{"id":"W1967604675","doi":"10.1162/pres.16.1.84","title":"Synthetic Soundscapes with Natural Grains","year":2007,"lang":"en","type":"article","venue":"PRESENCE Virtual and Augmented Reality","topic":"Speech and Audio Processing","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Soundscape; Wavelet; STREAMS; Segmentation; SIGNAL (programming language); Natural sounds; Sampling (signal processing); Speech recognition; Audio signal; Natural (archaeology); Acoustics; Artificial intelligence; Computer vision; Sound (geography); Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005243134,0.0001496129,0.0001410406,0.00007110728,0.0002579889,0.0002007742,0.0004034243,0.00004053361,0.000004888372],"category_scores_gemma":[0.0000760554,0.0001020692,0.00002840167,0.0003392162,0.0001802583,0.0005585198,0.0001985094,0.0001729099,0.000005681414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001855808,"about_ca_system_score_gemma":0.00003742351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006106748,"about_ca_topic_score_gemma":0.00007926668,"domain_scores_codex":[0.9986276,0.0000404051,0.0001769313,0.0004368359,0.0003281598,0.0003900832],"domain_scores_gemma":[0.9992421,0.0001534633,0.00007926978,0.0003062425,0.00006501193,0.0001539133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002292341,0.0003499307,0.01028643,0.0001026266,0.0001009042,0.0002070374,0.00294591,0.0000663393,0.05089088,0.0201447,0.0008824238,0.9137936],"study_design_scores_gemma":[0.004040465,0.001673203,0.1269591,0.0008898402,0.0000863002,0.000951662,0.003363511,0.03554188,0.7843713,0.02637669,0.01314933,0.002596664],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5382346,0.0004678266,0.456453,0.001596516,0.0002059914,0.0001511598,0.000003387185,0.0002522669,0.002635291],"genre_scores_gemma":[0.9948348,0.00002696773,0.004508322,0.0002403447,0.00004972861,0.000003911331,0.000002216658,0.000005270932,0.0003284407],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9111969,"threshold_uncertainty_score":0.4162259,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01157348907044301,"score_gpt":0.2487916693984134,"score_spread":0.2372181803279704,"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."}}