{"id":"W4413816953","doi":"10.1016/j.geoderma.2026.117873","title":"Predicting soil health properties across different agricultural land use systems using mid-infrared spectroscopy","year":2025,"lang":"en","type":"article","venue":"Geoderma","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Offshore Energy Research Association","keywords":"Agriculture; Infrared; Infrared spectroscopy; Spectroscopy; Environmental science; Soil health; Land use; Soil science; Business; Geography; Soil water; Chemistry; Physics; Engineering; Soil organic matter; Optics; Civil engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001244477,0.0001738315,0.0002228499,0.0000194756,0.0004396809,0.0002581024,0.0001445146,0.00004975547,0.00002141325],"category_scores_gemma":[0.00004302003,0.0001236555,0.00004059387,0.0001494768,0.00008098064,0.0001817902,0.0002510834,0.0001292395,0.00001984676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003003128,"about_ca_system_score_gemma":0.00002264591,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01020649,"about_ca_topic_score_gemma":0.0008506274,"domain_scores_codex":[0.9985917,0.00006289472,0.000286469,0.0003024128,0.0002299759,0.0005265395],"domain_scores_gemma":[0.9995496,0.00003630872,0.0001118851,0.0001915586,0.00001312859,0.00009752031],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002139212,0.00006702697,0.9483151,0.0001913751,0.00004136882,0.00000761548,0.002670464,0.01913718,0.02562526,0.0000316827,0.003296766,0.0005948115],"study_design_scores_gemma":[0.0006214686,0.00004790529,0.8952051,0.0003974972,0.00002068881,0.00002330205,0.002059496,0.09511353,0.004695687,0.00007979692,0.001427332,0.0003081666],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959229,0.0002677019,0.001833549,0.0001817012,0.0005640943,0.0003138742,0.00003890325,0.00008499228,0.0007923314],"genre_scores_gemma":[0.9977041,0.00003299739,0.0003400245,0.0002185889,0.00006406163,0.00001889202,0.00001893461,0.000009632963,0.001592694],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07597635,"threshold_uncertainty_score":0.9963846,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02543521980285502,"score_gpt":0.2594635776944217,"score_spread":0.2340283578915667,"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."}}