{"id":"W2037967026","doi":"10.1016/j.fishres.2004.11.002","title":"Stock discrimination of Lake Winnipeg walleye based on Fourier and wavelet description of scale outline signals","year":2004,"lang":"en","type":"article","venue":"Fisheries Research","topic":"Fish Biology and Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Manitoba; Fisheries and Oceans Canada","funders":"","keywords":"Wavelet; Mathematics; Fourier transform; Linear discriminant analysis; Scale (ratio); Pattern recognition (psychology); Statistics; Fourier analysis; Discriminant; Wavelet transform; Artificial intelligence; Computer science; Geography; Cartography; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001808176,0.0001532229,0.0001210848,0.0005545016,0.0001657294,0.0003431961,0.0001173769,0.0001372331,0.0004794562],"category_scores_gemma":[0.0006224453,0.0001058492,0.00008611045,0.0002595767,0.0001352024,0.0002338908,0.000216625,0.00008444716,0.00008256279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003531084,"about_ca_system_score_gemma":0.0002762443,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0537726,"about_ca_topic_score_gemma":0.1162862,"domain_scores_codex":[0.9999626,0.000005656561,0.000001866834,0.00000749134,0.000008845554,0.00001358692],"domain_scores_gemma":[0.9998232,0.00004186462,0.00002932835,0.000006140536,0.00006034522,0.00003925645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002561471,0.00004870793,0.6618186,0.00006021226,0.0001274594,0.0003435542,0.0006525444,0.002951326,0.2850981,0.0005037825,0.0002954491,0.04553875],"study_design_scores_gemma":[0.000009217577,0.0000523353,0.9873946,0.000005070555,0.00003099342,0.00007252219,0.0001749582,0.008536272,0.003586991,0.00004130425,0.00008666279,0.000009160266],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996217,0.00001799886,0.000167303,0.000003909707,9.356989e-7,0.000001233404,0.00002997578,0.000001634456,0.0001551981],"genre_scores_gemma":[0.9992365,0.00003124115,0.0004210008,0.000003755069,7.016385e-7,0.000001140782,0.00008337569,0.000002073354,0.0002202442],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9462274,"threshold_uncertainty_score":0.1069192,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09326805865132717,"score_gpt":0.2944512023288422,"score_spread":0.201183143677515,"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."}}