{"id":"W4395015241","doi":"10.1093/biomtc/ujae029","title":"Addressing age measurement errors in fish growth estimation from length-stratified samples","year":2024,"lang":"en","type":"article","venue":"Biometrics","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Memorial University of Newfoundland","keywords":"Estimator; Statistics; Computer science; Discretization; Stock assessment; Small area estimation; Stratified sampling; Estimation; Sample size determination; Observational error; Econometrics; Mathematics; Ecology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000551171,0.00009881383,0.0001032565,0.0004607099,0.00004719147,0.0002166007,0.0001920456,0.00007117595,0.001545383],"category_scores_gemma":[0.0005661335,0.00009047703,0.00003391783,0.003442851,0.00008224558,0.0002593247,0.0001557281,0.000150238,0.00008379448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003622096,"about_ca_system_score_gemma":0.00002265039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004918037,"about_ca_topic_score_gemma":0.001414803,"domain_scores_codex":[0.9984193,0.00004780249,0.0002020079,0.000295303,0.0008016635,0.0002339043],"domain_scores_gemma":[0.999617,0.0001216839,0.00002299486,0.0001537269,0.0000110693,0.00007348046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00002206469,0.0001589204,0.1707916,0.000105283,0.00002672666,0.0001719695,0.0006644922,0.00009224033,0.01143566,0.0001888223,0.01376237,0.8025799],"study_design_scores_gemma":[0.0004584203,0.0001234014,0.8345792,0.0000845351,0.00002104022,0.00000254293,0.0002204118,0.04015786,0.004307427,0.002992655,0.1165446,0.0005079563],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8561091,0.0003231851,0.01584188,0.001817174,0.0008003862,0.000704694,0.0001204824,0.0004274916,0.1238556],"genre_scores_gemma":[0.9946068,0.00006959058,0.004955808,0.00006262007,0.00003889009,0.00001633981,0.00005183199,0.00001786127,0.000180274],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8020719,"threshold_uncertainty_score":0.9993674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1835899347180771,"score_gpt":0.3214557153678229,"score_spread":0.1378657806497458,"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."}}