{"id":"W2084785009","doi":"10.1080/09064710.2012.711353","title":"Near-infrared spectroscopic assessment of hot water extractable and oxidizable organic carbon in cultivated and uncultivated Mollisols in China","year":2012,"lang":"en","type":"article","venue":"Acta Agriculturae Scandinavica Section B - Soil & Plant Science","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Mollisol; Partial least squares regression; Soil carbon; Coefficient of determination; Total organic carbon; Correlation coefficient; Environmental science; Dissolved organic carbon; Soil organic matter; Soil water; Calibration; Soil test; Organic matter; Environmental chemistry; Soil science; Chemistry; Mathematics; Statistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006475603,0.0002149232,0.0002684302,0.0001162357,0.0002501071,0.0001180538,0.0001726359,0.00009183177,0.0001093788],"category_scores_gemma":[0.00005939323,0.0001406243,0.00001532778,0.001143631,0.000349743,0.0008229133,0.0002132736,0.0002469427,0.000002653638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003842385,"about_ca_system_score_gemma":0.00003135984,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007578702,"about_ca_topic_score_gemma":0.0009050742,"domain_scores_codex":[0.9980283,0.00006302762,0.000345983,0.000466004,0.0004221592,0.0006745768],"domain_scores_gemma":[0.9994612,0.00005125566,0.0001300884,0.0001465907,0.00002046112,0.0001904187],"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":[0.00001134202,0.00008082828,0.3971222,0.00001102199,0.000002574804,0.000002682031,0.001010035,0.000112701,0.601541,0.00001541591,0.00004811335,0.0000420664],"study_design_scores_gemma":[0.0005067281,0.00007676239,0.9238874,0.00006642951,0.000008409735,0.00004578136,0.0003770756,0.004699378,0.06993956,0.00004335362,0.000130442,0.0002187365],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967895,0.00003479589,0.00001418568,0.00007149306,0.0001705341,0.0003178088,0.00001285141,0.00002542846,0.002563375],"genre_scores_gemma":[0.999189,0.0001196977,0.0004128198,0.0000200707,0.00002161603,0.00002039315,0.00003265159,0.000007416132,0.0001763956],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5316015,"threshold_uncertainty_score":0.9990299,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006361462084344374,"score_gpt":0.2237432463625162,"score_spread":0.2173817842781718,"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."}}