{"id":"W4390550620","doi":"10.1109/rmkmate59243.2023.10369927","title":"Fish Classification Using Convolutional Neural Network","year":2023,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Convolutional neural network; Computer science; Fish <Actinopterygii>; Artificial intelligence; Fishery; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001764174,0.00005297067,0.00004576444,0.00001712115,0.0001145132,0.00001944455,0.0001626664,0.00004765028,0.0002817794],"category_scores_gemma":[0.0000294649,0.00004825486,0.00001914105,0.0003588574,0.0001253571,0.0001182819,0.0002213327,0.00006025339,0.0007680157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001032995,"about_ca_system_score_gemma":0.000002135639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001496393,"about_ca_topic_score_gemma":0.0000146122,"domain_scores_codex":[0.999347,0.00002300283,0.00009933549,0.0001580051,0.0001658663,0.000206829],"domain_scores_gemma":[0.9997495,0.00003226614,0.00002601996,0.0001671106,0.000002768112,0.00002232283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000005927625,0.00002014076,0.7587444,0.0000039,0.000007259211,0.000005094668,0.00008603617,0.09264035,0.0269221,0.002980673,0.1148168,0.003767372],"study_design_scores_gemma":[0.00006142141,0.000009485419,0.8299313,0.000002706056,0.0000026761,0.000002423681,0.0000804463,0.1569119,0.001351881,0.005040722,0.006501142,0.0001038254],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9951128,0.00000122516,0.0004722343,0.001823579,0.0002785604,0.00005980392,0.000001574235,0.0007641809,0.001486056],"genre_scores_gemma":[0.9950758,0.000001930571,0.003796096,0.00008934616,0.00008707149,0.000006109848,0.000007943388,0.000005636764,0.0009300446],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1083157,"threshold_uncertainty_score":0.9871544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1248238035522188,"score_gpt":0.3027294023543918,"score_spread":0.177905598802173,"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."}}