{"id":"W4416507247","doi":"10.1152/jn.00407.2025","title":"ATHENA: automatically tracking hands expertly with no annotations","year":2025,"lang":"en","type":"article","venue":"Journal of Neurophysiology","topic":"Motor Control and Adaptation","field":"Neuroscience","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; Azrieli Foundation","keywords":"Toolbox; Kinematics; Thumb; Tracking (education); Wrist; Index finger; Object (grammar); Motion capture","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001217297,0.002087616,0.001298709,0.001510171,0.000581521,0.001936741,0.00248423,0.001457533,0.0618479],"category_scores_gemma":[0.004619849,0.001148351,0.001130089,0.000469348,0.0005178487,0.001666616,0.004436881,0.001209626,0.05083758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003446218,"about_ca_system_score_gemma":0.001111228,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002708721,"about_ca_topic_score_gemma":0.007341117,"domain_scores_codex":[0.998876,0.0001074252,0.0001030538,0.0005712486,0.0002382788,0.0001040078],"domain_scores_gemma":[0.9987148,0.0003242539,0.00008415459,0.000574867,0.0001840005,0.0001180263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.002432069,0.0002234661,0.003704576,0.001621352,0.0005422094,0.0008325862,0.0006378627,0.006259102,0.1035969,0.004757464,0.2502669,0.6251256],"study_design_scores_gemma":[0.0005916376,0.0005541947,0.02818203,0.0009756594,0.0002679637,0.00441907,0.0004355005,0.2777868,0.1921719,0.0285754,0.4652095,0.0008303444],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01133289,0.0004404672,0.5173737,0.0001445524,0.0005557335,0.0003581499,0.01129023,0.4430979,0.01540627],"genre_scores_gemma":[0.1785619,0.0004179585,0.6824021,0.0009869485,0.0001483329,0.002418715,0.03500371,0.05316869,0.04689168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0618479,"threshold_uncertainty_score":0.2069018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02319411004973979,"score_gpt":0.2729125092266322,"score_spread":0.2497183991768924,"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."}}