{"id":"W2077763322","doi":"10.1139/f03-102","title":"Change-in-ratio estimates of lobster exploitation rate using sampling concurrent with fishing","year":2003,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Fishing; Statistics; Sampling (signal processing); Environmental science; Bootstrapping (finance); Fishery; Sample size determination; Econometrics; Robustness (evolution); Mathematics; Ecology; Biology; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0034742,0.0004233997,0.0005206428,0.00231312,0.0002440454,0.0005337801,0.001214178,0.0003800157,0.0008269728],"category_scores_gemma":[0.02123621,0.0003727924,0.0004880453,0.00145512,0.0004473879,0.0006672197,0.0005727625,0.0006107911,0.0003025537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006275031,"about_ca_system_score_gemma":0.0003742192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01079127,"about_ca_topic_score_gemma":0.01088845,"domain_scores_codex":[0.9979258,0.0008372522,0.0001122985,0.0005106286,0.0005238908,0.00009016134],"domain_scores_gemma":[0.9849946,0.007487067,0.003730733,0.002284235,0.001241545,0.0002619342],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005241599,0.0001795128,0.7194743,0.0002081039,0.0005409241,0.0002216603,0.0004224888,0.05647514,0.007309764,0.001572597,0.0009983557,0.212073],"study_design_scores_gemma":[0.00004912801,0.0004448119,0.485401,0.00003621906,0.0001578546,0.000880789,0.0001314242,0.5044389,0.004849297,0.001817673,0.001666254,0.0001266626],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6482074,0.0005700773,0.3458653,0.0000522772,0.0000349799,0.0001902583,0.001053588,0.0009817034,0.003044363],"genre_scores_gemma":[0.8961635,0.0001264203,0.1017411,0.00002734457,0.0000334453,0.0001418213,0.001125315,0.00007737045,0.0005637238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01079127,"threshold_uncertainty_score":0.0214569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09195427833514612,"score_gpt":0.2933605185713971,"score_spread":0.2014062402362509,"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."}}