{"id":"W3116839971","doi":"10.1016/j.dib.2020.106701","title":"A dataset of labelled objects on raw video sequences","year":2020,"lang":"en","type":"article","venue":"Data in Brief","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Huawei Technologies","keywords":"Computer science; Coding (social sciences); Artificial intelligence; Object (grammar); Test set; Pattern recognition (psychology); Set (abstract data type); Raw data; Context (archaeology); Computer vision; Mathematics; Biology; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001247052,0.002531066,0.001535642,0.004768917,0.001151129,0.001439198,0.002327442,0.002520833,0.005740405],"category_scores_gemma":[0.004651912,0.0005116545,0.00134951,0.004303944,0.0007727804,0.001238521,0.001632552,0.001422035,0.009093068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0014084,"about_ca_system_score_gemma":0.001818801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01400861,"about_ca_topic_score_gemma":0.02586461,"domain_scores_codex":[0.9975326,0.0002950024,0.0002626532,0.0007540685,0.0008600567,0.0002954475],"domain_scores_gemma":[0.9967794,0.0005304735,0.000321945,0.0008978545,0.001194425,0.0002759454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002632113,0.002254075,0.01518277,0.005297604,0.0005028859,0.001962255,0.0004928348,0.01670155,0.06109707,0.002529571,0.4795633,0.4117841],"study_design_scores_gemma":[0.0008763806,0.002627602,0.144326,0.001996327,0.0004823057,0.007053282,0.00152001,0.09543093,0.09310521,0.0058376,0.6461161,0.000628306],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1028669,0.003193086,0.04050051,0.0003910825,0.0008451876,0.002280909,0.8267491,0.0127086,0.0104646],"genre_scores_gemma":[0.02517283,0.0003385733,0.02590563,0.00009289834,0.00007489764,0.000619923,0.9460026,0.0002120656,0.001580611],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01400861,"threshold_uncertainty_score":0.02785414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07748153984306536,"score_gpt":0.3357899120348077,"score_spread":0.2583083721917424,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}