{"id":"W4250254211","doi":"10.1139/f00-040","title":"Sampling for mercury at subnanogram per litre concentrations for load estimation in rivers","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Fisheries and Aquatic Sciences","topic":"Water Quality and Resources Studies","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Mercury (programming language); STREAMS; Sampling (signal processing); Standard deviation; Environmental science; Transect; Contamination; Replicate; Blank; Relative standard deviation; Chemistry; Environmental chemistry; MERCURE; Mean squared error; Analytical Chemistry (journal); Statistics; Mathematics; Detection limit; Ecology; Chromatography; Biology; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0009038084,0.0002902873,0.0005624351,0.001050497,0.0008918096,0.0007722637,0.0005616227,0.000525152,0.0006801248],"category_scores_gemma":[0.001410434,0.0003873304,0.0002446308,0.00100824,0.0004841554,0.000481406,0.0004719347,0.0004004675,0.0003783222],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006203316,"about_ca_system_score_gemma":0.0006806987,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01595935,"about_ca_topic_score_gemma":0.04667512,"domain_scores_codex":[0.9988017,0.0002889817,0.00008972704,0.0003061125,0.0004513354,0.00006205716],"domain_scores_gemma":[0.9995198,0.0001223386,0.0000840306,0.00007015953,0.0001763835,0.00002729927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003959742,0.0001731413,0.1979266,0.0001957255,0.0000789606,0.000136562,0.0006033954,0.001729894,0.7318099,0.0002783644,0.0003134583,0.0663581],"study_design_scores_gemma":[0.00003532414,0.0007744234,0.5846143,0.00004258723,0.0001143482,0.0005611298,0.0004553959,0.01519098,0.3921342,0.0009407333,0.005081108,0.00005547551],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9623825,0.0005382757,0.03426139,0.00007723024,0.000009536238,0.00009599843,0.0006083142,0.0002321828,0.001794532],"genre_scores_gemma":[0.9124902,0.0007476293,0.08237774,0.0001095303,0.00001761206,0.0002017571,0.001282366,0.0001077216,0.002665451],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01595935,"threshold_uncertainty_score":0.03173292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05057407498731965,"score_gpt":0.2515391451061102,"score_spread":0.2009650701187906,"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."}}