{"id":"W2529512423","doi":"10.1503/cmaj.1150112","title":"Are we consistent?","year":2016,"lang":"en","type":"letter","venue":"Canadian Medical Association Journal","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Abortion; Selection (genetic algorithm); Computer science; Data science; Operations research; Artificial intelligence; Pregnancy; Biology; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03763497,0.0007721118,0.002811683,0.002028182,0.0168406,0.01205636,0.007450559,0.04735303,0.0323282],"category_scores_gemma":[0.1585496,0.0009268687,0.001780547,0.001678405,0.02671908,0.02290761,0.007655475,0.09574712,0.009859775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02565118,"about_ca_system_score_gemma":0.05316169,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1287467,"about_ca_topic_score_gemma":0.2606835,"domain_scores_codex":[0.975045,0.006965447,0.001780131,0.003595577,0.008505879,0.004107965],"domain_scores_gemma":[0.9139508,0.03605105,0.004352068,0.003158461,0.02358485,0.01890284],"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":[0.00004173027,0.00004743872,0.00160362,0.0001618856,0.00005107159,0.0005936553,0.001935671,0.00003387416,0.00008934266,0.02239838,0.9471134,0.02593008],"study_design_scores_gemma":[0.00007085852,0.00004272605,0.001868066,0.002028459,0.00005893124,0.0008945069,0.01028801,0.0002021232,0.000120025,0.07362433,0.9106576,0.0001444181],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0001091902,0.000810096,0.00007275198,0.9942227,0.00378472,0.000003136428,0.00001998448,0.000007272094,0.0009701703],"genre_scores_gemma":[0.002479617,0.0008172934,0.0002435101,0.9911214,0.003477779,0.00001342683,0.00002231102,0.00001597267,0.001808666],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.1287467,"threshold_uncertainty_score":0.2559946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03758454759228576,"score_gpt":0.280068061098571,"score_spread":0.2424835135062853,"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."}}