{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003162792,0.0001514133,0.0001452844,0.0001537498,0.0002549279,0.00008430046,0.0001439799,0.000172763,0.000003805499],"category_scores_gemma":[0.00006985066,0.000145762,0.0000817678,0.0002285827,0.00003970796,0.00001045583,0.00001979589,0.00007609642,0.000008638047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004560605,"about_ca_system_score_gemma":0.0001269056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002649163,"about_ca_topic_score_gemma":0.000393125,"domain_scores_codex":[0.9989395,0.00009489337,0.0002757578,0.0003951234,0.00008773848,0.000207005],"domain_scores_gemma":[0.9990298,0.00002669187,0.0001262796,0.0004153101,0.0003544952,0.00004739433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000472671,0.0002525615,0.0003501784,0.0006633029,0.0001534222,4.815694e-7,0.0003349394,0.004649569,0.9810651,0.001563174,0.003413897,0.007080738],"study_design_scores_gemma":[0.003156327,0.0001867696,0.0003112764,0.0001315661,0.00005332723,0.000005422136,0.0009449042,0.1934456,0.7469722,0.0000133183,0.05448586,0.0002934536],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3293171,0.0003435189,0.6663832,0.00006259591,0.001043992,0.0007303077,0.00008590748,0.00007901042,0.001954385],"genre_scores_gemma":[0.9947052,0.00000352023,0.0003246536,0.00006197538,0.0001943041,0.0004427794,0.0003650963,0.00002535168,0.003877127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6660586,"threshold_uncertainty_score":0.5944003,"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."}}