{"id":"W2185820852","doi":"","title":"Remote sensing, geographic information system and modeling techniques for wheat area and production estimation.","year":2010,"lang":"en","type":"article","venue":"Journal of Farm Sciences","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Yield (engineering); Crop yield; Estimation; Agricultural engineering; Global Positioning System; Crop; Crop simulation model; Normalized Difference Vegetation Index; Environmental science; Productivity; Geography; Computer science; Climate change; Agronomy; Ecology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"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":[0.001194566,0.0008164793,0.0006556304,0.001867623,0.0003029119,0.0009575133,0.0007558089,0.0007801243,0.005946727],"category_scores_gemma":[0.001632282,0.0002581309,0.0005861519,0.003808015,0.0002700882,0.001202281,0.0008023764,0.0009846273,0.005147265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004142721,"about_ca_system_score_gemma":0.0007489735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005975666,"about_ca_topic_score_gemma":0.005779578,"domain_scores_codex":[0.9993359,0.0002467184,0.00004907182,0.00006678428,0.0002707669,0.00003063152],"domain_scores_gemma":[0.9994106,0.0002496295,0.00006189139,0.00008785632,0.0001680367,0.00002212071],"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":[0.0000450767,0.0001209501,0.00324973,0.0009845215,0.0001498105,0.0002293526,0.0001451169,0.0522664,0.004662305,0.02296217,0.09089439,0.8242902],"study_design_scores_gemma":[0.00004446787,0.0001916635,0.01262306,0.0005600105,0.0001428431,0.001046395,0.0003956931,0.356893,0.004174032,0.08448765,0.5393009,0.0001402517],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006377501,0.03094946,0.922127,0.002249009,0.001039865,0.0006099145,0.006722991,0.003847837,0.02607641],"genre_scores_gemma":[0.1312592,0.03731916,0.7866074,0.0008509398,0.0007892455,0.001079531,0.01252995,0.0003951694,0.02916931],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005975666,"threshold_uncertainty_score":0.01989377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01192717463834128,"score_gpt":0.233068519033473,"score_spread":0.2211413443951318,"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."}}