{"id":"W4409424225","doi":"10.1016/j.knosys.2025.113418","title":"FishDetectLLM: Multimodal instruction tuning with large language models for fish detection","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"Identification and Quantification in Food","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Fundamental Research Funds for the Central Universities; Sichuan Province Science and Technology Support Program; International S and T Cooperation Program of Sichuan Province; Natural Science Foundation of Xinjiang Province","keywords":"Fish <Actinopterygii>; Computer science; Artificial intelligence; Natural language processing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009325511,0.002159808,0.0008005713,0.0006111655,0.0003612218,0.0009102157,0.003559183,0.001553734,0.005365161],"category_scores_gemma":[0.004156159,0.0006525217,0.001232129,0.0003769562,0.0006106619,0.002849882,0.00267188,0.002884609,0.002851604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001132348,"about_ca_system_score_gemma":0.001199922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009887322,"about_ca_topic_score_gemma":0.01689204,"domain_scores_codex":[0.9993476,0.0001128521,0.00003711151,0.0003112769,0.000114835,0.00007620622],"domain_scores_gemma":[0.9992583,0.0003814763,0.00006155921,0.0001301901,0.0001093153,0.0000590104],"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":[0.000711255,0.000728946,0.005587185,0.0005352215,0.0003101077,0.0004593531,0.0003209787,0.2695532,0.03933528,0.003874018,0.04893646,0.6296479],"study_design_scores_gemma":[0.00003756897,0.00008516724,0.000298845,0.0000156343,0.00002130952,0.00003949382,0.00003035837,0.986279,0.007523384,0.002757492,0.002886746,0.00002496775],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.109468,0.001670271,0.7468005,0.001014157,0.0005313366,0.0003666133,0.003672949,0.1304707,0.006005432],"genre_scores_gemma":[0.5598534,0.0003496658,0.4165414,0.001859292,0.0001256429,0.0009066814,0.009315405,0.002725314,0.008323108],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009887322,"threshold_uncertainty_score":0.01965952,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01325727658793245,"score_gpt":0.2699773498572084,"score_spread":0.256720073269276,"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."}}