action; Identification (biology); Action (physics); Ingroups and outgroups; Social identity theory; Identity (music); Social group","score_opus":0.05524007751737237,"score_gpt":0.3322412496252795,"score_spread":0.27700117210790715,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W6980590187","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":[0.9994578,0.000045240366,0.000029428364,0.00004512562,0.000003975508,0.0000053425956,0.000025650525,6.292718e-7,0.0003868182],"genre_scores_gemma":[0.99957293,0.000045102806,0.00004933975,0.0000187356,0.000002488369,0.0000065698996,0.0000529361,6.288176e-7,0.0002512504],"study_design_codex":"observational","study_design_gemma":"observational","domain_scores_codex":[0.9997273,0.000060743936,0.000015111712,0.000054624903,0.000045603836,0.000096505755],"domain_scores_gemma":[0.9987632,0.00022136538,0.0004892782,0.000097814205,0.00011105069,0.00031735146],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010737103,0.00023448924,0.0001939441,0.0005455932,0.0008418382,0.0011393678,0.0003172244,0.0003669684,0.0025853072],"category_scores_gemma":[0.0021422459,0.00019003061,0.00044449262,0.00034263462,0.00059334555,0.00063839206,0.0013879039,0.00087276415,0.00021263842],"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":[0.000050825303,0.0001024913,0.9933629,0.000011753023,0.000039332772,0.000035742643,0.0029129875,0.000017527605,0.000119738674,0.00014146442,0.0000944231,0.0031108705],"study_design_scores_gemma":[0.0000027743122,0.00005341274,0.99540365,0.0000120154655,0.000017268394,0.000027389917,0.003991996,0.00011218347,0.000044492113,0.00008535542,0.0002461604,0.0000033320587],"about_ca_topic_score_codex":0.029245248,"about_ca_topic_score_gemma":0.044127624,"teacher_disagreement_score":0.029245248,"about_ca_system_score_codex":0.0005359174,"about_ca_system_score_gemma":0.00069798855,"threshold_uncertainty_score":0.058150053},"labels":[],"label_agreement":null},{"id":"W7008932598","doi":"","title":"Deep learning approaches to person re-identification","year":2019,"lang":"en","type":"dissertation","venue":"OPUS - Open Publications of UTS Scholars (University of Technology Sydney)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Deep learning; Feature (linguistics); Focus (optics); Perspective (graphical); Key (lock)","score_opus":0.08104640891092647,"score_gpt":0.2865991729207338,"score_spread":0.20555276400980732,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7008932598","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":[0.008522839,0.0036475356,0.98022985,0.0017321943,0.00035915492,0.000035375266,0.0003257942,0.0016488646,0.0034983663],"genre_scores_gemma":[0.3646369,0.008286565,0.55457664,0.0012240075,0.0009614426,0.000115537165,0.0032927745,0.00067300454,0.06623321],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9989324,0.00019801714,0.00005077563,0.0003465691,0.0003064868,0.00016574105],"domain_scores_gemma":[0.99850273,0.0005047087,0.000077734396,0.00045042727,0.00040389827,0.00006050959],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0014910749,0.0009865961,0.0014155781,0.0012866993,0.000494471,0.001969578,0.001620083,0.0017761658,0.0033998599],"category_scores_gemma":[0.002936494,0.00063546095,0.0008435031,0.001586443,0.00071000017,0.0031462782,0.001987444,0.0032230788,0.0029570719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00005315126,0.00012834712,0.0010262664,0.00012544809,0.0001152853,0.000045677192,0.00014344725,0.07871818,0.00409348,0.018574964,0.018370697,0.878605],"study_design_scores_gemma":[0.000003711395,0.00002275262,0.00086811837,0.00004746111,0.000021606125,0.000051918505,0.00009031066,0.9282535,0.0041495925,0.053852566,0.012616782,0.000021735084],"about_ca_topic_score_codex":0.012071258,"about_ca_topic_score_gemma":0.0126332315,"teacher_disagreement_score":0.012071258,"about_ca_system_score_codex":0.0013335268,"about_ca_system_score_gemma":0.00097465515,"threshold_uncertainty_score":0.024001956},"labels":[],"label_agreement":null},{"id":"W7020163053","doi":"","title":"La fabrique des analyses qualitatives The construction of qualitative analysis - perspectives on processes of collaboration","year":2017,"lang":"en","type":"other","venue":"OpenEdition (OpenEdition)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Perspective (graphical); Ethnography; Qualitative research; Qualitative