{"id":"W2063393451","doi":"10.1139/f08-208","title":"Performance of artificial neural networks and discriminant analysis in predicting fishing tactics from multispecific fisheries","year":2009,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Marine and fisheries research","field":"Environmental Science","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Fishing; Linear discriminant analysis; Artificial neural network; Fishery; Sample (material); Statistics; Geography; Machine learning; Artificial intelligence; Computer science; Mathematics; Biology","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":[0.009562424,0.002011385,0.0009763756,0.003106968,0.0004282848,0.001045617,0.0005866007,0.001331598,0.0005807572],"category_scores_gemma":[0.01596383,0.0003816314,0.0007274093,0.001257863,0.0004888687,0.001399228,0.001026084,0.0009081798,0.0003387318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007044086,"about_ca_system_score_gemma":0.0007279071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009967041,"about_ca_topic_score_gemma":0.00552641,"domain_scores_codex":[0.9973803,0.001487302,0.0002501185,0.0003983951,0.0003119904,0.0001718485],"domain_scores_gemma":[0.982447,0.01504672,0.00067545,0.0004551947,0.001034301,0.0003414022],"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":[0.003903108,0.0008206153,0.1368157,0.0002481509,0.0007375708,0.0001670544,0.0002155625,0.5053999,0.003733536,0.0006007483,0.001228015,0.34613],"study_design_scores_gemma":[0.00002331731,0.0001514442,0.01077098,0.00001730529,0.0000430359,0.00002243788,0.00005542825,0.9876407,0.0008229148,0.0003381951,0.00009492812,0.00001945295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9547231,0.001573151,0.040305,0.0002583789,0.0001010522,0.00006129633,0.0002873683,0.0003041784,0.002386408],"genre_scores_gemma":[0.9817738,0.0002582,0.01669767,0.0000572445,0.00003717263,0.00004033685,0.0003834198,0.00001808421,0.0007340715],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009967041,"threshold_uncertainty_score":0.0505715,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02562150961293579,"score_gpt":0.2333576376821107,"score_spread":0.2077361280691749,"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."}}