{"id":"W4413342904","doi":"10.7554/elife.107423.1","title":"A Context-Free Model of Savings in Motor Learning","year":2025,"lang":"en","type":"preprint","venue":"eLife","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Western University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Context (archaeology); Motor learning; Computer science; Psychology; Neuroscience; Geography","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":[0.0005186131,0.0003941222,0.0005736732,0.0003312101,0.0002747314,0.0005629548,0.0009972584,0.0007416857,0.003636635],"category_scores_gemma":[0.002013905,0.0004093128,0.0004418951,0.0001865739,0.0009221596,0.001038077,0.0007063941,0.0008806789,0.0002101771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008939314,"about_ca_system_score_gemma":0.0005196265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006240786,"about_ca_topic_score_gemma":0.005615037,"domain_scores_codex":[0.9998454,0.0000392422,0.000008848272,0.00004565552,0.00002105863,0.00003973765],"domain_scores_gemma":[0.9995199,0.0001990569,0.00009031613,0.00006102255,0.00006056626,0.00006918098],"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":[0.0001378191,0.00006005381,0.001244686,0.00003688565,0.00004408101,0.0001885904,0.00006943739,0.9413956,0.007074027,0.04102818,0.0005497467,0.00817094],"study_design_scores_gemma":[0.000005612244,0.00001783278,0.0003363233,0.000002085666,0.00000402771,0.00001375323,0.000003252054,0.9882057,0.0002515379,0.01108127,0.00007433836,0.000004233469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8363694,0.0002628598,0.153893,0.001053558,0.00005933603,0.00002405772,0.0002269129,0.0003543966,0.007756526],"genre_scores_gemma":[0.9955687,0.00004815986,0.002083055,0.00002556924,0.000008385682,0.00001615401,0.00002847606,0.00001947341,0.002202048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006240786,"threshold_uncertainty_score":0.01240891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04978966353312571,"score_gpt":0.2742892577809754,"score_spread":0.2244995942478497,"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."}}