{"id":"W4401372362","doi":"10.25144/23542","title":"DIGITAL COMPUTER ANALYSIS OF ECHO SOUNDER DATA FOR FISH IDENTIFICATION","year":2024,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Queen's University","keywords":"Shoal; Echo sounding; Computer science; Echo (communications protocol); Identification (biology); Fish <Actinopterygii>; Sonar; Sampling (signal processing); Target strength; Pattern recognition (psychology); Computer vision; Remote sensing; Artificial intelligence; Fishery; Geography; Ecology; Geology; Oceanography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008107105,0.0006091859,0.0005369684,0.003452542,0.0003616581,0.001049876,0.0004552612,0.0002291682,0.03677496],"category_scores_gemma":[0.004087348,0.0001725969,0.0002855647,0.002428961,0.0002290826,0.0005922929,0.0004381337,0.000505398,0.01016744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003499395,"about_ca_system_score_gemma":0.0005539308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001718215,"about_ca_topic_score_gemma":0.001847901,"domain_scores_codex":[0.9993057,0.00009781993,0.00006192208,0.0001343778,0.0003563924,0.00004370983],"domain_scores_gemma":[0.9979639,0.0007331565,0.00007990801,0.0003264177,0.0008526944,0.00004387091],"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":[0.000475377,0.0001179762,0.003057363,0.0004940697,0.00007917923,0.0001697606,0.0002511877,0.002231922,0.1617203,0.004581751,0.01900693,0.8078142],"study_design_scores_gemma":[0.0001823388,0.001071053,0.05448321,0.0004616791,0.0003994471,0.002186568,0.0004736749,0.1588633,0.3956174,0.009948137,0.3761002,0.0002130457],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05583441,0.001226208,0.8807314,0.0003469159,0.0006188394,0.0007750689,0.00554139,0.02110388,0.03382204],"genre_scores_gemma":[0.2429256,0.001670894,0.6984365,0.0004372306,0.0002830945,0.001298624,0.006733919,0.001433293,0.04678081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03677496,"threshold_uncertainty_score":0.1230245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07494838173206772,"score_gpt":0.3165205983819367,"score_spread":0.241572216649869,"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."}}