{"id":"W2008046515","doi":"10.1039/b312118f","title":"Recent temporal trend monitoring of mercury in Arctic biota ? how powerful are the existing data sets?","year":2004,"lang":"en","type":"article","venue":"Journal of Environmental Monitoring","topic":"Atmospheric and Environmental Gas Dynamics","field":"Environmental Science","cited_by":60,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Carleton University","funders":"","keywords":"Biota; Statistical power; Statistics; Mercury (programming language); Arctic; Environmental science; Series (stratigraphy); Statistical analysis; The arctic; Computer science; Mathematics; Ecology; Geology; Biology; Oceanography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006650029,0.000304838,0.0003857665,0.0000338929,0.000139496,0.00004274688,0.001024325,0.0001079187,0.00009767773],"category_scores_gemma":[0.00006241948,0.000238953,0.0001146834,0.000248581,0.0004025312,0.0007817054,0.0008148904,0.0005807782,0.00001385392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001431149,"about_ca_system_score_gemma":0.00001291083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000139426,"about_ca_topic_score_gemma":0.00001528703,"domain_scores_codex":[0.9975088,0.00008939751,0.0007203151,0.0003719788,0.0008677371,0.0004417704],"domain_scores_gemma":[0.9982009,0.00008019373,0.0009077886,0.0006383217,0.000002550556,0.0001702656],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004727942,0.0002893821,0.9370875,0.00001255907,0.00003476972,0.00009445742,0.0005242415,0.03272948,0.01124397,8.634522e-7,0.000007637785,0.01792782],"study_design_scores_gemma":[0.0009802749,0.0001876296,0.9841997,0.0002943191,0.00006415127,0.0001526754,0.009331237,0.0001991395,0.002692508,0.0001006109,0.001509605,0.0002881352],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968327,0.001392611,0.000161687,0.0005381804,0.000801077,0.0001479648,0.00001292874,0.000007086518,0.0001057352],"genre_scores_gemma":[0.9839958,0.003183762,0.01243968,0.00001444539,0.0002608473,0.000002087764,0.000003362103,0.00004073277,0.00005926303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04711218,"threshold_uncertainty_score":0.974422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0355324290738432,"score_gpt":0.2624887105539954,"score_spread":0.2269562814801522,"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."}}