analysis","score_opus":0.09105474929928377,"score_gpt":0.44202904534633686,"score_spread":0.3509742960470531,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7020163053","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":[0.04932611,0.005191299,0.7831007,0.03536187,0.0009330281,0.0011837583,0.0006602904,0.00026585214,0.12397701],"genre_scores_gemma":[0.7675908,0.003771649,0.20720904,0.0026901127,0.00022410338,0.003028747,0.000250875,0.00022069545,0.015013995],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","domain_scores_codex":[0.90369105,0.08622598,0.0015492401,0.002048239,0.0055898447,0.0008957053],"domain_scores_gemma":[0.88784826,0.09395088,0.0037520018,0.0059844507,0.0074968147,0.00096753356],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.06282278,0.0011315569,0.00094945903,0.005030588,0.0062269936,0.015029486,0.0017125476,0.001965302,0.0073609846],"category_scores_gemma":[0.06970976,0.0006815721,0.0010127631,0.004586419,0.030596409,0.014849844,0.0061497884,0.003648484,0.00065399596],"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":[0.000031594067,0.000028798127,0.0008625863,0.0007272199,0.000025723448,0.00018412257,0.15152334,0.0007667382,0.00086492614,0.82166815,0.0018114734,0.021505335],"study_design_scores_gemma":[0.000046433255,0.000064014435,0.0011885337,0.0022158881,0.000037073274,0.00029671536,0.18815356,0.004039558,0.0023753871,0.6456736,0.15584765,0.00006153373],"about_ca_topic_score_codex":0.008253292,"about_ca_topic_score_gemma":0.007141734,"teacher_disagreement_score":0.06282278,"about_ca_system_score_codex":0.010653456,"about_ca_system_score_gemma":0.012278374,"threshold_uncertainty_score":0.33224255},"labels":[],"label_agreement":null},{"id":"W7028539617","doi":"","title":"An Experimental Study on ObjectTracking","year":2025,"lang":"en","type":"article","venue":"Hogskolan Ihalmstad (Halmstad University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Particle filter; Robustness (evolution); Kalman filter; Bounding overwatch; Adverse weather; Tracking (education); Tracking system","score_opus":0.023565719858665513,"score_gpt":0.3024380936203891,"score_spread":0.2788723737617236,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7028539617","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":[0.8156493,0.0010559009,0.16048299,0.00043886766,0.0007287641,0.0008988506,0.003834405,0.0027356308,0.014175277],"genre_scores_gemma":[0.8525311,0.0007837344,0.12699333,0.0003830801,0.000075400436,0.000795134,0.008406326,0.0004342164,0.009597785],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99775344,0.000369314,0.00019704805,0.00083037507,0.0006185595,0.0002312014],"domain_scores_gemma":[0.9941215,0.0025114599,0.00040310647,0.0014227526,0.0012998763,0.0002413701],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021301128,0.0009646032,0.0008140144,0.00068281026,0.0010296607,0.0012410011,0.0014443817,0.0014553693,0.0070279394],"category_scores_gemma":[0.008964324,0.00043669436,0.0006036044,0.0009841354,0.00091289723,0.0017621213,0.0015626268,0.0009929711,0.0018372237],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0065012774,0.007416391,0.03152645,0.0024378188,0.00047614303,0.0013271379,0.0019621123,0.09387415,0.35144562,0.009004978,0.017187107,0.4768409],"study_design_scores_gemma":[0.0007163547,0.010102352,0.05297082,0.00048180387,0.00036715175,0.0016968378,0.0018001988,0.4971661,0.37207097,0.010109019,0.052231416,0.00028687384],"about_ca_topic_score_codex":0.0047622095,"about_ca_topic_score_gemma":0.005164359,"teacher_disagreement_score":0.0070279394,"about_ca_system_score_codex":0.0006619885,"about_ca_system_score_gemma":0.0009347551,"threshold_uncertainty_score":0.023510873},"labels":[],"label_agreement":null},{"id":"W7032834650","doi":"","title":"Exllu(gesis)","year":2024,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"York University","keywords":"Theme (computing); Set (abstract data type); Generative grammar; Key (lock); Object (grammar)","score_opus":0.014174756744681105,"score_gpt":0.18146531173288122,"score_spread":0.1672905549882001,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7032834650","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":[0.005966953,0.0028373883,0.0015483698,0.005142675,0.0015771162,0.000038543567,0.00026060254,0.0004970602,0.98213124],"genre_scores_gemma":[0.044197932,0.0013518895,0.0012259107,0.0016257336,0.00027242835,0.000080656944,0.00022295129,0.00027398748,0.9507484],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.99958295,0.00012993003,0.000013628652,0.00007370447,0.00012406644,0.000075746764],"domain_scores_gemma":[0.99974114,0.00008086649,0.00002272977,0.000039774455,0.000053503343,0.00006194841],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00054392003,0.0006658352,0.00022226563,0.0012371565,0.003083012,0.004103081,0.0005787762,0.001397249,0.105961435],"category_scores_gemma":[0.0016379764,0.0001724214,0.0002236154,0.0009135933,0.0018758079,0.0029691672,0.0034532119,0.0015025309,0.026376603],"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":[0.00006817103,0.000037950635,0.00091627304,0.00021826841,0.0000041554385,0.0005078911,0.008681895,0.000114190036,0.0007035685,0.3359255,0.4960812,0.15674093],"study_design_scores_gemma":[0.0000010725589,0.0000060098387,0.00022795486,0.00006524978,5.297089e-7,0.00012858096,0.001033625,0.000045235316,0.000285956,0.0021226848,0.99607974,0.0000032354271],"about_ca_topic_score_codex":0.0041371062,"about_ca_topic_score_gemma":0.013229163,"teacher_disagreement_score":0.89403856,"about_ca_system_score_codex":0.002290418,"about_ca_system_score_gemma":0.000737735,"threshold_uncertainty_score":0.35447633},"labels":[],"label_agreement":null},{"id":"W7032947845","doi":"","title":"Parents