{"id":"W4287840570","doi":"10.3390/rs14153510","title":"Quantitative Study of the Effect of Water Content on Soil Texture Parameters and Organic Matter Using Proximal Visible—Near Infrared Spectroscopy","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Soil Geostatistics and Mapping","field":"Environmental Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada; Université Laval; Institut National de la Recherche Scientifique","funders":"","keywords":"Silt; Soil texture; Soil science; Mean squared error; Soil test; Texture (cosmology); Calibration; Environmental science; Soil organic matter; Soil water; Organic matter; Spectroscopy; Mathematics; Chemistry; Geology; Statistics; Computer science; Artificial intelligence; 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":[0.0009574568,0.0004879504,0.0003699555,0.0007496272,0.0001672628,0.0004294671,0.0002517452,0.0003379311,0.0004421314],"category_scores_gemma":[0.001278146,0.0001922653,0.0003424429,0.000748417,0.0003099467,0.0005290429,0.0003142465,0.000305755,0.0002005474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001559992,"about_ca_system_score_gemma":0.0001565381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001448482,"about_ca_topic_score_gemma":0.003045875,"domain_scores_codex":[0.9992911,0.0001150793,0.0000329871,0.0001741676,0.00034954,0.00003715555],"domain_scores_gemma":[0.9992915,0.0003248684,0.0001682971,0.00004947908,0.0001478253,0.00001793689],"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":[0.0001843447,0.00006495562,0.04066006,0.0002748298,0.000160035,0.00009001285,0.0001350807,0.002268197,0.9195504,0.00007658746,0.00007377788,0.0364618],"study_design_scores_gemma":[0.00001410896,0.0005732728,0.3656999,0.00003478553,0.0002368678,0.0002966087,0.0003036443,0.0295496,0.6008293,0.0003257811,0.002067597,0.00006857616],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9622865,0.001242666,0.03462657,0.00002214185,0.00001664669,0.00005010027,0.0005533895,0.0001123846,0.001089718],"genre_scores_gemma":[0.9816756,0.0006877178,0.01651077,0.00003405991,0.000008072266,0.00005438819,0.0004296881,0.0000410326,0.0005588051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001448482,"threshold_uncertainty_score":0.005063593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01740624096587862,"score_gpt":0.243521337988241,"score_spread":0.2261150970223624,"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."}}