{"id":"W4327711003","doi":"10.1139/cjfas-2022-0270","title":"Automatic classification of the phenotype textures of three <i>Thunnus</i> species based on the machine learning SVM algorithm","year":2023,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning; Shanghai Municipal Education Commission; National Natural Science Foundation of China","keywords":"Tuna; Support vector machine; Artificial intelligence; Pattern recognition (psychology); Thunnus; Kernel (algebra); Computer science; Mathematics; Fishery; Biology; Fish <Actinopterygii>","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":[0.0002334904,0.0004107413,0.0003449135,0.0008910792,0.0001921152,0.0004020229,0.0002271815,0.0002859004,0.0005974399],"category_scores_gemma":[0.0003527423,0.0001255764,0.0004210654,0.0005038586,0.0001744678,0.0003583811,0.0002198966,0.0002213256,0.0001460478],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002150304,"about_ca_system_score_gemma":0.0002424588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003177446,"about_ca_topic_score_gemma":0.00271913,"domain_scores_codex":[0.9998699,0.0000150214,0.000009118093,0.00004416657,0.00003693388,0.00002488377],"domain_scores_gemma":[0.9998486,0.00003030805,0.00002643734,0.00001675468,0.0000590817,0.00001867937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004716703,0.0001568101,0.03405723,0.0001796115,0.0000671793,0.0002395131,0.0002409476,0.01982921,0.4045013,0.0007959821,0.001494236,0.5379663],"study_design_scores_gemma":[0.00002414884,0.0001792136,0.08822929,0.00002014114,0.00004823042,0.000242653,0.0002715802,0.8553039,0.05368927,0.0007104875,0.001245921,0.00003516559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7822593,0.0001973933,0.2145546,0.00008216155,0.00003549034,0.00004946131,0.0002389642,0.00113222,0.001450355],"genre_scores_gemma":[0.9047502,0.0001033446,0.09375595,0.00002367765,0.00000854339,0.00003190726,0.0003714963,0.00004005465,0.0009148477],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003177446,"threshold_uncertainty_score":0.006317854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03432608753546257,"score_gpt":0.236071757441881,"score_spread":0.2017456699064184,"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."}}