{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0006219506,0.0002339031,0.000290749,0.0004765511,0.001398958,0.002429391,0.003136622,0.0001694723,0.014412],"category_scores_gemma":[0.0003612318,0.0002405686,0.00014244,0.0007908354,0.00007430025,0.0004210706,0.00197864,0.0005244737,0.05051124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144182,"about_ca_system_score_gemma":0.000008311195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002062392,"about_ca_topic_score_gemma":7.570887e-7,"domain_scores_codex":[0.9976344,0.0003855966,0.0003030505,0.0006773188,0.0005827872,0.0004168993],"domain_scores_gemma":[0.9981319,0.00003151254,0.0001949519,0.0009589221,0.0005013823,0.0001813363],"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":[0.000006503372,0.00006358523,9.56323e-8,0.00007294314,0.00005023764,0.00001698685,0.00008221489,0.00002099321,0.00004951506,0.0001146105,0.9243124,0.07520988],"study_design_scores_gemma":[0.00030145,0.0000759657,0.00001309389,0.00005322735,0.00002749801,0.00005451671,0.00003216237,0.0007423025,0.00003884804,0.0000494182,0.9983232,0.0002883173],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00002626355,0.00007059285,0.01990232,0.0005615349,0.0004185198,0.0003607916,0.9662919,0.0006659689,0.01170211],"genre_scores_gemma":[0.0005556786,0.0002437176,0.0002161276,0.0002612317,0.0002036208,2.591562e-8,0.9972259,0.0004903725,0.0008032904],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07492156,"threshold_uncertainty_score":0.9999011,"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."}}