{"id":"W3023427038","doi":"10.3386/w21511","title":"Missing Unmarried Women","year":2015,"lang":"en","type":"preprint","venue":"National Bureau of Economic Research","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research","funders":"National Science Foundation","keywords":"Demography; Missing data; China; Geography; Marital status; Population; Sociology; Statistics; Mathematics","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":[0.001897871,0.0001505074,0.0003322208,0.0008320989,0.0007781928,0.0006075067,0.0004533521,0.0003685048,0.01258222],"category_scores_gemma":[0.01384205,0.0001644876,0.0003740724,0.0009010771,0.0003018461,0.0009215776,0.0009504139,0.0006031627,0.001553938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002217073,"about_ca_system_score_gemma":0.0005510871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004379803,"about_ca_topic_score_gemma":0.005394557,"domain_scores_codex":[0.9986364,0.0003956657,0.0001321282,0.0002592297,0.0003091862,0.0002673935],"domain_scores_gemma":[0.9935341,0.002025223,0.002491282,0.0009343491,0.0006170055,0.0003980113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002477739,0.0001002737,0.7673423,0.000478045,0.0001378375,0.0005236915,0.004414101,0.000379067,0.000795714,0.01245371,0.0252671,0.1878604],"study_design_scores_gemma":[0.00003301146,0.0002829836,0.8946472,0.0005975635,0.0001419939,0.003470149,0.008801597,0.001611655,0.001886815,0.0125767,0.07590811,0.00004211509],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9236853,0.003468785,0.008547026,0.004347703,0.0003403134,0.0001304091,0.02003251,0.0001029428,0.03934496],"genre_scores_gemma":[0.9845457,0.001808393,0.00161565,0.001483923,0.0001221366,0.00009665418,0.003674776,0.00001629673,0.006636445],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01258222,"threshold_uncertainty_score":0.04209167,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5617908358627705,"score_gpt":0.5681976840627753,"score_spread":0.006406848200004811,"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."}}