{"id":"W2150658671","doi":"10.3161/150811009x465811","title":"Detecting Bat Calls: An Analysis of Automated Methods","year":2009,"lang":"en","type":"article","venue":"Acta Chiropterologica","topic":"Bat Biology and Ecology Studies","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Detector; Computer science; Filter (signal processing); Range (aeronautics); Energy (signal processing); Noise (video); Real-time computing; Algorithm; Artificial intelligence; Computer vision; Telecommunications; Engineering; Mathematics; Statistics","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":[0.000572352,0.0001577653,0.0004701047,0.00003991702,0.0002679978,0.00001206369,0.0003093175,0.0002545897,0.0003578312],"category_scores_gemma":[0.0001768442,0.00005458549,0.0001699982,0.0006096977,0.0001321631,0.00008471553,0.0000643774,0.0001424602,0.000004810147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001031577,"about_ca_system_score_gemma":0.000001844522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006482893,"about_ca_topic_score_gemma":0.0005985157,"domain_scores_codex":[0.9986104,0.0003637754,0.0002887558,0.0003549062,0.00006194225,0.000320222],"domain_scores_gemma":[0.9992236,0.0003992029,0.000168306,0.00009432671,0.0000486941,0.00006586641],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000115873,0.0002850349,0.1283823,0.00000141568,0.0005876338,0.000005895812,0.0002162058,0.00003617261,0.7291039,0.00008775575,0.0001009562,0.141077],"study_design_scores_gemma":[0.00006335041,0.001238787,0.992192,0.000001280218,0.0002574032,0.000003008083,0.0001437453,0.002168848,0.003038742,0.0002167812,0.0005529623,0.0001231413],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971524,0.00009629552,0.00001152042,0.001361651,0.00006394275,0.00007971597,0.00001205906,0.0003051911,0.0009171508],"genre_scores_gemma":[0.9962939,0.00004750534,0.002319872,0.001223477,0.00004602905,0.000005341451,0.00004453977,3.659538e-7,0.00001897785],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8638097,"threshold_uncertainty_score":0.3918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03424557999516513,"score_gpt":0.3123899738450715,"score_spread":0.2781443938499064,"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."}}