{"id":"W2078093906","doi":"10.1126/science.310.5749.777d","title":"Costs and Benefits of Regulating Mercury","year":2005,"lang":"en","type":"letter","venue":"Science","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Mercury (programming language); Natural resource economics; Environmental science; Business; Computer science; Economics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.009744775,0.0007010197,0.001303999,0.0009792425,0.005573315,0.005799262,0.00273058,0.1160284,0.007932483],"category_scores_gemma":[0.04095211,0.0008624216,0.001695873,0.001178638,0.005614052,0.003880184,0.00235722,0.03787449,0.003296089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007179679,"about_ca_system_score_gemma":0.01027499,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03343218,"about_ca_topic_score_gemma":0.09059639,"domain_scores_codex":[0.9920317,0.002731885,0.0007028619,0.0007577187,0.00242905,0.001346799],"domain_scores_gemma":[0.9722482,0.02113976,0.001092703,0.0005749498,0.002931208,0.002013081],"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":[0.0004533242,0.0001147993,0.002250965,0.000125569,0.00009091265,0.00148843,0.000446483,0.0002435432,0.0005765754,0.03260704,0.9425532,0.01904908],"study_design_scores_gemma":[0.0003989227,0.0002365526,0.006914885,0.0005330768,0.0002499992,0.001088681,0.001914266,0.0007551158,0.0009314819,0.04782761,0.939013,0.0001364014],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.002158747,0.00165016,0.00014515,0.9763691,0.005829562,0.00002112223,0.0001388139,0.00002091416,0.01366634],"genre_scores_gemma":[0.0113366,0.0007360958,0.0002416641,0.9667982,0.008159976,0.0000464258,0.00004112345,0.00001284547,0.01262707],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.1160284,"threshold_uncertainty_score":0.06647515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02302099852281259,"score_gpt":0.262536817614976,"score_spread":0.2395158190921635,"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."}}