{"id":"W2273630580","doi":"","title":"Mechanisms for Tracking Canadian Mercury Imports and Exports for Use and Disposal","year":2003,"lang":"en","type":"article","venue":"","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Environmental science; Business; Natural resource economics; Computer science; Economics","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":[0.01079999,0.0007255238,0.0004242168,0.006884074,0.007153991,0.005318435,0.002364814,0.001591593,0.008520314],"category_scores_gemma":[0.01441534,0.0008195231,0.0008064535,0.006780161,0.001493688,0.002522451,0.00194714,0.001275072,0.001756593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05034892,"about_ca_system_score_gemma":0.110718,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9808703,"about_ca_topic_score_gemma":0.9857372,"domain_scores_codex":[0.9919264,0.001173769,0.000478046,0.0007832614,0.004237549,0.00140091],"domain_scores_gemma":[0.9712455,0.001570234,0.002350416,0.001977936,0.02218057,0.0006753461],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005999882,0.0002773879,0.3717786,0.001228505,0.0003490434,0.0002272167,0.005280573,0.01096518,0.0188053,0.1009691,0.2013524,0.2881666],"study_design_scores_gemma":[0.0001463849,0.0002146017,0.4617221,0.0006328101,0.0004733183,0.0001979186,0.008218911,0.01351007,0.03986009,0.006674433,0.4678693,0.0004799748],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.284915,0.004114105,0.08513774,0.02336072,0.0007666376,0.006502067,0.06110193,0.005655649,0.528446],"genre_scores_gemma":[0.7208797,0.002932397,0.1271161,0.002311549,0.0001034873,0.002461102,0.0158592,0.0003238637,0.1280126],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05034892,"threshold_uncertainty_score":0.3653086,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03002237201983196,"score_gpt":0.2539536949115118,"score_spread":0.2239313228916798,"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."}}