{"id":"W2086425825","doi":"10.1021/es053180r","title":"Canada bans fluoropolymer stain repellents | Funding woes eroding steam gage network | Cleaning up school bus emissions | Healthy student housing | Mercury in environmental journalists | Honda named greenest brand in 2004 | Green facts and figures | Mine tailings soak up greenhouse gas | Pollutants persist in drinking water","year":2005,"lang":"en","type":"article","venue":"Environmental Science & Technology","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Waste management; Environmental science; Engineering; Business; Fluoropolymer; Chemistry; Computer science","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":[0.0003958768,0.0006751238,0.0003450599,0.0009936828,0.002851749,0.001307511,0.001058297,0.001470608,0.3924048],"category_scores_gemma":[0.0008723696,0.0003997849,0.0004207649,0.0008972372,0.0005276818,0.0005189765,0.0004629287,0.00104133,0.07571171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006807454,"about_ca_system_score_gemma":0.01844552,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.771957,"about_ca_topic_score_gemma":0.9072744,"domain_scores_codex":[0.9989175,0.00002090875,0.00001243141,0.00006246893,0.0007658789,0.0002209212],"domain_scores_gemma":[0.9985971,0.00006287444,0.00005178375,0.00004323251,0.001029329,0.0002156513],"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":[0.0001012221,0.000179521,0.001339477,0.0002040376,0.000008487801,0.0001231732,0.00008868395,0.00007203844,0.006356482,0.002476284,0.9442809,0.04476977],"study_design_scores_gemma":[0.00001486895,0.00003865849,0.004289542,0.00004881241,0.000007297321,0.00003535098,0.00008137059,0.00006657101,0.002484714,0.0001017743,0.9928187,0.00001234711],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.009297502,0.001709703,0.0009070168,0.007423885,0.001177119,0.0004356976,0.01371394,0.0007764395,0.9645587],"genre_scores_gemma":[0.01115546,0.001235176,0.0006477354,0.0010494,0.00005122604,0.00004759593,0.002856981,0.0001058577,0.9828504],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3924048,"threshold_uncertainty_score":0.8666608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0101971289738027,"score_gpt":0.24354667398958,"score_spread":0.2333495450157773,"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."}}