{"id":"W2981382342","doi":"10.1371/journal.pone.0224328","title":"Application of national pollutant inventories for monitoring trends on dioxin emissions from stationary industrial sources in Australia, Canada and European Union","year":2019,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Toxic Organic Pollutants Impact","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Research Council; Australian Government","keywords":"Emission inventory; Environmental science; European union; Greenhouse gas; Incineration; Environmental protection; Industrial production; Pig iron; Pollutant; Electricity; Waste management; Business; Engineering; International trade; Chemistry","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":[],"consensus_categories":[],"category_scores_codex":[0.001159045,0.000525518,0.0003483901,0.006277194,0.0007482258,0.001041578,0.0006272362,0.0001746534,0.0006996768],"category_scores_gemma":[0.001664304,0.0002334052,0.0005104702,0.008052651,0.0001349338,0.0003655883,0.0007551105,0.0002868202,0.0001803895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00821827,"about_ca_system_score_gemma":0.01510347,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9320357,"about_ca_topic_score_gemma":0.9393834,"domain_scores_codex":[0.9987148,0.00006200249,0.00008719139,0.0001923614,0.0007861471,0.0001575488],"domain_scores_gemma":[0.9974215,0.00007328394,0.0002058072,0.00004653895,0.002160007,0.00009286231],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002005507,0.0001482754,0.8823398,0.0006309668,0.0005121169,0.000317502,0.001447233,0.008914302,0.003897524,0.001486458,0.007295846,0.09280956],"study_design_scores_gemma":[0.00000589955,0.00002716393,0.9729086,0.00007505431,0.00009178559,0.00007826259,0.0009964658,0.007554986,0.003244159,0.0001184081,0.01487146,0.00002761936],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8806118,0.001983258,0.009293664,0.00015668,0.00003880059,0.0005755643,0.07443345,0.0003026034,0.03260415],"genre_scores_gemma":[0.9010997,0.002387129,0.01814565,0.0001114684,0.00001106074,0.000355318,0.06543434,0.00006393714,0.01239151],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06796426,"threshold_uncertainty_score":0.1367289,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05061806937399642,"score_gpt":0.2481811977004929,"score_spread":0.1975631283264964,"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."}}