{"id":"W4242469323","doi":"10.31219/osf.io/tehu4","title":"Constructing a longitudinal database of Targeted Regulation of Abortion Providers laws","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Reproductive Health and Contraception","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Abortion; Business; Law; Database; Political science; Computer science; Pregnancy; Biology","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":[0.01441572,0.0003003173,0.0005696412,0.01981652,0.001187335,0.002114357,0.001419356,0.0008383815,0.005919559],"category_scores_gemma":[0.05091018,0.0007864101,0.0005520949,0.01654688,0.0004955697,0.002992608,0.002501013,0.001155817,0.002830966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003439439,"about_ca_system_score_gemma":0.01245137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0322322,"about_ca_topic_score_gemma":0.03661013,"domain_scores_codex":[0.9909455,0.002671823,0.002673845,0.001412887,0.001791396,0.0005044818],"domain_scores_gemma":[0.9011292,0.03532206,0.02685884,0.01001526,0.02440965,0.002264985],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003352638,0.000578729,0.7749863,0.003494096,0.0003000383,0.0005456108,0.008264679,0.002604302,0.002253461,0.006928095,0.08214733,0.1175621],"study_design_scores_gemma":[0.0001191931,0.0003881204,0.7222986,0.003078129,0.0003202569,0.0004563695,0.007442884,0.004957666,0.004736136,0.001929075,0.2540813,0.0001922541],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3186844,0.002421325,0.02186167,0.001710852,0.0001351162,0.004463705,0.6378314,0.001006452,0.01188497],"genre_scores_gemma":[0.2985473,0.002256866,0.0715307,0.0009310972,0.0001201369,0.01065276,0.6126552,0.0002315149,0.003074303],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0322322,"threshold_uncertainty_score":0.07623857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03814643463645765,"score_gpt":0.3246083015427018,"score_spread":0.2864618669062441,"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."}}