{"id":"W4394680131","doi":"10.2139/ssrn.4789781","title":"Novel Insights on Multivariate Modelling of Agricultural Soil Organic Carbon and Total Nitrogen from Diverse Ft-Nir Spectral Dataset","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Multivariate statistics; Soil carbon; Nitrogen; Total organic carbon; Environmental science; Agriculture; Carbon fibers; Soil science; Environmental chemistry; Mathematics; Statistics; Chemistry; Soil water; Ecology; Algorithm; Biology","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":[0.001019844,0.000711923,0.0003764252,0.0003746988,0.0002086685,0.001062637,0.0008144046,0.0006290241,0.0006876506],"category_scores_gemma":[0.002930658,0.0003059498,0.0008198192,0.0005922441,0.0003367368,0.001076477,0.0005755808,0.0008325462,0.0002230337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003519449,"about_ca_system_score_gemma":0.0006622577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008476807,"about_ca_topic_score_gemma":0.01309366,"domain_scores_codex":[0.9997812,0.00007592112,0.000009587197,0.00007079429,0.00003261151,0.00002993259],"domain_scores_gemma":[0.999302,0.0004392439,0.00007628535,0.00008753318,0.00006196409,0.00003295172],"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":[0.0001158841,0.0001212843,0.01201132,0.00009327461,0.0002007218,0.000153731,0.0001197391,0.9146356,0.01525752,0.008899686,0.002481,0.04591013],"study_design_scores_gemma":[0.00000226843,0.000005986008,0.002477258,0.000002304663,0.000006239334,0.00001352862,0.00001088981,0.9927738,0.0005034376,0.003884099,0.0003140855,0.000006063724],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4141331,0.0005017267,0.5797428,0.0008653366,0.00005984938,0.00002506422,0.002595808,0.0006069707,0.001469278],"genre_scores_gemma":[0.9284394,0.0004382758,0.06501138,0.000108272,0.0001014788,0.00003959796,0.003372249,0.0001679203,0.002321386],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008476807,"threshold_uncertainty_score":0.01685488,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01489950694237017,"score_gpt":0.2132297048591743,"score_spread":0.1983301979168041,"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."}}