{"id":"W2260189867","doi":"","title":"Predicting Organic Carbon Content of Canadian Prairie Soils Using Vis-Nir Spectroscopy: A Comparison of Pretreatment and Validation Methods","year":2014,"lang":"ko","type":"article","venue":"한국토양비료학회 학술발표회 초록집","topic":"Geochemistry and Geologic Mapping","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Soil water; Environmental science; Environmental chemistry; Carbon fibers; Soil carbon; Total organic carbon; Spectroscopy; Soil science; Chemistry; Materials science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0007899067,0.0006080979,0.000233225,0.0005816255,0.0009995588,0.0008011112,0.0007027619,0.0004265583,0.0004353161],"category_scores_gemma":[0.0008187462,0.0002690856,0.0003513661,0.0007446102,0.0003829461,0.0004253651,0.000224408,0.0004289311,0.0002029183],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003201799,"about_ca_system_score_gemma":0.006279295,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8663969,"about_ca_topic_score_gemma":0.9325289,"domain_scores_codex":[0.9996582,0.00001981265,0.0000129807,0.00009199818,0.000174213,0.00004277876],"domain_scores_gemma":[0.9995517,0.00007121624,0.00003908921,0.00002687379,0.0002854222,0.00002572879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0008891344,0.0005690885,0.3618106,0.0003841902,0.0004345113,0.0001168766,0.0006062933,0.07237967,0.3859788,0.0004930469,0.001591447,0.1747463],"study_design_scores_gemma":[0.00006009688,0.0001084794,0.7403769,0.00002608092,0.0001855007,0.00006083753,0.0004057863,0.1450716,0.1107544,0.0001772599,0.002699211,0.00007382911],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927105,0.0002354517,0.004767994,0.00004445937,0.000007207936,0.00005134019,0.0009816703,0.0001196483,0.0010817],"genre_scores_gemma":[0.9811066,0.000366048,0.01585808,0.0000412961,0.000002816443,0.00002606048,0.001413433,0.00002835877,0.001157328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1336031,"threshold_uncertainty_score":0.2687798,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06213156553497805,"score_gpt":0.3142661877912077,"score_spread":0.2521346222562297,"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."}}