{"id":"W3106134087","doi":"10.1002/saj2.20194","title":"Fine grinding is needed to maintain the high accuracy of mid‐infrared diffuse reflectance spectroscopy for soil property estimation","year":2020,"lang":"en","type":"article","venue":"Soil Science Society of America Journal","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Grinding; Diffuse reflectance infrared fourier transform; Calibration; Partial least squares regression; Spectral line; Goodness of fit; Spectroscopy; Soil test; Environmental science; Mathematics; Biological system; Computer science; Materials science; Remote sensing; Soil science; Statistics; Soil water; Chemistry; Geology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002300893,0.0006708631,0.0005771784,0.0005565064,0.0003927761,0.0009427466,0.0005429563,0.0005459088,0.0008086144],"category_scores_gemma":[0.003447136,0.0002824172,0.0006666334,0.0006092165,0.0004106641,0.001248324,0.0003965623,0.000617889,0.0003759463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004687937,"about_ca_system_score_gemma":0.0004373201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007253604,"about_ca_topic_score_gemma":0.01659576,"domain_scores_codex":[0.9992668,0.0001522466,0.00005182229,0.0001970274,0.0002869046,0.00004528546],"domain_scores_gemma":[0.998453,0.0006421419,0.0002029506,0.0003132934,0.0003650165,0.00002351113],"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":[0.000347868,0.0002966519,0.09720143,0.0003362811,0.0002960493,0.000199244,0.0001939056,0.3174236,0.3678631,0.0009589549,0.001466926,0.213416],"study_design_scores_gemma":[0.00001657125,0.0002226095,0.1565974,0.00004603744,0.00007487052,0.0001343688,0.0001904341,0.6659454,0.171422,0.0023165,0.002971128,0.00006272764],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7944049,0.0004634241,0.2010228,0.0002139369,0.00004188015,0.00004221402,0.0004998415,0.00101314,0.002298043],"genre_scores_gemma":[0.9522971,0.0001279726,0.04681228,0.00004930585,0.00000580958,0.00001573662,0.000337108,0.00005962408,0.0002951068],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007253604,"threshold_uncertainty_score":0.01442277,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01891237404528207,"score_gpt":0.2765243593414185,"score_spread":0.2576119852961364,"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."}}