{"id":"W6921973077","doi":"10.1021/es404851b.s001","title":"Transformation\\nof Mercury at the Bottom of the Arctic\\nFood Web: An Overlooked Puzzle in the Mercury Exposure Narrative","year":2016,"lang":"en","type":"article","venue":"Figshare","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); Zooplankton; Arctic; Pelagic zone; Methylmercury; Seawater; Bioaccumulation; Predation; Benthic zone","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":[0.0007601677,0.000370689,0.0003175799,0.0009262798,0.002502122,0.002977241,0.0004745929,0.0009806182,0.00229981],"category_scores_gemma":[0.001162097,0.0002191186,0.0002276099,0.0009946503,0.004200417,0.002540879,0.001773179,0.001502831,0.0005658469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002994294,"about_ca_system_score_gemma":0.002995924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1960457,"about_ca_topic_score_gemma":0.339178,"domain_scores_codex":[0.9996562,0.00004873682,0.00001286169,0.00008807642,0.0001074712,0.00008662092],"domain_scores_gemma":[0.999284,0.0001246885,0.00008555711,0.00007498835,0.0002694667,0.000161394],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.000460131,0.000082906,0.2849862,0.001316168,0.0004669442,0.001788581,0.02707268,0.001107159,0.02388524,0.06480585,0.06927405,0.524754],"study_design_scores_gemma":[0.000005427588,0.0001240106,0.3405129,0.001355772,0.00015611,0.0008942045,0.02900383,0.0005605099,0.004390399,0.02605401,0.5968233,0.0001196136],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5243673,0.2683643,0.004026314,0.1100988,0.002856719,0.00001103693,0.001767819,0.0001865433,0.08832116],"genre_scores_gemma":[0.8351678,0.1313487,0.001812058,0.01202266,0.001974385,0.000008069229,0.0005452411,0.00008710513,0.01703411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1960457,"threshold_uncertainty_score":0.3898092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02790981015806638,"score_gpt":0.2569257332058526,"score_spread":0.2290159230477862,"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."}}