{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002749963,0.0004193238,0.0002447228,0.0005150567,0.0002466379,0.0007266454,0.0004741941,0.0004233897,0.004426732],"category_scores_gemma":[0.001874914,0.0002538549,0.0004337739,0.0004419698,0.0005906947,0.0006541524,0.0008756487,0.0004800995,0.0007330026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001766761,"about_ca_system_score_gemma":0.0002025211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004029014,"about_ca_topic_score_gemma":0.0006920815,"domain_scores_codex":[0.9997517,0.00004415359,0.00001738809,0.00004967471,0.0001122352,0.00002479139],"domain_scores_gemma":[0.9993886,0.0002930111,0.00004260249,0.0001269924,0.00009929795,0.00004953123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001427484,0.000385863,0.002235455,0.0007650328,0.0001296423,0.001262188,0.001217289,0.2606767,0.4292171,0.05498404,0.007583887,0.2401153],"study_design_scores_gemma":[0.0003072063,0.001304163,0.005367626,0.00009974041,0.0001172565,0.001642251,0.0006125664,0.7054535,0.1777203,0.03183017,0.07536065,0.0001846275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1697308,0.0002399752,0.8141117,0.0001553847,0.0003212359,0.0002667979,0.0005158924,0.001601395,0.01305669],"genre_scores_gemma":[0.6521416,0.0002434195,0.3382767,0.000130643,0.00009472942,0.0004203594,0.00105112,0.0003863926,0.007254977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004426732,"threshold_uncertainty_score":0.01480883,"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."}}