{"id":"W4386309464","doi":"10.36227/techrxiv.24052680.v1","title":"Mapping Soil Organic Matter under Field Conditions","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Mitacs","keywords":"Environmental science; Soil texture; Computer science; Artificial intelligence; Soil science; Remote sensing; Soil water; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002417202,0.0004190495,0.0002697753,0.0007103345,0.0002865255,0.0005288497,0.0004186841,0.0004110295,0.0008253983],"category_scores_gemma":[0.0003809092,0.0001565865,0.0003102692,0.0006722605,0.0002293041,0.000517077,0.0002309625,0.000286684,0.0003434806],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004499157,"about_ca_system_score_gemma":0.0004441489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02446591,"about_ca_topic_score_gemma":0.05880969,"domain_scores_codex":[0.9998127,0.00001079526,0.000005131159,0.0000851383,0.00004942299,0.00003688452],"domain_scores_gemma":[0.9997846,0.00004135919,0.00003817906,0.00002863087,0.00008581077,0.00002147927],"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":[0.0008463752,0.000492657,0.184936,0.0006005093,0.0003107783,0.0005781857,0.000582336,0.04850654,0.4690007,0.0004195809,0.003138022,0.2905883],"study_design_scores_gemma":[0.00004993294,0.0002195316,0.6392883,0.00005427628,0.0001681745,0.0002432422,0.0006695291,0.2456597,0.107506,0.0007173207,0.005335158,0.00008883674],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9760154,0.0003034814,0.01822549,0.00004766286,0.00002522187,0.00004524134,0.00192082,0.000602786,0.00281385],"genre_scores_gemma":[0.9800888,0.0002092944,0.01717836,0.0000369211,0.00001521014,0.00002560584,0.00169488,0.00005439129,0.000696595],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02446591,"threshold_uncertainty_score":0.04864705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03818238378758965,"score_gpt":0.2338550768703896,"score_spread":0.1956726930827999,"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."}}