{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001867937,0.00004842994,0.00008383996,0.00007311393,0.00002161578,0.0001502572,0.0004881943,0.00003417191,0.00009794409],"category_scores_gemma":[0.00002885145,0.00003961519,0.00004633715,0.0004163212,0.00008440376,0.0004814335,0.0005055961,0.00002739535,0.00005909673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003958222,"about_ca_system_score_gemma":0.000001838584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004934002,"about_ca_topic_score_gemma":0.00002940194,"domain_scores_codex":[0.9993395,0.000005073889,0.0001617844,0.0002772966,0.0001330656,0.00008325165],"domain_scores_gemma":[0.9992393,0.00007934377,0.00002405107,0.0006415332,0.000003961561,0.00001184282],"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":[0.00002190164,0.0004071873,0.2104131,0.000252363,0.003222336,0.00000560889,0.001201291,0.01058525,0.03133227,0.005802102,0.2728311,0.4639255],"study_design_scores_gemma":[0.0001628228,0.00007811996,0.2102514,0.00002462203,0.0009351085,0.000001455663,0.0003587659,0.6590019,0.04686907,0.0143402,0.0675047,0.0004718278],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4236384,0.000008666417,0.5729855,0.00102477,0.0002613778,0.0001709802,0.0004756998,0.0004276378,0.001006926],"genre_scores_gemma":[0.9903985,0.000001697409,0.007909826,0.0000154288,0.00002120673,0.000005550939,0.000270913,0.000004765706,0.00137215],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6484166,"threshold_uncertainty_score":0.1615461,"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."}}