{"id":"W4382045300","doi":"10.3386/w31390","title":"International Trade, Noise Pollution, and Killer Whales","year":2023,"lang":"en","type":"report","venue":"National Bureau of Economic Research","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Noise pollution; Pollution; Noise (video); Fishery; Environmental science; Geography; Computer science; Biology; Ecology; Noise reduction; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004727999,0.0002390816,0.0001102328,0.0008494213,0.0002026246,0.000943173,0.0001460506,0.00028569,0.005324278],"category_scores_gemma":[0.0007035932,0.00007704759,0.0001710162,0.001741112,0.000412224,0.000489676,0.0003529717,0.0002586996,0.0006568375],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003700123,"about_ca_system_score_gemma":0.0004197269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02537021,"about_ca_topic_score_gemma":0.0376465,"domain_scores_codex":[0.9998736,0.00003549363,0.000009656099,0.00002166238,0.00003955423,0.00001995594],"domain_scores_gemma":[0.9993665,0.0002258919,0.0002778939,0.00003054256,0.00006093741,0.00003819739],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006218384,0.001174844,0.7027378,0.0009430594,0.0002628914,0.000735899,0.0007687244,0.0024853,0.001959361,0.01845675,0.04287324,0.2269804],"study_design_scores_gemma":[0.00003607677,0.0003626107,0.8945948,0.0004035145,0.0001437814,0.0003995847,0.002348529,0.0007290569,0.0007305722,0.005904002,0.09432074,0.00002661951],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6548853,0.09411174,0.001901059,0.008094376,0.000365081,0.0001172879,0.006937148,0.00009738952,0.2334907],"genre_scores_gemma":[0.8333129,0.1060894,0.001063715,0.001182473,0.00036772,0.00008451167,0.007180276,0.00003210955,0.05068684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02537021,"threshold_uncertainty_score":0.05044508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3577957632227141,"score_gpt":0.4892970844323888,"score_spread":0.1315013212096747,"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."}}