{"id":"W2150148211","doi":"10.1037/a0020556","title":"Disentangling stability, variability and adaptability in human performance: Focus on the interplay between local variance and serial correlation.","year":2010,"lang":"en","type":"article","venue":"Journal of Experimental Psychology Human Perception & Performance","topic":"Mental Health Research Topics","field":"Psychology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Adaptability; Variance (accounting); Correlation; Stability (learning theory); Autocorrelation; Adaptation (eye); Noise (video); Econometrics; Statistics; Psychology; Mathematics; Statistical physics; Computer science; Biology; Artificial intelligence; Physics; Economics; Ecology; Neuroscience; Machine learning","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":[0.0008178015,0.0004334689,0.0003387793,0.0007919892,0.0002100655,0.0009468334,0.0002365915,0.0002916969,0.001039533],"category_scores_gemma":[0.005072018,0.0001874872,0.000331267,0.0007427252,0.001025526,0.00112657,0.0008270394,0.0005345178,0.00009634741],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189177,"about_ca_system_score_gemma":0.0003108327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008667843,"about_ca_topic_score_gemma":0.001282859,"domain_scores_codex":[0.9997271,0.00009119812,0.00001771815,0.00008465513,0.00005502839,0.0000242271],"domain_scores_gemma":[0.9965639,0.002269166,0.0006035107,0.0002506245,0.000114961,0.0001978285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000859731,0.0003076838,0.4402081,0.0006205919,0.0008005798,0.0008824882,0.004155411,0.02666852,0.2106035,0.03275847,0.000431187,0.2817036],"study_design_scores_gemma":[0.00001780604,0.0005868652,0.8989038,0.00005363914,0.0002114973,0.0006298565,0.0005749834,0.05041358,0.009221639,0.03827475,0.001035584,0.00007595292],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9272664,0.00207115,0.06470145,0.0003163916,0.00002061318,0.00002638732,0.00005751161,0.00005990113,0.00548032],"genre_scores_gemma":[0.996014,0.0001626625,0.003582242,0.00001507708,0.00001880708,0.000006548019,0.00001904733,0.000008110444,0.0001735871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001039533,"threshold_uncertainty_score":0.004325032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06555135538473189,"score_gpt":0.4360852343509157,"score_spread":0.3705338789661838,"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."}}