{"id":"W4377832613","doi":"10.18280/ts.400213","title":"TraViQuA: Natural Language Driven Traffic Video Querying Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Deep learning; Artificial intelligence; Natural (archaeology); Natural language processing; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004823018,0.0001635668,0.0002041777,0.0002773402,0.0003169308,0.0002577387,0.000425798,0.00005110968,0.00007752624],"category_scores_gemma":[0.00002750273,0.0001538702,0.0001476815,0.0009853996,0.00002201178,0.0005033012,0.0001044776,0.0001980624,0.0000611816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007037822,"about_ca_system_score_gemma":0.0000330943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000296386,"about_ca_topic_score_gemma":0.00004841579,"domain_scores_codex":[0.9983369,0.0001486166,0.0003234569,0.0003865899,0.0004333717,0.0003710334],"domain_scores_gemma":[0.9994845,0.00008538135,0.0001127381,0.0001827163,0.00005362849,0.00008098252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001025938,0.0000639205,0.001741812,0.00003195321,0.0001157701,0.0001190224,0.01021251,0.6737911,0.05479892,0.001808351,0.0001764692,0.25713],"study_design_scores_gemma":[0.0003136631,0.00003835898,0.00291013,0.0000259981,0.00002540861,0.000005251488,0.0004949202,0.995027,0.0006609187,0.00002645932,0.000277649,0.00019423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6803942,0.0001682809,0.3184415,0.0002151076,0.0001546479,0.0001154741,7.312292e-7,0.0004027274,0.0001072836],"genre_scores_gemma":[0.9956301,0.00001167939,0.003837475,0.000129961,0.0001471721,0.000008192073,0.00003885386,0.00001527752,0.00018123],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.321236,"threshold_uncertainty_score":0.6274646,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01459287012862945,"score_gpt":0.2499085565516157,"score_spread":0.2353156864229862,"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."}}