of Woman Mocked by Toronto Cops 'Disappointed' in Disciplinary Hearing Delays","year":2017,"lang":"en","type":"article","venue":"eYLS (Yale Law School)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Discipline; Daughter; Hearing loss; Sudden Hearing Loss","score_opus":0.031482482771215965,"score_gpt":0.3184911824385669,"score_spread":0.28700869966735093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7032947845","genre_codex":"empirical","genre_gemma":"other","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"other","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.9458923,0.0009567468,0.0008959734,0.040869568,0.0011740889,0.00006305448,0.00020416718,0.0001246096,0.00981948],"genre_scores_gemma":[0.98537123,0.0007937203,0.00062165695,0.0063967393,0.00017096968,0.000025209996,0.00006315173,0.000033134027,0.006524222],"study_design_codex":"case_report","study_design_gemma":"not_applicable","domain_scores_codex":[0.9993069,0.00018276952,0.00004526867,0.000083792576,0.00021198865,0.00016925776],"domain_scores_gemma":[0.9978036,0.0005390819,0.00049150124,0.000118428376,0.00032947664,0.0007178784],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005092092,0.00047265342,0.0003619862,0.00044187653,0.00668274,0.001313177,0.0005389675,0.0024119916,0.0024531574],"category_scores_gemma":[0.007219009,0.0004461648,0.00029066284,0.0003607501,0.0014163123,0.0007576438,0.0014799506,0.0036227454,0.00048736116],"study_design_candidate":"not_applicable","study_design_consensus":null,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00018467217,0.00011491746,0.062801056,0.000086792665,0.000048125432,0.62195146,0.22802594,0.00020269875,0.0041926685,0.0018954051,0.05641734,0.024078898],"study_design_scores_gemma":[0.00002090973,0.00036424486,0.084952965,0.00044233215,0.00008156736,0.4515835,0.38514408,0.00088045426,0.003982366,0.0009434592,0.071434185,0.00016991589],"about_ca_topic_score_codex":0.07141725,"about_ca_topic_score_gemma":0.1530652,"teacher_disagreement_score":0.9285827,"about_ca_system_score_codex":0.0037884293,"about_ca_system_score_gemma":0.001973627,"threshold_uncertainty_score":0.14200312},"labels":[],"label_agreement":null},{"id":"W7034089129","doi":"","title":"Talking to play by play man Morley Scott: The Edmonton \"Eskimos\" are officially no more","year":2020,"lang":"en","type":"other","venue":"Bulletin of Miscellaneous Information (Royal Gardens Kew)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Perspective (graphical); Government (linguistics); Agency (philosophy); Work (physics)","score_opus":0.006937467788684094,"score_gpt":0.2094525378935923,"score_spread":0.2025150701049082,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7034089129","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":[0.002278312,0.009750857,0.0015824509,0.10436755,0.051533747,0.00010024783,0.00069372694,0.0011202983,0.8285728],"genre_scores_gemma":[0.0019819415,0.0010307789,0.00015008425,0.003369842,0.0007788551,0.000011245427,0.00007548718,0.00014829541,0.9924535],"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","domain_scores_codex":[0.9994199,0.00009355733,0.0000149522375,0.00008959835,0.00023444506,0.00014749647],"domain_scores_gemma":[0.9988148,0.00014688571,0.000033193082,0.000046449106,0.00037267082,0.0005859472],"candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0010750081,0.0011358064,0.0003354654,0.0008180229,0.006893874,0.004516918,0.00076016807,0.0027537448,0.3063993],"category_scores_gemma":[0.0022456378,0.00047873196,0.00019618675,0.0004765084,0.0012789548,0.0031068115,0.0030621472,0.003296614,0.12913182],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.000010872629,0.0000060850957,0.00007391569,0.000015180269,6.202873e-7,0.000033268567,0.00024943263,0.0000045819484,0.00006820605,0.0010432092,0.9821485,0.016346129],"study_design_scores_gemma":[0.0000013802675,0.000006238038,0.00020745618,0.000037738406,9.998057e-7,0.00004237204,0.0007829348,0.000007751069,0.000045929715,0.00014008461,0.9987243,0.00000287628],"about_ca_topic_score_codex":0.044562828,"about_ca_topic_score_gemma":0.14985164,"teacher_disagreement_score":0.9554372,"about_ca_system_score_codex":0.0012349014,"about_ca_system_score_gemma":0.0020266396,"threshold_uncertainty_score":0.98933727},"labels":[],"label_agreement":null},{"id":"W7039830813","doi":"","title":"Multi-feature RGB-D generic object tracking using a simple filter hierarchy","year":2014,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"McGill University","keywords":"Object (grammar); Video tracking; Tracking (education); Initialization; Particle filter; Tracking system; A priori and a posteriori; Filter (signal