{"id":"W4413340112","doi":"10.1101/2025.08.12.669753","title":"ATHENA: Automatically Tracking Hands Expertly with No Annotations","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Robot Manipulation and Learning","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund","keywords":"Computer science; Tracking (education); Artificial intelligence; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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"],"consensus_categories":[],"category_scores_codex":[0.000252119,0.0005646657,0.0005293693,0.0003692812,0.0002062415,0.0003735755,0.0004110371,0.0004266984,0.0001167915],"category_scores_gemma":[0.0001599369,0.0005781048,0.0001266519,0.0005291023,0.00006382172,0.0002188021,0.0001376797,0.0009336123,0.0001416302],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002384459,"about_ca_system_score_gemma":0.0002774142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001129406,"about_ca_topic_score_gemma":0.000002750422,"domain_scores_codex":[0.997928,0.00007386888,0.0005392849,0.000600394,0.0003567568,0.0005017148],"domain_scores_gemma":[0.9982172,0.0001013806,0.0001503247,0.0008744806,0.0004564492,0.0002001425],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006111863,0.0002468147,0.01033436,0.003467198,0.001188641,0.0001549424,0.0004053818,0.8485029,0.1266574,0.004949985,0.003981784,0.00004945368],"study_design_scores_gemma":[0.001869087,0.0001197566,0.1655153,0.003576599,0.0003557791,5.977896e-8,0.00001780032,0.7821911,0.02744897,0.000004399477,0.01611491,0.002786278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6070203,0.001527455,0.3736312,0.000385917,0.003434616,0.002001356,0.00007366266,0.009840517,0.002085039],"genre_scores_gemma":[0.9832541,0.00006200442,0.01581098,0.0001404371,0.0003277855,0.0002101211,8.174521e-7,0.000141762,0.00005200113],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3762338,"threshold_uncertainty_score":0.999667,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01348761848386946,"score_gpt":0.2163525812872928,"score_spread":0.2028649628034234,"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."}}