{"id":"W3138631854","doi":"10.28924/2291-8639-19-2021-91","title":"Soil Quality Prediction for Determining Soil Fertility in Bhimtal Block of Uttarakhand (India) Using Machine Learning","year":2020,"lang":"en","type":"article","venue":"International Journal of Analysis and Applications","topic":"Soil and Land Suitability Analysis","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Soil fertility; Agriculture; Soil quality; Environmental science; Agricultural engineering; Nutrient; Soil carbon; Mathematics; Soil test; Agronomy; Agroforestry; Geography; Soil science; Soil water; Engineering; Biology; Ecology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"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":[0.0002120713,0.0003303234,0.0002528792,0.001029546,0.0002585704,0.0006470775,0.0004507077,0.0003585219,0.0007319524],"category_scores_gemma":[0.0005916114,0.0001396129,0.0004143043,0.001079814,0.0001692194,0.0002540953,0.0003103681,0.0002759742,0.0002800286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006177009,"about_ca_system_score_gemma":0.0005489073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06028685,"about_ca_topic_score_gemma":0.07947952,"domain_scores_codex":[0.9998347,0.00003404524,0.00001768138,0.00003349879,0.00004080161,0.00003932862],"domain_scores_gemma":[0.9995558,0.0001518731,0.00008717929,0.00002664794,0.0001422694,0.00003620565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001992944,0.0003095346,0.9053065,0.0002229022,0.0001420403,0.0007393921,0.0004379974,0.03690738,0.006161272,0.0002726293,0.001601155,0.04769999],"study_design_scores_gemma":[0.00001790667,0.0002738477,0.8623589,0.00004490884,0.00008984317,0.0002695482,0.001188102,0.1298211,0.004011462,0.0002261312,0.001668557,0.00002968052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9939854,0.000158559,0.002315868,0.0001186251,0.000007651979,0.00003122468,0.001409823,0.0001342235,0.001838703],"genre_scores_gemma":[0.9963669,0.00008651021,0.001908139,0.00001370457,0.000002847016,0.00001584279,0.001070363,0.000004407725,0.000531279],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06028685,"threshold_uncertainty_score":0.1198719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02748123781283611,"score_gpt":0.3008156163798241,"score_spread":0.273334378566988,"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."}}