{"id":"W3121162340","doi":"10.1111/1755-0998.13331","title":"Predicting sample success for large‐scale ancient DNA studies on marine mammals","year":2021,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Forensic and Genetic Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Trent University","funders":"Horizon 2020; Norges Forskningsråd; Vetenskapsrådet; Trinity College Dublin; Universitetet i Oslo; European Commission; Universitetet i Tromsø","keywords":"Context (archaeology); Biology; Sample (material); Ancient DNA; Evolutionary biology; Scale (ratio); Range (aeronautics); Ecology; Zoology; Paleontology; Geography; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02659965,0.0005793389,0.0007300204,0.001984666,0.001239722,0.00143243,0.0008736191,0.0009048842,0.002647667],"category_scores_gemma":[0.07007769,0.0004460923,0.0008711058,0.001809106,0.001007878,0.001272649,0.00174607,0.0006890407,0.0008543229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003352881,"about_ca_system_score_gemma":0.0005582682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001844171,"about_ca_topic_score_gemma":0.005293339,"domain_scores_codex":[0.9854623,0.009367208,0.0009999489,0.001885706,0.001874885,0.0004099056],"domain_scores_gemma":[0.9106903,0.07111154,0.007904339,0.005594373,0.003156269,0.001543164],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003295115,0.0000629721,0.9708183,0.0001319923,0.0003064182,0.0002356834,0.0008694762,0.001065048,0.003874947,0.0002445543,0.0003195795,0.02174161],"study_design_scores_gemma":[0.00002259741,0.0007742035,0.9827775,0.00008637263,0.0003595001,0.0005105814,0.001591511,0.005609307,0.004389176,0.001024728,0.002827429,0.00002707415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9719455,0.0008443614,0.0245704,0.0001914649,0.00003491453,0.0002394585,0.001039902,0.00008027919,0.001053658],"genre_scores_gemma":[0.97574,0.0003166738,0.02193352,0.00006531923,0.00002666074,0.0002435508,0.001114204,0.00003921234,0.0005209591],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02659965,"threshold_uncertainty_score":0.1406741,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01539006984755836,"score_gpt":0.3013084217386301,"score_spread":0.2859183518910718,"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."}}