{"id":"W1988011581","doi":"10.1016/j.bbr.2014.11.043","title":"Integrating actions into object location memory: A benefit for active versus passive reaching movements","year":2014,"lang":"en","type":"article","venue":"Behavioural Brain Research","topic":"Motor Control and Adaptation","field":"Neuroscience","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Recall; Object (grammar); Artificial intelligence; Computer science; Computer vision; Psychology; Active learning (machine learning); Movement (music); Cognitive psychology; Communication","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":[],"consensus_categories":[],"category_scores_codex":[0.0009717212,0.0001460468,0.0001496463,0.0002636348,0.0009976981,0.0001824692,0.000311135,0.00008454623,0.00002701014],"category_scores_gemma":[0.008308293,0.0001321302,0.00007956365,0.0004631118,0.000105066,0.0004501619,0.00009444268,0.00047228,0.0000295787],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003930134,"about_ca_system_score_gemma":0.0001006846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001796325,"about_ca_topic_score_gemma":0.0009819887,"domain_scores_codex":[0.9976808,0.0004258434,0.0002334821,0.0005073314,0.0006656232,0.0004869094],"domain_scores_gemma":[0.9963505,0.002770955,0.0001119409,0.0002737614,0.0003631242,0.000129703],"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":[0.0005489081,0.0001045616,0.0001060997,0.00001937117,0.000008429269,0.000002095803,0.002246645,0.0001060051,0.5979887,0.01245636,0.0001912398,0.3862216],"study_design_scores_gemma":[0.01796363,0.005476484,0.1008452,0.0004737522,0.00009049625,0.00001293895,0.01872729,0.1871415,0.635663,0.02313655,0.00889908,0.001570108],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813689,0.000006506975,0.01116957,0.003440852,0.0004352916,0.001421668,0.00002751631,0.00009613875,0.002033548],"genre_scores_gemma":[0.9966543,0.000002002808,0.0007151974,0.0001822868,0.0001695941,0.0005966244,0.00003587221,0.00002657858,0.00161749],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3846515,"threshold_uncertainty_score":0.9946403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1993457524417568,"score_gpt":0.4137226958954982,"score_spread":0.2143769434537414,"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."}}