{"id":"W4394796530","doi":"10.1101/2024.04.10.588860","title":"Worldwide Soundscapes: a synthesis of passive acoustic monitoring across realms","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Animal Vocal Communication and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada; University of Northern British Columbia; University of Victoria","funders":"Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research; Universidade Federal de Mato Grosso do Sul; Curtin University of Technology","keywords":"Diel vertical migration; Biodiversity; Sampling (signal processing); Soundscape; Habitat; Temporal scales; Environmental science; Macroecology; Geography; Ecology; Environmental monitoring; Environmental resource management; Oceanography; Sound (geography); Geology; Computer science; Biology","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":[0.003796091,0.0009246556,0.0006267029,0.009560365,0.0002945575,0.00193664,0.0006518215,0.0005375714,0.003025878],"category_scores_gemma":[0.008734548,0.0003086142,0.0006704959,0.01015688,0.0004047667,0.002219982,0.002832825,0.0004416756,0.0009119601],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004247504,"about_ca_system_score_gemma":0.0005725196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004463809,"about_ca_topic_score_gemma":0.007527722,"domain_scores_codex":[0.9976878,0.0004916882,0.0004265389,0.0006406668,0.0005973192,0.0001559985],"domain_scores_gemma":[0.9915804,0.003276446,0.001381247,0.001437691,0.001966336,0.0003578609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0004587288,0.0001055726,0.342117,0.004442308,0.001099959,0.0003069587,0.001861654,0.008149468,0.01460046,0.00342267,0.03924317,0.584192],"study_design_scores_gemma":[0.00001956874,0.0001360811,0.8118895,0.001278721,0.0004280513,0.0005044279,0.002210689,0.005167576,0.003596313,0.002477988,0.1721621,0.0001289848],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"review","genre_scores_codex":[0.4874687,0.01822205,0.05979133,0.001818909,0.0005394184,0.0004959396,0.3975515,0.002612561,0.0314995],"genre_scores_gemma":[0.5390075,0.007515597,0.05772626,0.0004928033,0.0004525013,0.0007181106,0.3895614,0.0006518616,0.003874074],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.009560365,"threshold_uncertainty_score":0.02007586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01738863501589908,"score_gpt":0.2724099156528756,"score_spread":0.2550212806369765,"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."}}