{"id":"W6920325247","doi":"10.60692/wv258-6e946","title":"Yoga for all: A Comprehensive Collection of Yoga Images and Videos dataset","year":2023,"lang":"en","type":"article","venue":"Greater South Information System","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Abbott (Canada)","funders":"","keywords":"Focus (optics); Sequence (biology); Visualization; Variety (cybernetics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006116658,0.003670369,0.001686971,0.00375939,0.00129648,0.001452352,0.002504698,0.002454509,0.01578375],"category_scores_gemma":[0.002124116,0.0004433353,0.00167481,0.003277872,0.0004771902,0.001430583,0.002234684,0.001642039,0.02022769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001440343,"about_ca_system_score_gemma":0.001444288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03115181,"about_ca_topic_score_gemma":0.09304392,"domain_scores_codex":[0.9988707,0.000168368,0.0001288133,0.0003002177,0.0003266031,0.0002052158],"domain_scores_gemma":[0.9989641,0.0001931884,0.0001094585,0.0001956337,0.0003799199,0.0001577936],"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":[0.0007264827,0.0004658765,0.00483686,0.003123833,0.0001861365,0.0004081828,0.0002236769,0.0006839483,0.003849356,0.000433261,0.9268549,0.05820741],"study_design_scores_gemma":[0.0004279406,0.0005151462,0.06941073,0.00187324,0.0002499215,0.001448343,0.00169997,0.007075223,0.006157322,0.001000864,0.9098328,0.0003083933],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01514141,0.005210362,0.001464913,0.0005155553,0.0007426688,0.00053319,0.9634328,0.004295203,0.008663926],"genre_scores_gemma":[0.006760284,0.0007686926,0.002969866,0.0002156923,0.00006885504,0.0004774489,0.9861319,0.0001206841,0.002486557],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03115181,"threshold_uncertainty_score":0.06194097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06046433704614786,"score_gpt":0.256928624176805,"score_spread":0.1964642871306571,"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."}}