{"id":"W4388671533","doi":"10.22541/au.169995548.84464946/v1","title":"Smart Fields: Enhancing Agriculture with Machine Learning","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":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Agriculture; Sustainability; Precision agriculture; Agricultural engineering; Crop; Cornerstone; Fertilizer; Environmental science; Population; Sustainable agriculture; Business; Agroforestry; Environmental resource management; Engineering; Agronomy; Geography; Ecology; Biology; Environmental health","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":[0.0004979529,0.000505325,0.0003799731,0.0004642562,0.0001925094,0.0006645942,0.0005590074,0.0006606489,0.00290769],"category_scores_gemma":[0.001117969,0.0001893677,0.0003412599,0.0004945757,0.0002855239,0.001093031,0.0006661444,0.0005954521,0.0008785964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002625202,"about_ca_system_score_gemma":0.0002830194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001424702,"about_ca_topic_score_gemma":0.001738196,"domain_scores_codex":[0.9998239,0.00004482837,0.000008495591,0.00005095659,0.00005025761,0.0000216077],"domain_scores_gemma":[0.9995944,0.0002407732,0.00003075315,0.00005398416,0.00005937063,0.00002074581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001985421,0.0004368656,0.002751921,0.0002098704,0.0000944358,0.0001466593,0.0001308365,0.2177264,0.02556077,0.005465666,0.007563075,0.7397149],"study_design_scores_gemma":[0.00002023406,0.0001057013,0.0009809395,0.0000168409,0.0000115828,0.00003759845,0.00002910011,0.9773254,0.006114172,0.01024915,0.005097552,0.0000117005],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0483529,0.001023227,0.9351612,0.0007112033,0.0001854183,0.0001029955,0.0002984188,0.006414565,0.007750059],"genre_scores_gemma":[0.5157927,0.0010439,0.4740895,0.0004787504,0.0001674402,0.0001851541,0.0006064684,0.0002158505,0.007420228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00290769,"threshold_uncertainty_score":0.00972718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01928637225890752,"score_gpt":0.2020824651660962,"score_spread":0.1827960929071887,"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."}}