{"id":"W1481382778","doi":"10.1109/pacrim.1993.407263","title":"Use of image processing in the development of a dynamic model for fish processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Water Quality Monitoring Technologies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Image processing; Fish <Actinopterygii>; Computer science; Data processing; Image (mathematics); Artificial intelligence; Finite element method; Fish processing; Computer vision; Engineering; Biology; Fishery","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002559851,0.00006980092,0.00009572747,0.00003327308,0.00004951994,0.00002201855,0.0002787648,0.00003840941,0.00001071144],"category_scores_gemma":[0.00006315326,0.00004689757,0.00001706559,0.0001696471,0.0001394617,0.0002948301,0.0001102315,0.00004981827,0.000001669956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007281052,"about_ca_system_score_gemma":0.000007789352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002435257,"about_ca_topic_score_gemma":0.0001255599,"domain_scores_codex":[0.999253,0.00001117725,0.0002675862,0.0001390372,0.0001806468,0.0001486052],"domain_scores_gemma":[0.9997206,0.00002571842,0.00009143981,0.0001449753,0.000009246785,0.000008023388],"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":[0.00003206557,0.0008583764,0.02525624,0.0008265488,0.000007686085,0.000001537804,0.06647898,0.009002202,0.2391232,0.00005002695,0.0008500497,0.6575131],"study_design_scores_gemma":[0.0001428006,0.00001772908,0.01307238,0.0000712464,0.000004097056,7.121881e-7,0.000908745,0.9348294,0.04992685,0.0008466252,0.00006622591,0.0001131557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9545095,0.000007119135,0.04467344,0.0003352923,0.000004916683,0.0002165555,0.000001838887,0.00004086176,0.0002104524],"genre_scores_gemma":[0.7163159,9.410377e-7,0.2834808,0.00001543007,7.936573e-7,0.00002852674,4.734133e-7,0.00000441473,0.0001527422],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9258272,"threshold_uncertainty_score":0.1912427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1036399351360651,"score_gpt":0.292198014356278,"score_spread":0.1885580792202129,"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."}}