{"id":"W4317824266","doi":"10.55365/1923.x2022.20.94","title":"Forensic Demography: An Overlooked Area of Practice among Applied Demographers","year":2022,"lang":"en","type":"article","venue":"Review of Economics and Finance","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Damages; Forensic science; Value (mathematics); Personal injury; Sociology; Law; Criminology; Actuarial science; Political science; History; Economics; Computer science; Archaeology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0019349,0.0001369799,0.0004863477,0.0001189383,0.0003243088,0.00001782657,0.0003284886,0.00003756043,0.00003950211],"category_scores_gemma":[0.00004910233,0.0001560173,0.0001942736,0.0004632933,0.0004956566,0.0002744141,0.0001026169,0.0001254334,5.111031e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003109239,"about_ca_system_score_gemma":0.00006951245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008225983,"about_ca_topic_score_gemma":0.0006153593,"domain_scores_codex":[0.9985787,0.0001591357,0.0005477155,0.0003264698,0.0001468598,0.0002411545],"domain_scores_gemma":[0.9986014,0.00009704261,0.0008105331,0.0003657143,0.00007542042,0.00004986479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005754563,0.0003091942,0.06911033,0.00140781,0.0002095019,0.000003406173,0.001699633,0.0004687701,0.00000325723,0.7744527,0.001070977,0.1512069],"study_design_scores_gemma":[0.000770829,0.0002846827,0.1498269,0.0007114044,0.0003774519,0.000003035735,0.004126508,0.0003749449,0.00001610499,0.01546913,0.8273655,0.0006735731],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9140927,0.05567818,0.00002615336,0.000388584,0.0002262175,0.001047314,0.00006299084,0.00001752528,0.02846039],"genre_scores_gemma":[0.6355596,0.3627463,0.001075628,0.0004860715,0.00001960468,0.00007281892,0.000008928962,0.000009912015,0.00002109777],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8262945,"threshold_uncertainty_score":0.6362199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01650517939104779,"score_gpt":0.2623162683576714,"score_spread":0.2458110889666236,"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."}}