{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002722243,0.0001046639,0.0001961671,0.00005625555,0.00002719383,0.00004840848,0.002417059,0.00003604123,0.0000166575],"category_scores_gemma":[0.0005766737,0.0000934323,0.00001434141,0.0006008642,0.00006256103,0.001166084,0.00116325,0.0001436304,0.00002934634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000112573,"about_ca_system_score_gemma":0.00005562239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001030702,"about_ca_topic_score_gemma":0.00001341767,"domain_scores_codex":[0.9987585,0.00005726369,0.0002691928,0.0005038227,0.0002397205,0.0001715326],"domain_scores_gemma":[0.9982461,0.0001462233,0.0001054818,0.001407708,0.00002583224,0.00006867798],"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":[0.0003286293,0.0005609384,0.001843078,0.0003772505,0.00006564864,0.000872257,0.002142492,0.0001116531,0.05388398,0.0291498,0.6232226,0.2874417],"study_design_scores_gemma":[0.001515061,0.001078512,0.001216317,0.0002679951,0.00001290763,0.00001830707,0.00006714696,0.03374572,0.4023282,0.007076169,0.551991,0.0006826603],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003293913,0.0006451947,0.9822088,0.00352316,0.0001449814,0.0005841226,0.008179531,0.0003147017,0.001105589],"genre_scores_gemma":[0.7541012,0.0005581236,0.2256732,0.01267121,0.0001251394,0.00001653963,0.006811676,0.00002203636,0.00002094098],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7565356,"threshold_uncertainty_score":0.4491538,"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."}}