{"id":"W6963412209","doi":"10.21227/mqw3-4k98","title":"Angled Posed Facial Expression Dataset","year":2020,"lang":"en","type":"dataset","venue":"IEEE DataPort","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Facial expression; Facial expression recognition; Expression (computer science); Face (sociological concept); Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.000509568,0.001250226,0.001380421,0.0004485888,0.0002725253,0.0002895349,0.003810872,0.0009076942,0.00604313],"category_scores_gemma":[0.0004616617,0.001223856,0.0002260694,0.0006955796,0.0002402578,0.0008647124,0.001258512,0.001676323,0.2871677],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001891975,"about_ca_system_score_gemma":0.0005908964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005385532,"about_ca_topic_score_gemma":0.0002674421,"domain_scores_codex":[0.993349,0.0002920566,0.001229382,0.002226144,0.001871573,0.001031837],"domain_scores_gemma":[0.9925687,0.00009861738,0.001065587,0.005395673,0.0001048181,0.0007666092],"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":[0.0003646595,0.0002794647,0.000003758777,0.0002105879,0.0001374246,0.002268628,0.00001441178,0.000004701051,0.009457993,1.594133e-7,0.9871619,0.00009633294],"study_design_scores_gemma":[0.001273354,0.0001029734,0.0000144564,0.0001677492,0.0004067765,0.00007360667,0.00001083585,0.00001026492,0.002161166,0.000004350406,0.9944553,0.001319213],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001745473,0.00003877787,0.00001559613,0.00004299279,0.002379563,0.001004699,0.9961591,0.0002979326,0.0000438489],"genre_scores_gemma":[0.00000383206,0.0000605376,0.0003309299,0.001256464,0.002660283,0.0001112311,0.9952526,0.0002547337,0.00006935837],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2811246,"threshold_uncertainty_score":0.9990211,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0473419369734307,"score_gpt":0.313433520232847,"score_spread":0.2660915832594163,"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."}}