{"id":"W2883229900","doi":"10.21611/qirt.2018.051","title":"Université Laval Face Motion and Time-Lapse Video Database (UL-FMTV)","year":2018,"lang":"en","type":"article","venue":"Proceedings of the 2018 International Conference on Quantitative InfraRed Thermography","topic":"Face recognition and analysis","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Face (sociological concept); Motion (physics); Computer vision; Computer graphics (images); Artificial intelligence; Database","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.000723255,0.001194315,0.001123024,0.00318362,0.0005060319,0.0007804713,0.00143105,0.000805063,0.01348674],"category_scores_gemma":[0.002216041,0.0001999307,0.0004953468,0.001853415,0.0002404438,0.0007402262,0.001000502,0.0005402291,0.01158064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006158293,"about_ca_system_score_gemma":0.0009363341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01955539,"about_ca_topic_score_gemma":0.01879626,"domain_scores_codex":[0.9993827,0.00007737815,0.00006151923,0.0001716266,0.0002242467,0.00008253445],"domain_scores_gemma":[0.999188,0.0001068642,0.00007481645,0.0002317815,0.000322356,0.00007616717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001661956,0.0006657378,0.01129638,0.001153527,0.0001628377,0.00064837,0.0001754028,0.001620484,0.01395284,0.002000666,0.7088174,0.2578444],"study_design_scores_gemma":[0.0005787615,0.0006530235,0.1513365,0.0006276345,0.0002319425,0.004438384,0.0005596385,0.03161471,0.02343105,0.002479358,0.7837887,0.0002602889],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.1053555,0.00796622,0.02574761,0.0006093502,0.0006290628,0.001232874,0.8245212,0.01026916,0.02366894],"genre_scores_gemma":[0.06302655,0.001363448,0.02279021,0.0001561494,0.0001399786,0.001108616,0.9040768,0.0002550531,0.007083177],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01955539,"threshold_uncertainty_score":0.04511768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02940590984648331,"score_gpt":0.2627325564900136,"score_spread":0.2333266466435303,"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."}}