{"id":"W4404824007","doi":"10.3390/microorganisms12122449","title":"Mercury and Arctic Char Gill Microbiota Correlation in Canadian Arctic Communities","year":2024,"lang":"en","type":"article","venue":"Microorganisms","topic":"Mercury impact and mitigation studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique; Université Laval","funders":"Canada First Research Excellence Fund; Sentinelle Nord, Université Laval; Polar Knowledge Canada","keywords":"Mercury (programming language); Proteobacteria; Biology; Arctic char; Ecology; Arctic; Bacteroidetes; Microbial population biology; Environmental chemistry; Chemistry; Bacteria; Fishery; 16S ribosomal RNA","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":[0.0003038838,0.0003675412,0.0004030256,0.001234788,0.002133174,0.0009695797,0.0002092646,0.0002395331,0.00119384],"category_scores_gemma":[0.0004976215,0.0002152431,0.0003022277,0.001312254,0.0004985406,0.0001783578,0.0006312398,0.0003061634,0.0001541616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003796963,"about_ca_system_score_gemma":0.005412187,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8290857,"about_ca_topic_score_gemma":0.9418157,"domain_scores_codex":[0.9995937,0.00002881763,0.00001424457,0.0001088661,0.000124792,0.0001296347],"domain_scores_gemma":[0.9994634,0.00002428142,0.00009538618,0.00002034101,0.000292826,0.0001036937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004285338,0.00002885026,0.960505,0.00007986494,0.0001356396,0.0001738445,0.001544078,0.0002551628,0.02889048,0.0001222737,0.0003476796,0.007488728],"study_design_scores_gemma":[0.000001474428,0.00001955773,0.997343,0.0000116447,0.00003159314,0.00006721717,0.001118915,0.0001138292,0.0006979897,0.0000184401,0.0005687874,0.00000760922],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9980332,0.0003290312,0.0001413648,0.00004846762,0.000006031291,0.000004332534,0.0005948591,0.000008153826,0.0008345231],"genre_scores_gemma":[0.9979381,0.000227477,0.0003132385,0.00003523802,0.000002837884,0.000004838281,0.0005788453,0.000007320812,0.0008919433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1709143,"threshold_uncertainty_score":0.3438417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01064990498685143,"score_gpt":0.2226237698521153,"score_spread":0.2119738648652639,"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."}}