{"id":"W4414044098","doi":"10.1101/2025.09.02.673800","title":"Using Tools as Cues for Motor Adaptation in Virtual Reality","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Virtual Reality Applications and Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Virtual reality; Adaptation (eye); Motor control; Motor learning; Task (project management); Motion capture; Motor system; Dual (grammatical number)","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":[0.0002163591,0.0003327698,0.0002236692,0.0001766185,0.00009767367,0.0004941495,0.0002467461,0.0002545442,0.001316619],"category_scores_gemma":[0.001524651,0.000172666,0.000198808,0.00008203666,0.0003720696,0.000339764,0.0009473031,0.000333431,0.0001141337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007977679,"about_ca_system_score_gemma":0.00006767597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001907091,"about_ca_topic_score_gemma":0.0002128492,"domain_scores_codex":[0.9997054,0.00008462343,0.00001989091,0.00005921803,0.00008570885,0.00004511124],"domain_scores_gemma":[0.9994859,0.0002050874,0.0001611204,0.00006887108,0.0000312685,0.00004790429],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007055997,0.00006897441,0.001485101,0.0001029276,0.00002556253,0.0001116823,0.0002930072,0.0009180385,0.9820207,0.0002258855,0.0000498845,0.01399281],"study_design_scores_gemma":[0.0003017139,0.008976826,0.2621772,0.0001216936,0.0002673026,0.002053114,0.001634516,0.02674661,0.6873416,0.003568639,0.006630422,0.0001804817],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927297,0.0001222922,0.006479629,0.00001869989,0.00001154931,0.00001072475,0.00001864636,0.00004687203,0.0005617514],"genre_scores_gemma":[0.9971826,0.00003870003,0.002562661,0.00001264021,0.000002614097,0.00000999667,0.00001386453,0.000009790947,0.0001670794],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001316619,"threshold_uncertainty_score":0.004404485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07600373798811172,"score_gpt":0.3076118019844304,"score_spread":0.2316080639963187,"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."}}