{"id":"W4389582740","doi":"10.7185/gold2023.20355","title":"Canada’s Global mercury passive sampling network study","year":2023,"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":"Environment and Climate Change Canada","funders":"","keywords":"Mercury (programming language); Computer science; Sampling (signal processing); Environmental science; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001450675,0.0004315691,0.0004247625,0.001102787,0.003406258,0.001361329,0.001100849,0.0006690176,0.003086002],"category_scores_gemma":[0.002314338,0.0003811951,0.0006054274,0.004265499,0.0006438031,0.0005517001,0.001270295,0.001022301,0.000386339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04065295,"about_ca_system_score_gemma":0.1151733,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9979177,"about_ca_topic_score_gemma":0.9991911,"domain_scores_codex":[0.9985197,0.0001923803,0.00004195367,0.000161664,0.0006174376,0.0004669216],"domain_scores_gemma":[0.9972078,0.0001607929,0.0001768057,0.0002009572,0.001697594,0.0005561341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001077218,0.0003411103,0.6466606,0.0003061993,0.0006379472,0.0007460638,0.002370579,0.00177032,0.001053777,0.008489725,0.2658322,0.07071427],"study_design_scores_gemma":[0.0001439125,0.00009316739,0.8694781,0.0001598451,0.000300876,0.0001755786,0.003836075,0.0009575369,0.0008750535,0.0005099471,0.1234174,0.00005258333],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7515538,0.003771714,0.001352511,0.02329851,0.0004140013,0.0006105225,0.1283017,0.0001372827,0.09055987],"genre_scores_gemma":[0.9059632,0.002510152,0.001886273,0.004598559,0.00007776964,0.0003089086,0.02532234,0.00008948347,0.05924334],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04065295,"threshold_uncertainty_score":0.2949592,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03103417015261745,"score_gpt":0.2893118867927392,"score_spread":0.2582777166401217,"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."}}