processing)","score_opus":0.048100015209335656,"score_gpt":0.3027638603023526,"score_spread":0.2546638450930169,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7039830813","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":[0.004474987,0.000093744784,0.9935487,0.00002183619,0.00001122804,0.00002840178,0.000038591625,0.0006361276,0.0011463054],"genre_scores_gemma":[0.14470279,0.00033395787,0.8476067,0.00010182265,0.000036389665,0.00007781042,0.0004548016,0.00012918714,0.0065565733],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99943274,0.000028798207,0.00002363237,0.00022577851,0.00021686975,0.0000722249],"domain_scores_gemma":[0.9997013,0.00003985755,0.00004290183,0.0000824842,0.00010062455,0.000032772325],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00059483526,0.00059884024,0.0010293993,0.0012603032,0.0006436988,0.0013609864,0.001527373,0.0010090509,0.0025424084],"category_scores_gemma":[0.00083575584,0.00056247285,0.0011160647,0.0016897805,0.0004072139,0.00170347,0.001188798,0.0008435702,0.0018027751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00015024394,0.00019035552,0.001980355,0.0001531399,0.00015940044,0.00008794621,0.0001755565,0.13318118,0.06495149,0.014792039,0.0030954205,0.78108287],"study_design_scores_gemma":[0.000010229929,0.00006164291,0.0022965828,0.000016837665,0.00003237797,0.00007711921,0.000022105183,0.9793382,0.008317029,0.0045561935,0.00525059,0.000020947058],"about_ca_topic_score_codex":0.019005897,"about_ca_topic_score_gemma":0.02420598,"teacher_disagreement_score":0.019005897,"about_ca_system_score_codex":0.001451612,"about_ca_system_score_gemma":0.0012362783,"threshold_uncertainty_score":0.037790537},"labels":[],"label_agreement":null},{"id":"W7045346613","doi":"","title":"ACE Surveillance: The Next Generation Surveillance for Long-Term Monitoring and Activity Summarization","year":2006,"lang":"en","type":"article","venue":"NPARC","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":true,"route_about_ca":false,"ca_institutions":"","funders":"","keywords":"Automatic summarization; Key (lock); Data extraction; Annotation","score_opus":0.062144022543600644,"score_gpt":0.30717194997871805,"score_spread":0.24502792743511742,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7045346613","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":[0.011517334,0.0023387629,0.97512895,0.00038518404,0.00017160228,0.00038415138,0.0010731936,0.0032386773,0.005762199],"genre_scores_gemma":[0.14849883,0.0021402494,0.84060776,0.00030058075,0.00029894896,0.00047415786,0.0029482057,0.0002025958,0.0045286356],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9981389,0.0005069264,0.00010611086,0.00045068204,0.0007032799,0.00009401932],"domain_scores_gemma":[0.9961326,0.0012520171,0.0005107925,0.0007410599,0.0010861077,0.00027748273],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0021874125,0.0007415628,0.00072990044,0.0033635148,0.0007427665,0.0020554115,0.0014634131,0.0009803888,0.0027363903],"category_scores_gemma":[0.004439262,0.0002643873,0.00047427663,0.0017898252,0.00068249245,0.0024350688,0.001683925,0.0010932784,0.0010093378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0004889583,0.00017884994,0.008277643,0.0007109164,0.00016008323,0.0003467799,0.0007449772,0.006158871,0.04052746,0.023071133,0.017663809,0.90167063],"study_design_scores_gemma":[0.00018925432,0.0015034304,0.037430063,0.0008994408,0.00048306177,0.003490246,0.0015959452,0.36615407,0.14797997,0.050586723,0.38928416,0.0004036732],"about_ca_topic_score_codex":0.0024872997,"about_ca_topic_score_gemma":0.0036672766,"teacher_disagreement_score":0.0033635148,"about_ca_system_score_codex":0.00059212244,"about_ca_system_score_gemma":0.0010245211,"threshold_uncertainty_score":0.011568308},"labels":[],"label_agreement":null},{"id":"W7067270146","doi":"","title":"Learning-based detection of people for automated video surveillance","year":2004,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Class (philosophy); Classifier (UML); Object detection; Feature extraction; Image processing","score_opus":0.01402677148503723,"score_gpt":0.26971293999530926,"score_spread":0.25568616851027204,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7067270146","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":[0.19404761,0.0015818247,0.7692621,0.00068215386,0.00031546623,0.00039654347,0.0013132637,0.010208262,0.02219285],"genre_scores_gemma":[0.5915788,0.0013051598,0.3756895,0.00024740727,0.00019356038,0.00028110063,0.002635266,0.00027249233,0.027796699],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9995388,0.000100924066,0.000019154222,0.00015828978,0.000118239324,0.00006464747],"domain_scores_gemma":[0.9991652,0.00043514188,0.00007288225,0.00007037866,0.00020190637,0.000054562282],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00087839516,0.0004909573,0.0005060218,0.0021124058,0.00027788916,0.0009264863,0.0006966441,0.00074040896,0.0063053053],"category_scores_gemma":[0.002672401,0.00022869826,0.00037179477,0.0009140016,0.00022298734,0.0008537