{"id":"W2119097164","doi":"10.1109/icnn.1991.163352","title":"The potential of a neural network based sonar system in classifying fish","year":2002,"lang":"en","type":"article","venue":"","topic":"Underwater Acoustics Research","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hydro One (Canada)","funders":"","keywords":"Artificial neural network; Sonar; Artificial intelligence; Identification (biology); Linear discriminant analysis; Fish <Actinopterygii>; Discriminant; Computer science; Pattern recognition (psychology); Machine learning; Fishery; Biology; Ecology","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003639247,0.00006310196,0.00009389745,0.00004263049,0.0001423944,0.00007040432,0.0002345135,0.00003821474,0.001036006],"category_scores_gemma":[0.000009964912,0.00003846475,0.00003432763,0.0002285955,0.00007211955,0.00006292752,0.00001172532,0.0001341811,0.00005456742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005765273,"about_ca_system_score_gemma":0.00001576995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001148508,"about_ca_topic_score_gemma":0.005504133,"domain_scores_codex":[0.9988807,0.0001245438,0.0001887889,0.0001219409,0.0003208447,0.0003632241],"domain_scores_gemma":[0.9995121,0.000241476,0.00003137544,0.0001297813,0.00002531674,0.00005999093],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002783138,0.00000933688,0.1256382,0.00003923858,0.000006595997,0.00004838105,0.00002892329,0.8465044,0.00004014858,0.00001273722,0.002792208,0.02485201],"study_design_scores_gemma":[0.0001726014,0.00004092253,0.05290407,0.00001326088,0.000002330268,0.00000586925,0.0001265541,0.9461923,0.00001859993,0.00002161847,0.0004555685,0.00004628439],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.87055,0.001139447,0.03516176,0.003870397,0.001154428,0.001096399,0.00008568251,0.0001966082,0.08674534],"genre_scores_gemma":[0.9984549,0.00001336146,0.0009413925,0.00007731601,0.00007941784,7.246909e-7,0.000005984609,0.000002103133,0.0004248415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1279049,"threshold_uncertainty_score":0.9998772,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03315287607922401,"score_gpt":0.2217958887950016,"score_spread":0.1886430127157775,"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."}}