{"id":"W2041583427","doi":"10.1144/1467-7873/07-145","title":"Thompson–Howarth error analysis: unbiased alternatives to the large-sample method for assessing non-normally distributed measurement error in geochemical samples","year":2008,"lang":"en","type":"article","venue":"Geochemistry Exploration Environment Analysis","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"","keywords":"Statistics; Sample (material); Error analysis; Observational error; Mathematics; Computer science; Chemistry; Chromatography; Applied mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001959573,0.0004552341,0.0007356362,0.0003110728,0.0005762929,0.0001980227,0.001203334,0.0001649188,0.0001453565],"category_scores_gemma":[0.0007364777,0.0003935011,0.000636275,0.002628784,0.00008286699,0.0006052101,0.000396542,0.0002658777,0.00001223006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002820729,"about_ca_system_score_gemma":0.00009505033,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009257381,"about_ca_topic_score_gemma":0.0001547545,"domain_scores_codex":[0.9959269,0.0002214789,0.0008503341,0.00123304,0.001017883,0.0007503671],"domain_scores_gemma":[0.997415,0.000397473,0.0004545691,0.001289534,0.0002090718,0.0002343448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006518005,0.0006925719,0.03104726,0.00006880085,0.003222,0.00002379049,0.003755762,0.8964506,0.06263747,0.0001067723,0.0008312641,0.001098573],"study_design_scores_gemma":[0.0008247811,0.00003490744,0.020237,0.00002415481,0.001274016,0.000003371813,0.001377441,0.8415596,0.1247248,0.0006312581,0.008619384,0.0006893043],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04042095,0.00006537039,0.9513994,0.007204966,0.00003137585,0.0005203711,0.0001798579,0.00007391987,0.0001038139],"genre_scores_gemma":[0.8860052,0.00002513473,0.111168,0.0002330478,0.0000975869,0.0007141054,0.001603963,0.000007937328,0.0001450018],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8455842,"threshold_uncertainty_score":0.9998517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07006042415710388,"score_gpt":0.2906992079937167,"score_spread":0.2206387838366128,"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."}}