336,0.00041263364,0.00044630113,0.0036740047],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00019742269,0.0002691152,0.0053637037,0.00008452724,0.000039267223,0.000039508377,0.00006895544,0.011244355,0.0190622,0.00077388005,0.009246854,0.95361024],"study_design_scores_gemma":[0.00006247446,0.00027020913,0.020406254,0.000075712705,0.000057315952,0.00016936887,0.00017051127,0.94050944,0.025062913,0.0027166577,0.010463381,0.000035751735],"about_ca_topic_score_codex":0.0074235923,"about_ca_topic_score_gemma":0.009061256,"teacher_disagreement_score":0.0074235923,"about_ca_system_score_codex":0.0005180871,"about_ca_system_score_gemma":0.0005527262,"threshold_uncertainty_score":0.021093369},"labels":[],"label_agreement":null},{"id":"W7085088046","doi":"10.5281/zenodo.17290458","title":"Retrofits and Revisions: How Evolutionary Theory Fails the Test of Predictive Science","year":2025,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"St. Clair College","funders":"","keywords":"Geneticist; Darwinism; Evolutionary theory; Population; Core (optical fiber); Mechanism (biology); Mutation rate; Mutation","score_opus":0.02390455712657837,"score_gpt":0.2702633379871073,"score_spread":0.2463587808605289,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7085088046","genre_codex":"commentary","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.055630323,0.031396694,0.052840512,0.7128902,0.008181092,0.000090489964,0.00032559488,0.0006893761,0.13795574],"genre_scores_gemma":[0.9217413,0.0071968636,0.01302617,0.038639978,0.0034683011,0.00015234147,0.00015589199,0.0009974532,0.014621712],"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","domain_scores_codex":[0.9652456,0.022710131,0.00090732775,0.0037507508,0.0055883597,0.0017978647],"domain_scores_gemma":[0.8837471,0.08753484,0.004379101,0.013208491,0.008114073,0.00301636],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03998973,0.0010345264,0.0013366472,0.0039402717,0.011364913,0.02102426,0.0045903847,0.009729711,0.008701261],"category_scores_gemma":[0.12017407,0.00084289897,0.0010419695,0.0023717529,0.0782487,0.03855836,0.009525958,0.01518621,0.0021675373],"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":[0.000094786716,0.000036800586,0.0018546333,0.00015904699,0.00004283048,0.0005626496,0.03164449,0.0008946582,0.0001755055,0.8949263,0.03426456,0.035343677],"study_design_scores_gemma":[0.000031104697,0.000032340562,0.00045493667,0.00033932377,0.000016716936,0.0002189634,0.010174192,0.0015183309,0.00027248773,0.89218044,0.09470319,0.00005795753],"about_ca_topic_score_codex":0.0056757256,"about_ca_topic_score_gemma":0.0045385845,"teacher_disagreement_score":0.03998973,"about_ca_system_score_codex":0.0075213406,"about_ca_system_score_gemma":0.006295484,"threshold_uncertainty_score":0.21148843},"labels":[],"label_agreement":null},{"id":"W7095724912","doi":"","title":"Title of Thesis: Automatic Pedestrian Detection and Tracking with a Multiple-","year":2010,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Tracking (education); Authorization; Pedestrian; Work (physics); Tracking system","score_opus":0.01806171431907782,"score_gpt":0.2590916044054264,"score_spread":0.24102989008634856,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7095724912","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.0383773,0.010198856,0.6096847,0.01208113,0.040838342,0.0018141727,0.014777505,0.011926365,0.26030165],"genre_scores_gemma":[0.12361327,0.0068343757,0.15897997,0.0009704203,0.00735338,0.0006732593,0.015618878,0.0024138067,0.6835426],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99949586,0.00004242031,0.000021590393,0.00022386263,0.0001770977,0.000039243485],"domain_scores_gemma":[0.998792,0.0001941343,0.000060715007,0.0001395148,0.0005615408,0.0002521516],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009517332,0.001062404,0.0010079133,0.0010125634,0.0006571853,0.0022349164,0.0007575437,0.001018013,0.116996974],"category_scores_gemma":[0.0023518004,0.00030877342,0.000647058,0.0008184509,0.0003488461,0.0012260608,0.0008682796,0.00090986444,0.10311166],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00051130814,0.00026570368,0.0017449695,0.00064018805,0.00003815848,0.00017863595,0.00016049485,0.0038858692,0.028513862,0.0065061576,0.39432243,0.5632321],"study_design_scores_gemma":[0.00025286165,0.0014615749,0.013365434,0.00063058856,0.00015120009,0.0015214123,0.00019755862,0.11136731,0.072505005,0.010240853,0.7881743,0.00013191164],"about_ca_topic_score_codex":0.0009233636,"about_ca_topic_score_gemma":0.0011973651,"teacher_disagreement_score":0.116996974,"about_ca_system_score_codex":0.0004956644,"about_ca_system_score_gemma":0.0010559215,"threshold_uncertainty_score":0.39139384},"labels":[],"label_agreement":null},{"id":"W7096759216","doi":"","title":"UNCLASSIFIED UNCLASSIFIED Automated video surveillance: challenges and solutions. ACE Surveillance (Annotated Critical Evidence) case