{"id":"W2986687892","doi":"10.1139/cjfas-2019-0093","title":"Using censored regression when estimating abundance with CPUE data to account for daily catch limits","year":2019,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine Bivalve and Aquaculture Studies","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Statistics; Regression; Ordinary least squares; Estimator; Catch per unit effort; Abundance (ecology); Regression analysis; Censored regression model; Econometrics; Cross-sectional regression; Fishery; Mathematics; Biology; Polynomial regression","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.02400906,0.001404577,0.001517294,0.001962157,0.0008088975,0.001439055,0.003403339,0.002047045,0.002984169],"category_scores_gemma":[0.09840748,0.0009013824,0.002510038,0.003159545,0.0009800227,0.002692854,0.001443514,0.002390159,0.0009595754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001276806,"about_ca_system_score_gemma":0.001710069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04692907,"about_ca_topic_score_gemma":0.03964734,"domain_scores_codex":[0.9883559,0.007133889,0.001059355,0.002097291,0.0009236711,0.0004299759],"domain_scores_gemma":[0.9573476,0.02979299,0.004640852,0.004904719,0.002876845,0.0004369583],"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":[0.0004972859,0.0002541671,0.3250385,0.0005438322,0.002185985,0.001109394,0.0007628109,0.5502982,0.002303918,0.01200595,0.006282193,0.09871776],"study_design_scores_gemma":[0.00008662295,0.0002243509,0.04697703,0.0001815477,0.0002311177,0.0002898241,0.0002625539,0.9238606,0.002099152,0.01554345,0.01008145,0.000162392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1226702,0.001097242,0.8668469,0.0006871557,0.0003239443,0.0002500432,0.002645714,0.003111663,0.002367162],"genre_scores_gemma":[0.6835722,0.0004917051,0.3038986,0.0009969737,0.0001577702,0.0007384895,0.004814064,0.001106301,0.004223831],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04692907,"threshold_uncertainty_score":0.1269736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06202634996273167,"score_gpt":0.2858443674014501,"score_spread":0.2238180174387184,"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."}}