{"id":"W1569676305","doi":"","title":"Detection and characterization of marine mammal calls by parametric modelling","year":2004,"lang":"en","type":"article","venue":"Canadian acoustics","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Engineering and Physical Sciences Research Council","keywords":"Spectrogram; Computer science; Marine mammal; Parametric model; Noise (video); Constant false alarm rate; Parametric statistics; Waveform; False alarm; Bioacoustics; Time domain; SIGNAL (programming language); Filter (signal processing); Algorithm; Pattern recognition (psychology); Artificial intelligence; Computer vision; Mathematics; Telecommunications; Statistics; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000511597,0.0004876056,0.0003940514,0.0008040721,0.0002020556,0.0007029022,0.0007082387,0.0005872683,0.0009005441],"category_scores_gemma":[0.003017896,0.0002985622,0.0006973353,0.0003179583,0.0003908134,0.0008357988,0.0005195834,0.0005067646,0.0004292181],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002857047,"about_ca_system_score_gemma":0.0004300452,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002015032,"about_ca_topic_score_gemma":0.002053936,"domain_scores_codex":[0.9994982,0.00008151366,0.00002088584,0.0001450287,0.0002221857,0.00003211235],"domain_scores_gemma":[0.9990221,0.0005442391,0.0001506837,0.000153117,0.0001047206,0.00002520172],"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":[0.0001698554,0.0001059412,0.008369721,0.0002151929,0.0001082817,0.0001987383,0.000300795,0.3543342,0.1473873,0.008155721,0.0005492534,0.480105],"study_design_scores_gemma":[0.000004634055,0.00007607109,0.004991612,0.00001589197,0.00002139556,0.0003107247,0.00003538582,0.9754477,0.01420462,0.00233835,0.002510921,0.00004272059],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01932595,0.0001106626,0.9796513,0.00002664562,0.000007699286,0.0000143902,0.00003345291,0.0002888031,0.0005411512],"genre_scores_gemma":[0.566448,0.0004971899,0.4290878,0.0000520276,0.00006595817,0.0001276831,0.0002981072,0.0001700218,0.003253209],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9979849,"threshold_uncertainty_score":0.004006624,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01019305920165938,"score_gpt":0.1830958142674062,"score_spread":0.1729027550657468,"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."}}