{"id":"W2122139432","doi":"10.1139/cjfas-2013-0401","title":"A peaked logistic-based selection curve","year":2014,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; University of New South Wales; NSW Recreational Fishing Trust","keywords":"Selection (genetic algorithm); Log-normal distribution; Statistics; Logistic regression; Logistic function; Mathematics; Hook; Fishing; Model selection; Biology; Fishery; Econometrics; Computer science; Artificial intelligence","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":[0.004287155,0.0008303109,0.0008044826,0.002060018,0.0008040537,0.002001949,0.001749982,0.001354478,0.01047335],"category_scores_gemma":[0.02347468,0.0003409882,0.001308981,0.001487592,0.001874846,0.00234574,0.00152306,0.00149013,0.00337775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001919899,"about_ca_system_score_gemma":0.001192651,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005245998,"about_ca_topic_score_gemma":0.002222937,"domain_scores_codex":[0.9981634,0.0006933048,0.00009209008,0.0004615288,0.000357216,0.0002325079],"domain_scores_gemma":[0.9915255,0.003872682,0.0009006215,0.0008085246,0.002314264,0.0005783742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0009281791,0.0002327553,0.1016559,0.0005709833,0.0003081784,0.00283866,0.001910791,0.1988164,0.01486517,0.409776,0.02353034,0.2445666],"study_design_scores_gemma":[0.0001031193,0.0003953166,0.04282415,0.0002534714,0.0001009389,0.004364584,0.0006212682,0.7016617,0.003465225,0.2231135,0.02281624,0.000280519],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2403392,0.001243778,0.7018347,0.001820534,0.0001385689,0.0005206927,0.001455316,0.002267414,0.05037971],"genre_scores_gemma":[0.9063089,0.0007153507,0.06895956,0.0005868836,0.00009160752,0.0004400733,0.001395682,0.0003973955,0.02110456],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01047335,"threshold_uncertainty_score":0.0350368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03050157033981473,"score_gpt":0.2435481279507961,"score_spread":0.2130465576109814,"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."}}