{"id":"W2340594420","doi":"10.1111/2041-210x.12556","title":"Acoustic identification of Mexican bats based on taxonomic and ecological constraints on call design","year":2016,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Bat Biology and Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Engineering and Physical Sciences Research Council; Consejo Nacional de Ciencia y Tecnología; American Society of Mammalogists; Rufford Foundation; Idea Wild; Bat Conservation International","keywords":"Guild; Biodiversity; Identification (biology); Taxonomic rank; Ecology; Random forest; Genus; Global biodiversity; Bioacoustics; Biology; Machine learning; Computer science; Habitat; Taxon","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"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":[0.001270976,0.000275635,0.0002807314,0.0008064758,0.0002846004,0.0006408878,0.0002593538,0.0002991981,0.001417318],"category_scores_gemma":[0.003953706,0.0002007077,0.0002796129,0.0004521117,0.0003456015,0.0003269491,0.0005015712,0.0002949962,0.0003412672],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003052316,"about_ca_system_score_gemma":0.0002033762,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003371353,"about_ca_topic_score_gemma":0.006043442,"domain_scores_codex":[0.9994854,0.0001525016,0.00003656284,0.0001507568,0.0001056968,0.00006911818],"domain_scores_gemma":[0.9964562,0.001754721,0.0008939779,0.0003301762,0.0004645307,0.000100325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005964981,0.00003638241,0.8630921,0.0001719424,0.00008138226,0.0001100985,0.0008234083,0.003009505,0.07993853,0.0002035505,0.000366652,0.05156991],"study_design_scores_gemma":[0.00000582936,0.00007152421,0.9891205,0.00003081241,0.00003856246,0.0001706451,0.0002873467,0.006496746,0.003083237,0.00009364106,0.0005871067,0.0000140913],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995996,0.0001238701,0.002693169,0.00001920871,0.000004179154,0.00001066046,0.0002677584,0.00004542769,0.0008397346],"genre_scores_gemma":[0.9944201,0.00008400755,0.004557291,0.00001748546,0.000005723932,0.0000371033,0.0006690854,0.00001741516,0.0001918404],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003371353,"threshold_uncertainty_score":0.006721675,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05207571276641094,"score_gpt":0.302626103301859,"score_spread":0.2505503905354481,"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."}}