{"id":"W2790095428","doi":"10.1002/sim.7611","title":"Discrimination surfaces with application to region‐specific brain asymmetry analysis","year":2018,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Morphological variations and asymmetry","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fundação para a Ciência e a Tecnologia; University of British Columbia; University of South Carolina; National Science Foundation","keywords":"Estimator; Statistic; Asymmetry; Brain asymmetry; Mathematics; Statistics; Confidence interval; Pattern recognition (psychology); Psychology; Computer science; Artificial intelligence; Cognitive psychology; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006497925,0.0001377571,0.0003302766,0.0004456807,0.00008774098,0.00001561684,0.0001584099,0.00006084922,0.0002378023],"category_scores_gemma":[0.001018935,0.00009461743,0.00001763875,0.002006306,0.000179958,0.00003981011,0.00003067433,0.0001222638,0.00003024539],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007047843,"about_ca_system_score_gemma":0.00001371186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001208236,"about_ca_topic_score_gemma":0.0006142854,"domain_scores_codex":[0.9986442,0.00008832163,0.0003820697,0.000327441,0.0003619326,0.0001960414],"domain_scores_gemma":[0.9981962,0.0009538793,0.0001523999,0.0003834096,0.0002202716,0.00009383495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005079739,0.0001421521,0.01119262,0.00003677222,0.0000964635,0.00001966511,0.0009066905,0.00004603941,0.0002708841,0.8885472,0.08654499,0.01214572],"study_design_scores_gemma":[0.002450617,0.002094636,0.3762445,0.0002547332,0.0009098116,0.00001855053,0.003272576,0.02175186,0.0003028207,0.5679638,0.02389815,0.0008378474],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0143563,0.00002158483,0.9773297,0.003208308,0.00006464813,0.0002981701,0.00003944528,0.0000374491,0.004644369],"genre_scores_gemma":[0.740939,0.00001197763,0.2578883,0.0003929315,0.0001616489,0.000031727,0.00009802187,0.00001299316,0.0004634054],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7265827,"threshold_uncertainty_score":0.3858387,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04038446706239159,"score_gpt":0.3465978518653931,"score_spread":0.3062133848030015,"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."}}