{"id":"W4378893568","doi":"10.47536/jcrm.v2i3.503","title":"Comparison of subjective and statistical methods of dive classification using data from a time-depth recorder attached to a gray whale (Eschrichtius robustus)","year":2000,"lang":"en","type":"article","venue":"The journal of cetacean research and management. Special issue","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Fisheries and Oceans Canada; Earthwatch Institute","keywords":"Linear discriminant analysis; Whale; Discriminant function analysis; Statistical analysis; Gray (unit); Marine mammal; Computer science; Artificial intelligence; Cartography; Geography; Statistics; Mathematics; Machine learning; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.01026001,0.0005359884,0.0002916991,0.00350289,0.0004366667,0.001347099,0.000518551,0.0004578158,0.0005753603],"category_scores_gemma":[0.03543185,0.0002030474,0.0004005643,0.001457945,0.0009052861,0.0008428748,0.001044361,0.0004374316,0.0003323244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004319462,"about_ca_system_score_gemma":0.0003825805,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002803661,"about_ca_topic_score_gemma":0.00953592,"domain_scores_codex":[0.9906167,0.003242488,0.001057784,0.0008220625,0.004077825,0.0001831175],"domain_scores_gemma":[0.9643331,0.01709697,0.004400573,0.002184085,0.01139888,0.0005864662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002245875,0.0006100583,0.5790364,0.001247274,0.0008016389,0.0002577086,0.007670495,0.00574819,0.08646125,0.001122393,0.001835566,0.3129631],"study_design_scores_gemma":[0.00005985098,0.001544576,0.9479439,0.0001708942,0.0001719066,0.0004960404,0.00408814,0.02486866,0.01656896,0.0007906511,0.003030549,0.0002658962],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.887016,0.0003733456,0.1063363,0.0001256572,0.0001158019,0.0004481081,0.0008839896,0.0002114134,0.004489443],"genre_scores_gemma":[0.9088758,0.0003209797,0.08691123,0.000147732,0.0001049099,0.0005175889,0.001488619,0.00009000877,0.001543163],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01026001,"threshold_uncertainty_score":0.05426073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1793503824086018,"score_gpt":0.4541439181071353,"score_spread":0.2747935356985334,"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."}}