{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002452792,0.0007645466,0.0003296413,0.0006652381,0.001392758,0.001189395,0.0005943986,0.001257937,0.6984791],"category_scores_gemma":[0.0003977512,0.0003377155,0.000432649,0.0005037054,0.0003177099,0.0008987632,0.0007825284,0.0009413331,0.3296225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008804405,"about_ca_system_score_gemma":0.0009139116,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01084497,"about_ca_topic_score_gemma":0.0384255,"domain_scores_codex":[0.9997703,0.000009639755,0.000004180015,0.00001475922,0.0001300954,0.00007095512],"domain_scores_gemma":[0.999799,0.00001964797,0.00001457525,0.00001811392,0.00008561924,0.00006313883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007756035,0.0001010123,0.0005718437,0.0002332317,0.000006239484,0.0001633637,0.00006988841,0.00004003094,0.007764624,0.0009976403,0.9360064,0.05396818],"study_design_scores_gemma":[0.00001277176,0.00005261919,0.00206551,0.00004955987,0.000005579417,0.00005168537,0.0001368705,0.00003164565,0.004069423,0.0001296101,0.9933861,0.000008617572],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.008394354,0.001627907,0.0007702878,0.006014248,0.004380931,0.00025452,0.004760505,0.001446006,0.9723512],"genre_scores_gemma":[0.008353592,0.00176883,0.0005271229,0.0009465028,0.0002625218,0.00004325313,0.001676554,0.0001941694,0.9862275],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.9891551,"threshold_uncertainty_score":0.4300829,"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."}}