{"id":"W2007949824","doi":"10.1007/s10818-005-0494-x","title":"Using Genetic Algorithms to Estimate and Validate Bioeconomic Models: The Case of the Ibero-atlantic Sardine Fishery","year":2006,"lang":"en","type":"article","venue":"Journal of Bioeconomics","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Sardine; Bioeconomics; Parametric statistics; Perspective (graphical); Fishery; Computer science; Fisheries management; Econometrics; Work (physics); Economics; Mathematics; Artificial intelligence; Engineering; Fish <Actinopterygii>; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007602155,0.001215635,0.001078914,0.001642902,0.001032537,0.002446959,0.001410952,0.002681443,0.0008519381],"category_scores_gemma":[0.02736937,0.0007924058,0.000751175,0.001050941,0.001214737,0.001368243,0.0008898354,0.001467654,0.0001415818],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002839971,"about_ca_system_score_gemma":0.002860567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07935476,"about_ca_topic_score_gemma":0.04912392,"domain_scores_codex":[0.9988576,0.0007377074,0.00006834859,0.0001222924,0.0001134722,0.0001006204],"domain_scores_gemma":[0.9838327,0.01357396,0.0008085052,0.0005509348,0.001084989,0.0001489167],"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":[0.00003437918,0.00003601965,0.003377558,0.00001010264,0.00005043421,0.00003313565,0.00002793563,0.9922809,0.00009074358,0.000829078,0.00007446716,0.003155182],"study_design_scores_gemma":[0.0000222371,0.00001505866,0.0004854492,0.000006675407,0.00001428847,0.000004598203,0.00002041592,0.9982265,0.0001037363,0.001044923,0.00005102794,0.000005051766],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9388984,0.0002829881,0.05723953,0.0006458146,0.00005007314,0.00005814782,0.0001482713,0.0002597051,0.002417039],"genre_scores_gemma":[0.964852,0.00007999543,0.03440435,0.00007492891,0.00001040839,0.00004096332,0.0001902714,0.00003575349,0.000311303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07935476,"threshold_uncertainty_score":0.1577857,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03072829072051741,"score_gpt":0.2761548005675331,"score_spread":0.2454265098470157,"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."}}