{"id":"W1964870735","doi":"10.1139/f05-153","title":"An individual-based modeling approach to spawning-potential per-recruit models: an application to blue crab (<i>Callinectes sapidus</i>) in Chesapeake Bay","year":2005,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Maryland Sea Grant, University of Maryland; U.S. Department of Commerce","keywords":"Callinectes; Fishery; Chesapeake bay; Bay; Fishing; Population; Range (aeronautics); Population size; Biology; Ecology; Environmental science; Estuary; Geography; Crustacean; Demography; Engineering","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":[0.001439166,0.0007754801,0.0008536039,0.000736231,0.0009107968,0.001100424,0.002195246,0.00147948,0.002074128],"category_scores_gemma":[0.002507036,0.0006500215,0.001048833,0.0007298994,0.0004091322,0.0006066782,0.0008893624,0.0009500015,0.0002469907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001640011,"about_ca_system_score_gemma":0.002009295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1287083,"about_ca_topic_score_gemma":0.1085439,"domain_scores_codex":[0.9997106,0.0001294948,0.00001791343,0.00007127329,0.0000344733,0.00003631989],"domain_scores_gemma":[0.998968,0.0005916954,0.0001413121,0.00004274242,0.0001609457,0.00009521977],"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":[0.00001826578,0.0000216479,0.003957981,0.00000959844,0.00005069712,0.00003847616,0.000035886,0.9930276,0.0002022243,0.0008470679,0.0001369263,0.001653528],"study_design_scores_gemma":[0.000004780083,0.000009479045,0.000498035,0.000002107631,0.00001120573,0.000007283572,0.000009408418,0.9990185,0.00002218036,0.0003237468,0.00008801545,0.00000532166],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7060632,0.0003919364,0.2848804,0.0008281242,0.00006621523,0.0001425911,0.001163535,0.0005241769,0.005939731],"genre_scores_gemma":[0.9327555,0.0002445139,0.06159159,0.0001478848,0.00004592656,0.0002250348,0.0004163895,0.00008972317,0.00448344],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1287083,"threshold_uncertainty_score":0.2559183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03898730135593983,"score_gpt":0.2530889052368233,"score_spread":0.2141016038808835,"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."}}