study","year":2015,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":true,"ca_institutions":"","funders":"","keywords":"Software deployment; Event (particle physics); Task (project management); Data extraction; Automation; Video tracking; Object detection","score_opus":0.2394704977981725,"score_gpt":0.3872659504549628,"score_spread":0.1477954526567903,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7096759216","genre_codex":"other","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.1621613,0.0072372584,0.25171727,0.030336503,0.0022460525,0.0051307245,0.0051470143,0.00443042,0.5315935],"genre_scores_gemma":[0.66007435,0.0053518866,0.14579757,0.002518231,0.00062455307,0.00093212596,0.0055267494,0.00042637635,0.17874812],"study_design_codex":"design_other","study_design_gemma":"observational","domain_scores_codex":[0.99743134,0.00063807564,0.00022726804,0.00034686708,0.0010868859,0.00026958933],"domain_scores_gemma":[0.992482,0.0017507555,0.0007084903,0.0012227515,0.00331287,0.00052311417],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0025513263,0.0005225923,0.00026968427,0.0015199764,0.0017454097,0.0032339871,0.0012811219,0.0025009662,0.017851932],"category_scores_gemma":[0.0065897936,0.00016538022,0.00034986227,0.0018051855,0.0010286705,0.002036554,0.0013791407,0.0007958736,0.0052297767],"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":[0.00043075564,0.0008248489,0.029928721,0.00086380256,0.00006188396,0.014847432,0.0027778398,0.009933151,0.014667708,0.047826555,0.22167099,0.6561664],"study_design_scores_gemma":[0.000079947036,0.00033885604,0.019537477,0.0006020403,0.000040172148,0.013814888,0.0038776451,0.040472556,0.01163227,0.016871564,0.8926201,0.00011248788],"about_ca_topic_score_codex":0.023982825,"about_ca_topic_score_gemma":0.018467572,"teacher_disagreement_score":0.023982825,"about_ca_system_score_codex":0.0021890625,"about_ca_system_score_gemma":0.003056689,"threshold_uncertainty_score":0.059720635},"labels":[],"label_agreement":null},{"id":"W7118171922","doi":"10.1109/aiccsa66935.2025.11315263","title":"STTATrack: Enhancing One-Stream Single Object Tracking via Score Temporal Token Attention","year":2025,"lang":"","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital","funders":"","keywords":"Embedding; Security token; Temporal database; Adaptability; Object (grammar); Relation (database); Pattern recognition (psychology); Video tracking","score_opus":0.04769521865771172,"score_gpt":0.309887823596921,"score_spread":0.2621926049392093,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118171922","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.020033818,0.0005605651,0.9702681,0.0001116895,0.00014083696,0.00007901714,0.00026141477,0.006343659,0.0022009418],"genre_scores_gemma":[0.5395488,0.0006169463,0.44822368,0.00034761662,0.00017908776,0.00013931947,0.0019246148,0.00090990256,0.008110044],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994025,0.000068933274,0.00002559071,0.00021588881,0.00022547516,0.00006156892],"domain_scores_gemma":[0.99898547,0.00037718788,0.00008466085,0.0002231544,0.00024181174,0.00008763857],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0011801836,0.0009402318,0.0010821667,0.00085682597,0.00039784503,0.0012010587,0.0018606563,0.00061413733,0.002524955],"category_scores_gemma":[0.004349102,0.0003367614,0.00058922934,0.0011603108,0.00051250943,0.0022952305,0.001999713,0.0012352638,0.0012778449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0006730334,0.00023101966,0.0049086977,0.00018423819,0.00014628985,0.0001895075,0.0002698816,0.0968974,0.038068656,0.013319012,0.013060552,0.8320518],"study_design_scores_gemma":[0.00003901473,0.00012265569,0.00087857427,0.00001175387,0.000049366998,0.0001521317,0.00002729874,0.97424954,0.012361215,0.0073893964,0.0046944404,0.00002458287],"about_ca_topic_score_codex":0.01039004,"about_ca_topic_score_gemma":0.015869442,"teacher_disagreement_score":0.01039004,"about_ca_system_score_codex":0.0007890208,"about_ca_system_score_gemma":0.0014469649,"threshold_uncertainty_score":0.020659149},"labels":[],"label_agreement":null},{"id":"W7118173412","doi":"10.1109/aiccsa66935.2025.11315437","title":"Decoupling Tracking and Segmentation: Introducing VOST for Efficient Video Object Segmentation","year":2025,"lang":"","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"University of British Columbia, Okanagan Campus; Kelowna General Hospital","funders":"","keywords":"Segmentation; Video tracking; Minimum bounding box; Bounding overwatch; Object (grammar); Image segmentation; Scale-space