{"id":"W2064752938","doi":"10.1007/s00216-009-2619-x","title":"Isotope scrambling and error magnification in multiple-spiking isotope dilution","year":2009,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Isotope Analysis in Ecology","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Isotope dilution; Analyte; Isotope; Chemistry; Scrambling; Kinetic isotope effect; Dilution; Analytical Chemistry (journal); Detection limit; Biological system; Chromatography; Algorithm; Computer science; Mass spectrometry; Deuterium; Thermodynamics; Physics; Nuclear physics","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":[0.000386128,0.0002153685,0.0003248489,0.0000541474,0.0001226623,0.00006865247,0.0001727872,0.0002142914,0.000816947],"category_scores_gemma":[0.0003927476,0.0001883636,0.00009161095,0.0004804157,0.0004654772,0.0001834701,0.0001562372,0.0003082118,0.00006269709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000122532,"about_ca_system_score_gemma":0.000009882086,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009161078,"about_ca_topic_score_gemma":0.00007380581,"domain_scores_codex":[0.9981827,0.00003665836,0.0004428601,0.000639083,0.0002563164,0.000442354],"domain_scores_gemma":[0.999294,0.0001364886,0.00007126998,0.0002508769,0.00001611946,0.0002311981],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001791407,0.0007936541,0.9091289,0.000101671,0.00008291302,0.000128342,0.0001553701,0.0004024618,0.04596508,0.002149371,0.0005145817,0.0403985],"study_design_scores_gemma":[0.001042745,0.0001220752,0.547619,0.00004107956,0.0002550907,0.00005173219,0.0002123271,0.4383523,0.004967308,0.004199127,0.002493984,0.0006432075],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9834238,0.00009441642,0.0004775289,0.003976917,0.0000123796,0.0001304397,0.0000023518,0.00003470496,0.01184747],"genre_scores_gemma":[0.9977959,0.0001219339,0.0006392088,0.0006375731,0.00005525208,0.000005567858,0.00001126378,0.000007899243,0.0007253938],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4379498,"threshold_uncertainty_score":0.8944997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01854195075694387,"score_gpt":0.2544637644546384,"score_spread":0.2359218136976945,"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."}}