{"id":"W4412310810","doi":"","title":"ARCTOX: a pan-Arctic sampling network to track mercury contamination across Arctic marine food webs","year":2017,"lang":"en","type":"article","venue":"","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University; McGill University","funders":"","keywords":"Arctic; Mercury (programming language); Environmental science; Mercury contamination; Contamination; The arctic; Oceanography; Sampling (signal processing); Geography; Ecology; Geology; Biology; Computer science","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":[0.003093728,0.0008798355,0.0005170802,0.003587766,0.0007171251,0.0008447796,0.0008721976,0.0004997207,0.003776463],"category_scores_gemma":[0.003325006,0.0004219683,0.00056632,0.002666385,0.0001711591,0.0006243102,0.002107167,0.0005896422,0.002166363],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000783581,"about_ca_system_score_gemma":0.00304771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05234838,"about_ca_topic_score_gemma":0.07287601,"domain_scores_codex":[0.9983752,0.0004337033,0.000162557,0.0004580035,0.000430664,0.0001398276],"domain_scores_gemma":[0.9960688,0.0003457391,0.0008675202,0.0003740795,0.001797064,0.0005467933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001881654,0.0003977568,0.6537387,0.001238426,0.001090894,0.000567509,0.002329528,0.004079442,0.0244764,0.001961872,0.1441145,0.1641234],"study_design_scores_gemma":[0.0001872658,0.0003242858,0.7095823,0.0003681575,0.0003345149,0.000371186,0.001038063,0.0150525,0.006216187,0.001151017,0.2652539,0.0001205195],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3529121,0.002956654,0.07950165,0.0009931867,0.0007373343,0.003466123,0.5132985,0.008584212,0.03755023],"genre_scores_gemma":[0.2376108,0.00170599,0.1962081,0.0008554701,0.0002909808,0.006224689,0.535446,0.001329866,0.0203282],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05234838,"threshold_uncertainty_score":0.1040874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04297573285763947,"score_gpt":0.3190704417069402,"score_spread":0.2760947088493007,"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."}}