segmentation; Inference","score_opus":0.024670561488207097,"score_gpt":0.3331186814211825,"score_spread":0.3084481199329754,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7118173412","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":[0.008349287,0.00041221257,0.98308325,0.000065587265,0.00006446952,0.00007491136,0.00018944795,0.006766575,0.0009942522],"genre_scores_gemma":[0.18029036,0.00045803303,0.8116594,0.0003122284,0.00012284814,0.00017378326,0.0026340606,0.0011983009,0.0031511013],"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","domain_scores_codex":[0.99879396,0.00012355119,0.00006357747,0.0005585794,0.00032943903,0.0001308566],"domain_scores_gemma":[0.99908197,0.00024320105,0.00011332843,0.000273055,0.00020871063,0.00007972989],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0010845967,0.0014135352,0.0014484298,0.0018288533,0.0005053012,0.0017318025,0.0031541293,0.0014974389,0.0013821252],"category_scores_gemma":[0.0031404106,0.0007774182,0.0012818334,0.0016973797,0.0010299825,0.0021228245,0.002097647,0.001483702,0.0013377896],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00039285363,0.00023103092,0.0031802505,0.0003382774,0.00020199841,0.00020508925,0.00029574631,0.1450411,0.08733459,0.013161505,0.013636412,0.7359811],"study_design_scores_gemma":[0.000022942086,0.000081512175,0.00048807412,0.00002066885,0.00002370889,0.00013977918,0.000028435476,0.96726793,0.019415813,0.006816712,0.005669942,0.000024473857],"about_ca_topic_score_codex":0.009925251,"about_ca_topic_score_gemma":0.014701983,"teacher_disagreement_score":0.009925251,"about_ca_system_score_codex":0.0009990785,"about_ca_system_score_gemma":0.0022417873,"threshold_uncertainty_score":0.019734979},"labels":[],"label_agreement":null},{"id":"W7127379659","doi":"10.1109/ism66958.2025.00056","title":"CCAFF: Object Tracking Under Heavy Occlusion","year":2025,"lang":"","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Feature (linguistics); Pattern recognition (psychology); Object (grammar); Codebook; Identity (music); Matching (statistics); Video tracking; Similarity (geometry); Feature extraction","score_opus":0.03311481023390863,"score_gpt":0.3330829518207365,"score_spread":0.2999681415868279,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127379659","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.18136369,0.0042166687,0.7490091,0.00044780146,0.0010531056,0.0005560885,0.0040724794,0.050653566,0.008627617],"genre_scores_gemma":[0.6262389,0.0008137453,0.3374772,0.0005851447,0.000209073,0.0002505667,0.025085948,0.001064785,0.008274484],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.998195,0.00019178275,0.00007393249,0.00074231904,0.00050608127,0.00029095745],"domain_scores_gemma":[0.9988607,0.00022561807,0.000110444904,0.00048694154,0.00023498411,0.00008135655],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0023600906,0.0019946462,0.0019099723,0.0015761565,0.00069495256,0.0013591019,0.00245221,0.0016422988,0.0021632419],"category_scores_gemma":[0.0046855058,0.00040193193,0.0010612827,0.0016397116,0.0005989116,0.0019314343,0.002525052,0.0016701118,0.0017370889],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00093966885,0.0004961111,0.007709331,0.00029660744,0.0004645704,0.000319375,0.0001252371,0.122028776,0.021224987,0.0023904222,0.04223675,0.8017683],"study_design_scores_gemma":[0.000075221564,0.00037271358,0.0037942806,0.00004244781,0.00007573898,0.00044236926,0.00003979845,0.9731203,0.012209386,0.0028708633,0.0069213607,0.00003560148],"about_ca_topic_score_codex":0.019728001,"about_ca_topic_score_gemma":0.017239828,"teacher_disagreement_score":0.019728001,"about_ca_system_score_codex":0.00080572197,"about_ca_system_score_gemma":0.0016671894,"threshold_uncertainty_score":0.039226353},"labels":[],"label_agreement":null},{"id":"W7127443195","doi":"10.1109/ccece64018.2025.11364393","title":"DetTrack: Detect Target from Local Region for 3D Single Object Tracking in Point Clouds","year":2025,"lang":"","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Dalhousie University","funders":"","keywords":"Detector; Tracking (education); Object detection; Point cloud; Object (grammar); Modular design; Inference; Task (project management)","score_opus":0.03905143804477182,"score_gpt":0.30906883374759475,"score_spread":0.2700173957028229,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W7127443195","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.00751788,0.00020396296,0.9879764,0.000066279754,0.000040256564,0.00007026013,0.00021509218,0.0035127723,0.00039715364],"genre_scores_gemma":[0.19699408,0.00046830895,0.7970274,0.00024753102,0.00009015496,0.00027499185,0.0021238641,0.00065899955,0.0021147074],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9994393,0.000049842663,0.00001950858,0.00021225223,0.00021072141,0.00006824304],"domain_scores_gemma":[0.9995289,0.000111648915,0.00006820232,0.00013173146,0.00010723387,0.00005236743],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00078919565,0.0012288269,0.0012996076,0.0017381526,0.00060821127,0.0012930896,0.0027178957,0.0014468954,0.0015156983],"category_scores_gemma":[0.0020599067,0.0008476397,0.0018410096,0.0015102978,0.0007282846,0.0012625747,0.002484832,0.0017525394,0.0012823311],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00030166455,0.00019318135,0.0034404402,0.0002261743,0.00029024176,0.00025707888,0.00022254296,0.3266291,0.028762726,0.0120311,0.01814942,0.60949636],"study_design_scores_gemma":[0.000012077128,0.000019828687,0.00034766045,0.000010722491,0.000015897158,0.00007363977,0.00001494497,0.99028546,0.004148911,0.003339954,0.0017169556,0.000013998429],"about_ca_topic_score_codex":0.014325148,"about_ca_topic_score_gemma":0.016224671,"teacher_disagreement_score":0.014325148,"about_ca_system_score_codex":0.0007946633,"about_ca_system_score_gemma":0.001768164,"threshold_uncertainty_score":0.02848351},"labels":[],"label_agreement":null},{"id":"W784702625","doi":"10.1007/s00138-015-0697-7","title":"An