{"id":"W4248823468","doi":"10.4095/301285","title":"Age, 1996 - The Golden Years (65 to 74 years) by Census Subdivision","year":2010,"lang":"en","type":"report","venue":"","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subdivision; Census; Genealogy; Demography; Geography; History; Archaeology; Sociology; Population","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00780654,0.0003324841,0.0005730852,0.0004915384,0.000181651,0.0008181593,0.00177937,0.0005958904,0.001042546],"category_scores_gemma":[0.002259373,0.0002034621,0.0003795291,0.0009195438,0.0001292439,0.000127364,0.0003701666,0.0008566442,0.001846352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005786671,"about_ca_system_score_gemma":0.0002710803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003119901,"about_ca_topic_score_gemma":0.001567001,"domain_scores_codex":[0.9917498,0.0001890902,0.001122339,0.00105391,0.005407433,0.0004774338],"domain_scores_gemma":[0.9961886,0.0009070067,0.0004963791,0.001487404,0.0006520156,0.0002685898],"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":[0.000008660174,0.00003008933,0.000236615,0.000003507072,0.00001978077,0.00002976802,0.0001590189,0.00003090522,0.0001319475,0.0001366393,0.8089699,0.1902432],"study_design_scores_gemma":[0.0001133632,0.00004840586,0.01401174,0.00003604442,0.00003966209,0.00001891984,0.0002954956,0.0004079902,0.00001018912,0.002555116,0.9821516,0.000311465],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4048581,0.002611925,0.007341818,0.003627984,0.01609346,0.002269828,0.001266435,0.0006694002,0.561261],"genre_scores_gemma":[0.6513263,0.002302667,0.002152918,0.001615032,0.001230902,0.00008335314,0.0005441849,0.000162147,0.3405825],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2464682,"threshold_uncertainty_score":0.9998707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1659764608342079,"score_gpt":0.4164718178443454,"score_spread":0.2504953570101375,"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."}}