{"id":"W975089079","doi":"10.1016/j.marpolbul.2015.06.043","title":"Mercury concentrations in feathers of marine birds in Arctic Canada","year":2015,"lang":"en","type":"article","venue":"Marine Pollution Bulletin","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Acadia University; Carleton University; Environment and Climate Change Canada","funders":"Natural Resources Canada; Aboriginal Affairs and Northern Development Canada; National Research Council Canada; Canada Research Chairs; Environment Canada; Natural Sciences and Engineering Research Council of Canada","keywords":"Feather; Mercury (programming language); Arctic; The arctic; Tern; Biology; Larus; Zoology; Ecology; Fishery; Oceanography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001050926,0.0002265162,0.0001950974,0.0008388882,0.001957153,0.0006811,0.000286981,0.0002940289,0.0008798661],"category_scores_gemma":[0.0002475069,0.0001995818,0.000135608,0.0006469949,0.0004250011,0.0001522721,0.0002362133,0.0002033374,0.0001838605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004037604,"about_ca_system_score_gemma":0.002348956,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9319559,"about_ca_topic_score_gemma":0.9741328,"domain_scores_codex":[0.9998788,0.000006896188,0.000004582676,0.00002428009,0.00004357565,0.0000419266],"domain_scores_gemma":[0.9997427,0.00001513023,0.00002739584,0.000004963943,0.0001349523,0.00007488704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0005088836,0.00004866425,0.9695411,0.00004092605,0.00009655307,0.000205217,0.002209777,0.0002197893,0.01883314,0.00005850524,0.0004431902,0.0077943],"study_design_scores_gemma":[8.818835e-7,0.00001700338,0.9984068,0.000002419302,0.000007884556,0.00002552076,0.0006315578,0.00004378047,0.0005500642,0.000004376405,0.0003080804,0.000001754778],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9988813,0.00009334582,0.00002306867,0.00001321064,0.000001709,0.000001924432,0.0002644093,0.000002488004,0.0007186325],"genre_scores_gemma":[0.9967642,0.000151286,0.0001064516,0.00001785873,0.000001527051,0.000002722327,0.0002870013,0.000003074999,0.002665893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06804407,"threshold_uncertainty_score":0.1368895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01537406030498843,"score_gpt":0.2293843734307452,"score_spread":0.2140103131257568,"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."}}