{"id":"W4321238849","doi":"10.1111/fme.12614","title":"Parentage‐based tagging using mothers balances accuracy and cost for discriminating between natural and stocked recruitment for inland fisheries","year":2023,"lang":"en","type":"article","venue":"Fisheries Management and Ecology","topic":"Fish Ecology and Management Studies","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"New York Sea Grant, State University of New York","keywords":"Stocking; Fishery; Identification (biology); Fish <Actinopterygii>; Hatchery; Biology; Fisheries management; Ecology; Fishing","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":[0.002120577,0.0003040811,0.0003284336,0.0005173967,0.0003806614,0.0008924136,0.0004386517,0.0004400348,0.001614393],"category_scores_gemma":[0.004202419,0.0001914065,0.0001417114,0.0003697933,0.000313798,0.0004797852,0.0002776045,0.0002136428,0.0005328204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000919965,"about_ca_system_score_gemma":0.0006796009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03996176,"about_ca_topic_score_gemma":0.1439802,"domain_scores_codex":[0.9992513,0.0003054033,0.0000411026,0.0001337725,0.0001918874,0.00007653418],"domain_scores_gemma":[0.9973683,0.0008409449,0.0007245148,0.0002958965,0.0006544553,0.00011582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005256638,0.0001108173,0.9273676,0.0000675589,0.0001148256,0.00006458947,0.0002330494,0.0008450467,0.02885319,0.0001416058,0.0006471401,0.04102887],"study_design_scores_gemma":[0.00003143811,0.0004468553,0.9547503,0.00006982536,0.0002259681,0.0001986817,0.000322155,0.0204705,0.02208645,0.0001827049,0.001192058,0.0000229356],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911203,0.0002806504,0.006088179,0.0001384126,0.00002219635,0.00004917427,0.0002778557,0.00007365625,0.001949653],"genre_scores_gemma":[0.9894372,0.0001042327,0.008689323,0.00008075118,0.000007237637,0.00002727369,0.0002275708,0.00001207504,0.001414306],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03996176,"threshold_uncertainty_score":0.0794583,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07573470391221147,"score_gpt":0.3019015337545355,"score_spread":0.226166829842324,"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."}}