adaptive ensemble-based system for face recognition in person re-identification","year":2015,"lang":"en","type":"article","venue":"Machine Vision and Applications","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":false,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Facial recognition system; A priori and a posteriori; Ensemble learning; Computer vision; Machine learning","score_opus":0.08291734158363036,"score_gpt":0.35169801386684224,"score_spread":0.2687806722832119,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W784702625","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.049783178,0.0007240954,0.94453233,0.00012540925,0.0003458667,0.000090902184,0.00018247844,0.0022093954,0.0020063221],"genre_scores_gemma":[0.49108317,0.0005773834,0.49841473,0.00035144217,0.0002559405,0.00020708992,0.00075195736,0.00014856941,0.0082096895],"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9991999,0.00013564034,0.00004605777,0.00026738492,0.00024882096,0.000102085054],"domain_scores_gemma":[0.99914646,0.00013922041,0.00003708607,0.00016627832,0.00046718618,0.00004381103],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001176426,0.0006435727,0.001345688,0.00071977475,0.00066387985,0.0005245536,0.0012958964,0.0011217084,0.0022905553],"category_scores_gemma":[0.0014938356,0.00034211384,0.00078720675,0.0007336015,0.00018139042,0.0010946367,0.0012479839,0.0010918457,0.0018583208],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00045509962,0.00031204766,0.0028812585,0.00006235902,0.00020696121,0.000104549385,0.000079858255,0.01888654,0.07489349,0.0008535974,0.0050172294,0.89624697],"study_design_scores_gemma":[0.000016572823,0.0002315553,0.004192289,0.000013217041,0.0001452973,0.00030865637,0.000028791357,0.96167916,0.029714936,0.00086087233,0.0027670243,0.000041746567],"about_ca_topic_score_codex":0.0032954046,"about_ca_topic_score_gemma":0.005424054,"teacher_disagreement_score":0.0032954046,"about_ca_system_score_codex":0.000304178,"about_ca_system_score_gemma":0.0004909009,"threshold_uncertainty_score":0.007662654},"labels":[],"label_agreement":null},{"id":"W9544183","doi":"10.5281/zenodo.1060437","title":"Real-Time Target Tracking Using A Pan And Tilt Platform","year":2009,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"route_ca_aff":false,"route_ca_fund":true,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"","funders":"Ministère de l'Éducation, du Loisir et du Sport Québec","keywords":"Tilt (camera); Tracking (education); Computer science; Computer vision; Artificial intelligence; Psychology; Mathematics; Geometry","score_opus":0.057563640684062216,"score_gpt":0.2882793746268032,"score_spread":0.23071573394274097,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W9544183","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.06084696,0.0006016325,0.932446,0.00006299618,0.00010049467,0.0000685318,0.000056177796,0.002699317,0.0031178475],"genre_scores_gemma":[0.6279452,0.000912792,0.36231977,0.00010724688,0.00008480481,0.00009086348,0.00024333494,0.00011243526,0.008183514],"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.9996916,0.00002987949,0.000009638776,0.00011475933,0.00012425803,0.000029803332],"domain_scores_gemma":[0.99980813,0.00004333127,0.000026976271,0.000041927116,0.000056620156,0.000023064393],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00031421232,0.0006404525,0.0005085044,0.00042498426,0.00026325916,0.0005641112,0.0006995478,0.0004748733,0.001959149],"category_scores_gemma":[0.0005111897,0.0002897147,0.00028300696,0.00038183352,0.0003023386,0.00066787115,0.00059031334,0.0004450258,0.00073690026],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.0009588608,0.00011845154,0.0019683281,0.00019982038,0.00007577082,0.00034051426,0.00020448194,0.01948512,0.48940322,0.0028728729,0.002286714,0.48208582],"study_design_scores_gemma":[0.0001502614,0.0013529776,0.010076558,0.00006246051,0.00017293563,0.002368004,0.000098260236,0.6206731,0.34583974,0.0018018509,0.017295428,0.00010841818],"about_ca_topic_score_codex":0.0017596588,"about_ca_topic_score_gemma":0.0016115101,"teacher_disagreement_score":0.001959149,"about_ca_system_score_codex":0.00024661253,"about_ca_system_score_gemma":0.00035105066,"threshold_uncertainty_score":0.0065540075},"labels":[],"label_agreement":null}]}