{"id":"W2763963518","doi":"10.1016/j.neuropsychologia.2017.09.032","title":"Erratum to “A watershed model of individual differences in fluid intelligence” [Neuropsychologia 91 (2016) 186–198]","year":2017,"lang":"en","type":"erratum","venue":"Neuropsychologia","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Baycrest Hospital","funders":"Biotechnology and Biological Sciences Research Council; Wellcome Trust","keywords":"Psychology; Watershed; Fluid intelligence; Neuroscience; Cognition; Computer science; 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":[0.00206604,0.001099723,0.0008775155,0.001792378,0.001415741,0.002714051,0.00260876,0.002851144,0.06705412],"category_scores_gemma":[0.0243348,0.0006525988,0.00116808,0.001534616,0.001645028,0.002775264,0.001429798,0.004395547,0.02294891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001282735,"about_ca_system_score_gemma":0.002663262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008954536,"about_ca_topic_score_gemma":0.01156371,"domain_scores_codex":[0.9991666,0.0001673822,0.0001560282,0.0001654455,0.0003106059,0.00003386956],"domain_scores_gemma":[0.9944122,0.002246025,0.0002641092,0.0004294502,0.002403929,0.0002442874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002737012,0.000006413628,0.0001221244,0.00009121712,0.00001257995,0.000187982,0.00005313087,0.0004025544,0.0001013712,0.02018345,0.9605918,0.01822011],"study_design_scores_gemma":[0.00006069784,0.00002943983,0.000833359,0.0003113495,0.00005105269,0.000772618,0.00009908919,0.005530382,0.0006817082,0.05999264,0.9315604,0.00007741967],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.002081251,0.005195221,0.07397977,0.1167649,0.7421429,0.0001356896,0.006870427,0.001822655,0.05100726],"genre_scores_gemma":[0.04335462,0.01778453,0.1116888,0.05276031,0.1219745,0.0004468026,0.01275787,0.004682405,0.6345502],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.06705412,"threshold_uncertainty_score":0.2243184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1638571616234721,"score_gpt":0.3883942908832086,"score_spread":0.2245371292597365,"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."}}