{"id":"W7097541841","doi":"","title":"Messin &amp;apos; with Texas Deriving Mother&amp;apos;s Maiden Names Using Public Records","year":2005,"lang":"en","type":"article","venue":"","topic":"Names, Identity, and Discrimination Research","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Insider; Certainty; Quarter (Canadian coin); Toponymy; Entropy (arrow of time); State (computer science)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"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":[0.001250872,0.0003415982,0.0003442152,0.001621107,0.0009292401,0.001610289,0.0006300588,0.0004354265,0.008687994],"category_scores_gemma":[0.01141146,0.0003011218,0.0002457408,0.001159278,0.0006546215,0.002393382,0.001937018,0.0007692376,0.004570209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009490715,"about_ca_system_score_gemma":0.00163341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006054786,"about_ca_topic_score_gemma":0.007743396,"domain_scores_codex":[0.9987714,0.0001925624,0.0001099816,0.00034352,0.0004764218,0.000106136],"domain_scores_gemma":[0.9938273,0.001322068,0.001396091,0.002482735,0.0008453038,0.0001265112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0006552824,0.0001587682,0.1348489,0.000336641,0.0001330223,0.001185965,0.005510687,0.01608756,0.02777937,0.04275395,0.04415102,0.7263987],"study_design_scores_gemma":[0.00008321947,0.0003208843,0.09216502,0.0002821619,0.0001565586,0.001966032,0.003131671,0.3794814,0.2136277,0.04794698,0.2606077,0.0002307696],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4519354,0.0005251152,0.4738299,0.003614739,0.0002599673,0.0002981477,0.01303944,0.02178787,0.03470952],"genre_scores_gemma":[0.8020091,0.0003618908,0.1626555,0.0001768912,0.0001012997,0.0001313438,0.006318648,0.0004624116,0.02778286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008687994,"threshold_uncertainty_score":0.02906424,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3754935822674945,"score_gpt":0.4715170041141752,"score_spread":0.09602342184668072,"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."}}