{"id":"W4210480843","doi":"10.3390/agriengineering4010006","title":"Precision Irrigation Management Using Machine Learning and Digital Farming Solutions","year":2022,"lang":"en","type":"article","venue":"AgriEngineering","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":254,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Agriculture; Precision agriculture; Irrigation; Software deployment; Computer science; Sustainable agriculture; Irrigation management; Agricultural engineering; Engineering; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009614988,0.0000829466,0.00006745487,0.00001443108,0.00058481,0.00007523246,0.00006695353,0.00001667171,0.00007113257],"category_scores_gemma":[0.000007670726,0.000036513,0.00003180172,0.0002589709,0.000007073854,0.0001617998,0.0002299601,0.0001225783,0.000002263591],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003383126,"about_ca_system_score_gemma":7.734425e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002872251,"about_ca_topic_score_gemma":0.000008333165,"domain_scores_codex":[0.9993965,0.00001325771,0.0001009467,0.0001629469,0.0001469479,0.0001793878],"domain_scores_gemma":[0.9998615,0.00003956323,0.00002886439,0.00001743561,0.000009824802,0.00004278047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002417244,0.0001409623,0.03038226,0.00003364321,0.00008115453,0.00004109957,0.0006167871,0.05190191,0.5321146,0.003645973,0.0001805158,0.3808369],"study_design_scores_gemma":[0.0006076019,0.0005354569,0.1649089,0.00009294727,0.00009537479,0.0002944354,0.005446983,0.1977897,0.001985404,0.0008942231,0.626084,0.001264891],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983022,0.0003297695,0.0002684077,0.000121518,0.0001227901,0.0001095321,0.00001185881,0.00009946504,0.0006344728],"genre_scores_gemma":[0.9992983,0.00001338978,0.0002286299,0.00001452601,0.00008937865,0.00001333108,0.00008009709,9.35923e-7,0.0002614483],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6259035,"threshold_uncertainty_score":0.4497945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184248166752605,"score_gpt":0.182820195556518,"score_spread":0.1709777138889919,"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."}}