{"id":"W4313216565","doi":"10.1016/j.ehb.2022.101216","title":"Surviving the Deluge: British servicemen in World War I","year":2022,"lang":"en","type":"article","venue":"Economics & Human Biology","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Infantry; Officer; Demography; Action (physics); Socioeconomic status; First world war; Military service; History; Political science; Sociology; Ancient history; Law; Population","routes":{"ca_aff":true,"ca_fund":true,"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":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002453179,0.0001039061,0.0002054857,0.000169806,0.001412811,0.00007491377,0.0008408017,0.00004503872,0.001550322],"category_scores_gemma":[0.00001372219,0.000124081,0.00008495068,0.0002997624,0.0003017838,0.00009736927,0.0004318624,0.0002772321,0.00003236386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002675478,"about_ca_system_score_gemma":0.00005470564,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.04539981,"about_ca_topic_score_gemma":0.664403,"domain_scores_codex":[0.9981171,0.0006255031,0.0003511403,0.0003627135,0.00006439407,0.0004791186],"domain_scores_gemma":[0.9993845,0.000107831,0.0001556306,0.0002898922,0.00001963167,0.00004251344],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000006365458,0.00007640443,0.7754921,0.000005616817,0.00005150762,0.000007873262,0.004823673,0.0003622654,0.000006756381,0.2123445,0.002133996,0.004688981],"study_design_scores_gemma":[0.0003411904,0.00003626721,0.4034285,0.000003494107,0.000009946181,0.000001293233,0.004589797,0.00008435726,0.000001472344,0.05139484,0.5398486,0.000260293],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9325405,0.0003191369,0.000003732799,0.001432798,0.0008508295,0.0003651662,0.00003130519,0.00004851159,0.06440807],"genre_scores_gemma":[0.9962198,0.0003048483,0.00002286459,0.001602469,0.0002420667,0.0001314623,0.00003534539,0.00001483302,0.001426322],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6190032,"threshold_uncertainty_score":0.9998872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02189712352117871,"score_gpt":0.2905423348839861,"score_spread":0.2686452113628074,"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."}}