{"id":"W4386330354","doi":"10.36227/techrxiv.24052680","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":"","funders":"Mitacs","keywords":"Environmental science; Soil texture; Computer science; Artificial intelligence; Soil science; Remote sensing; Soil water; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002288444,0.0004201631,0.0002393934,0.000709381,0.000293431,0.000530087,0.000393606,0.0004207857,0.0008573316],"category_scores_gemma":[0.0003780435,0.0001564199,0.0002996969,0.0006223302,0.000229417,0.0005090682,0.0002278723,0.0002848922,0.0003325973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000423267,"about_ca_system_score_gemma":0.0004560404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02821786,"about_ca_topic_score_gemma":0.05550149,"domain_scores_codex":[0.9998313,0.000009598282,0.000004814643,0.00007773145,0.00004357067,0.00003299125],"domain_scores_gemma":[0.9997943,0.00004265031,0.00003476031,0.00002669136,0.00008114652,0.00002043617],"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":[0.0007407686,0.0004792496,0.1545915,0.0004614645,0.000236525,0.0005787438,0.000510407,0.05027667,0.4915414,0.0003825387,0.003089104,0.2971116],"study_design_scores_gemma":[0.00004441012,0.0002384804,0.5418128,0.00004577513,0.0001406942,0.0002479478,0.0006512534,0.3208094,0.1303284,0.000618315,0.004974834,0.00008773244],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9735532,0.0002287567,0.02088891,0.0000479824,0.00002431159,0.00004240073,0.001763313,0.0006936645,0.002757411],"genre_scores_gemma":[0.9786589,0.0001547461,0.01898316,0.00003409386,0.00001359568,0.00002460379,0.001365142,0.00005814941,0.0007075213],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02821786,"threshold_uncertainty_score":0.05610722,"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."}}