{"id":"W4318969623","doi":"10.1109/icdsaai55433.2022.10028970","title":"Krishi Nanban: AI Driven Precision Agriculture","year":2022,"lang":"en","type":"article","venue":"","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"SAIT Polytechnic","funders":"","keywords":"Agriculture; Dirt; Mistake; Metropolitan area; Agricultural economics; Business; Agricultural science; Geography; Agroforestry; Economics; Environmental science","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":[0.0007698016,0.0006896855,0.0003607934,0.000926764,0.001513559,0.004072965,0.001284598,0.001328221,0.1430937],"category_scores_gemma":[0.001422574,0.000263282,0.0003043596,0.001460831,0.0007260124,0.002336685,0.002643312,0.001785219,0.06703744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001265944,"about_ca_system_score_gemma":0.002495267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002216323,"about_ca_topic_score_gemma":0.002638028,"domain_scores_codex":[0.9988936,0.0001130271,0.00005473672,0.000281469,0.0005160326,0.0001410757],"domain_scores_gemma":[0.9988846,0.0001668149,0.00007747802,0.0001864945,0.0004087103,0.0002759063],"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":[0.0002928421,0.0002419843,0.003476965,0.0009250318,0.00002572064,0.002047717,0.001232224,0.001184619,0.01165867,0.04967129,0.2497421,0.6795009],"study_design_scores_gemma":[0.00001962048,0.00006976547,0.001753072,0.000139273,0.000008634152,0.0008998404,0.0005745744,0.001501126,0.002510484,0.005573199,0.9869192,0.00003108169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.01662284,0.008863671,0.02517697,0.01561069,0.003284234,0.0003656486,0.001951392,0.008133827,0.9199908],"genre_scores_gemma":[0.1603481,0.01094161,0.024516,0.003543996,0.0007969361,0.0002653967,0.002493765,0.001027724,0.7960665],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1430937,"threshold_uncertainty_score":0.4786962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008600473736633566,"score_gpt":0.1936893254685538,"score_spread":0.1850888517319202,"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."}}