{"id":"W4398330871","doi":"10.7910/dvn/o7eggp/zshq5y","title":"childmarr_3Oct2019-1.tab","year":2019,"lang":"de","type":"dataset","venue":"Harvard Dataverse","topic":"Demographic Trends and Gender Preferences","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Statistics; Geography; Demography; Genealogy; Mathematics; History; Sociology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001475271,0.0009655367,0.00114812,0.0005742587,0.00103373,0.0007523131,0.003424797,0.001162329,0.210473],"category_scores_gemma":[0.0002931006,0.0009252935,0.0006015102,0.001101581,0.0009809425,0.0007623532,0.001174198,0.001304923,0.8706482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001220399,"about_ca_system_score_gemma":0.0007937644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003504348,"about_ca_topic_score_gemma":0.004884115,"domain_scores_codex":[0.9932433,0.0007928675,0.0008978187,0.001790117,0.001757599,0.001518347],"domain_scores_gemma":[0.9950885,0.0003338042,0.0007418788,0.002989343,0.0001739353,0.000672537],"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":[0.00005608055,0.0002709502,0.000396601,0.0001548893,0.000470873,0.0000572305,0.001361023,0.000003793323,0.000001674965,0.000555345,0.9948785,0.001792982],"study_design_scores_gemma":[0.0007778374,0.000151343,0.0005983768,0.000224264,0.0006182333,0.00000528213,0.002240607,0.00001191959,0.00000128816,0.0002691302,0.9939395,0.001162212],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001130114,0.00005028057,0.00001589112,0.00008826085,0.009493819,0.0007282699,0.978915,0.0001150011,0.01048045],"genre_scores_gemma":[0.0010018,0.01316799,0.0001372468,0.0007932301,0.003324136,0.00003355769,0.9720346,0.00005583795,0.00945159],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6601752,"threshold_uncertainty_score":0.9993198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03715946658548194,"score_gpt":0.2863756100924306,"score_spread":0.2492161435069486,"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."}}