{"id":"W4412346021","doi":"10.1109/icorr66766.2025.11063027","title":"Pupillometry for Arm and Hand Motor Intent Detection","year":2025,"lang":"en","type":"article","venue":"","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pupillometry; Computer science; Artificial intelligence; Computer vision; Pupil; Psychology; Neuroscience","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":[0.0008927708,0.0005482613,0.0005525858,0.000938394,0.0003067808,0.0005463314,0.0003810328,0.0006806938,0.003877665],"category_scores_gemma":[0.003389455,0.0003225491,0.0003846443,0.0005465371,0.000235291,0.0008429741,0.0005243214,0.0005801332,0.0008694805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002733289,"about_ca_system_score_gemma":0.0003273018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007220171,"about_ca_topic_score_gemma":0.001585656,"domain_scores_codex":[0.9993339,0.0002129045,0.00003760899,0.0001827175,0.0001837364,0.0000491438],"domain_scores_gemma":[0.9986343,0.000627742,0.0002527333,0.000117826,0.0003133632,0.00005402337],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001967338,0.0004049912,0.04039469,0.001693874,0.0002518146,0.0003932106,0.0004967089,0.00298411,0.5035122,0.001291094,0.004206921,0.4424031],"study_design_scores_gemma":[0.0003557756,0.004252188,0.4198857,0.000545739,0.0004769881,0.006382368,0.0007057398,0.1559949,0.380002,0.005995019,0.02514268,0.0002608972],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5542514,0.007295568,0.4211355,0.0005880813,0.0003121566,0.0006407404,0.001336514,0.002280684,0.0121593],"genre_scores_gemma":[0.8667175,0.002549985,0.1265075,0.0002164426,0.0001205169,0.0003665851,0.00048201,0.0001267769,0.002912761],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003877665,"threshold_uncertainty_score":0.01297212,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02439365343864296,"score_gpt":0.2601914099452174,"score_spread":0.2357977565065744,"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."}}