{"id":"W3193414438","doi":"10.3390/soilsystems5030048","title":"Evaluating the Precision and Accuracy of Proximal Soil vis–NIR Sensors for Estimating Soil Organic Matter and Texture","year":2021,"lang":"en","type":"article","venue":"Soil Systems","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Soil texture; Loam; Environmental science; Soil science; Soil water; Soil test; Silt; Soil organic matter; Precision agriculture; Repeatability; Partial least squares regression; Remote sensing; Soil quality; Mathematics; Statistics; Geology; Geography","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":[0.003985392,0.0007198307,0.0004042808,0.0009966305,0.0002961126,0.0007342611,0.0007145082,0.0007305163,0.0003319159],"category_scores_gemma":[0.003810245,0.0003214283,0.000530354,0.0008029061,0.0003849264,0.0005409206,0.0005707594,0.0003886826,0.0004590941],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002198048,"about_ca_system_score_gemma":0.0002783214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002303344,"about_ca_topic_score_gemma":0.005996779,"domain_scores_codex":[0.997405,0.0005876666,0.0001435068,0.0007645949,0.0009873837,0.0001118729],"domain_scores_gemma":[0.9980162,0.0007866626,0.0003060976,0.0003315451,0.0005230617,0.00003637467],"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":[0.0007535406,0.0003196599,0.1401049,0.0004316268,0.0003232025,0.0001016523,0.00037396,0.01739467,0.6752338,0.0003284685,0.0002970447,0.1643376],"study_design_scores_gemma":[0.00005645141,0.001455183,0.4496037,0.0001102423,0.000494398,0.0005246483,0.0005818373,0.1426909,0.3998643,0.0006377234,0.003823481,0.0001571432],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9042683,0.001084803,0.09155302,0.00005334246,0.00004053636,0.00008678647,0.0004772139,0.0003416678,0.002094439],"genre_scores_gemma":[0.9176407,0.0006477216,0.08023682,0.00004426983,0.00002028977,0.00006773062,0.0004540004,0.0000448153,0.0008436365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003985392,"threshold_uncertainty_score":0.02107704,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02539839638036222,"score_gpt":0.2961139214917622,"score_spread":0.2707155251114,"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."}}