{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006681975,0.00005653295,0.00008688853,0.00008046734,0.0002885773,0.00005466693,0.0002949322,0.00003061637,0.00005103863],"category_scores_gemma":[0.0005237035,0.00003183619,0.00005148331,0.0003466097,0.0006743405,0.000007675259,0.00001040311,0.00006625108,0.00000115802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005723915,"about_ca_system_score_gemma":0.0003435953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002171332,"about_ca_topic_score_gemma":0.001813722,"domain_scores_codex":[0.9993082,0.00007516771,0.0002499691,0.00008529368,0.0001871078,0.0000942219],"domain_scores_gemma":[0.9993225,0.00008629163,0.0003174001,0.0001439554,0.00007875207,0.00005109308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005070958,0.0001201714,0.2519871,0.0002192995,0.0002132021,0.000003662041,0.004093369,0.001851752,0.4959542,0.01697037,0.0458118,0.1827243],"study_design_scores_gemma":[0.0004398504,0.000784041,0.6787066,0.0002216019,0.00006908414,0.00001569131,0.005561161,0.1965864,0.07482999,0.001596268,0.04093252,0.0002567935],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889967,0.0002778958,0.0007034987,0.00861493,0.0003070331,0.0001025986,0.00001467324,0.000002560139,0.0009801515],"genre_scores_gemma":[0.9992766,0.00002846607,0.0002380152,0.0001314798,0.00003362279,0.000001760092,0.000004221693,0.0000035063,0.0002823456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4267195,"threshold_uncertainty_score":0.2484636,"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."}}