{"id":"W4405710218","doi":"10.1101/2024.12.16.628779","title":"Simulation-based inference for close-kin mark-recapture: implications for small populations and nonrandom mating","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mark and recapture; Mating; Inference; Biology; Statistics; Evolutionary biology; Zoology; Econometrics; Demography; Computer science; Mathematics; Artificial intelligence; Population; Sociology","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":[0.02588648,0.0005171389,0.001372867,0.001255303,0.001028023,0.001981368,0.002237066,0.001654323,0.00136794],"category_scores_gemma":[0.2239862,0.0008043124,0.0008593717,0.001440777,0.002914291,0.003293726,0.002049369,0.002148664,0.0001604786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002133671,"about_ca_system_score_gemma":0.001971708,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02684872,"about_ca_topic_score_gemma":0.0177501,"domain_scores_codex":[0.9868131,0.01055519,0.0004864105,0.0008938361,0.001068338,0.0001831021],"domain_scores_gemma":[0.7101913,0.2724202,0.005654416,0.007803689,0.003276038,0.0006542809],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009602074,0.00003446516,0.02015628,0.0001422962,0.0001747808,0.0001551622,0.0005546514,0.8591549,0.0005797494,0.08457801,0.0005047828,0.03386896],"study_design_scores_gemma":[0.00001143957,0.0000150444,0.00109423,0.00002350055,0.00001077043,0.00004357763,0.00002975565,0.9566975,0.0001562708,0.04160626,0.0002985159,0.00001313803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0392513,0.0004384615,0.9587268,0.000531559,0.00002360524,0.00005120078,0.00003843739,0.0002088312,0.0007298485],"genre_scores_gemma":[0.6057696,0.0005455322,0.3922126,0.0003931327,0.00005950435,0.0002661369,0.0001666425,0.0001422191,0.0004446186],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02684872,"threshold_uncertainty_score":0.1369024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03500283544107519,"score_gpt":0.3081922547512574,"score_spread":0.2731894193101823,"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."}}