{"id":"W4393751681","doi":"10.5281/zenodo.3255101","title":"RAVDESS Facial Landmark Tracking","year":2019,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Face recognition and analysis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Landmark; Artificial intelligence; Computer vision; Computer science; Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001002988,0.002004397,0.001397781,0.002110821,0.0006848025,0.001326761,0.001851515,0.001232214,0.101771],"category_scores_gemma":[0.003512314,0.0006045218,0.001171394,0.001291838,0.0002531091,0.00145701,0.00195187,0.001318524,0.1652635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007757613,"about_ca_system_score_gemma":0.000933164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01033335,"about_ca_topic_score_gemma":0.02395379,"domain_scores_codex":[0.9986964,0.0001201773,0.00009836199,0.0004351754,0.0005305379,0.00011942],"domain_scores_gemma":[0.9985088,0.0001688571,0.00008896067,0.0005705634,0.0005661216,0.00009682197],"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":[0.0003180214,0.00006904357,0.00193432,0.0004207702,0.00004813564,0.00007314673,0.00005523164,0.0006940759,0.002893284,0.0004799652,0.9262864,0.06672762],"study_design_scores_gemma":[0.0002423999,0.0002566066,0.0271065,0.0004647124,0.00008329209,0.001072899,0.0002215785,0.01470432,0.0156062,0.002775441,0.9372789,0.0001871163],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007345019,0.0008743742,0.01154346,0.0001856447,0.0006028741,0.0004695204,0.9138815,0.03901111,0.02608644],"genre_scores_gemma":[0.009694901,0.0001989259,0.01109552,0.0001533585,0.00004833079,0.0007142535,0.9651334,0.001749275,0.01121217],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.101771,"threshold_uncertainty_score":0.3404578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05146911144280894,"score_gpt":0.2645705262431995,"score_spread":0.2131014148003906,"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."}}