{"id":"W2069761724","doi":"10.1021/es072543f","title":"Clearing the air on ethanol | A nano Trojan horse | Perfume, perfume everywhere | News Briefs: Montreal beats Kyoto on climate controls ` Bigger fish to fry? ` Asian pollution strengthens storms ` Snapping fluorocarbon superbonds ` New aerosol source ` Snapping fluorocarbon superbonds | Perchlorate from fireworks | Seeing the forest for the methane | Thailand fuels up with cassava","year":2007,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clearing; Environmental science; Fish <Actinopterygii>; Storm; Pollution; Environmental engineering; Waste management; Meteorology; Environmental protection; Engineering; Business; Ecology; Fishery; Geography; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.000987979,0.0006318737,0.0004698934,0.0004057705,0.001478426,0.0002592181,0.001079057,0.0004126246,0.00004461689],"category_scores_gemma":[0.0001352767,0.0003861746,0.0001425747,0.001161858,0.0007474514,0.0005586216,0.0001984576,0.001056249,0.00002462095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000647412,"about_ca_system_score_gemma":0.00008689168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009407988,"about_ca_topic_score_gemma":0.005587485,"domain_scores_codex":[0.9962597,0.00006962036,0.0005931993,0.0009495548,0.0008066936,0.00132119],"domain_scores_gemma":[0.9981955,0.0004235371,0.0001768879,0.0009121962,0.00002820195,0.00026362],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001006125,0.0001645974,0.004864322,0.00002740829,0.000227906,0.00002114096,0.0141274,0.1297635,0.7302521,0.0002481258,0.0003999553,0.1188974],"study_design_scores_gemma":[0.009204779,0.004995375,0.06716989,0.0011085,0.0007415343,0.0003010924,0.1245604,0.1711476,0.5581252,0.0004354861,0.05802589,0.004184245],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834484,0.0005209154,0.006892941,0.005725364,0.0003851074,0.00163373,0.00005351542,0.00044712,0.0008929412],"genre_scores_gemma":[0.9972968,0.0002734262,0.0002493208,0.001137942,0.0002351269,0.0002336615,0.00002988249,0.00009780768,0.0004460387],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1721269,"threshold_uncertainty_score":0.999859,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009120173069952007,"score_gpt":0.2194666535865848,"score_spread":0.2103464805166328,"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."}}