{"id":"W2808928550","doi":"10.21467/proceedings.1.58","title":"Survey of Estimation of Crop Yield Using Agriculture Data","year":2018,"lang":"en","type":"article","venue":"","topic":"Agricultural Economics and Practices","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Estimation; Agriculture; Yield (engineering); Crop; Volume (thermodynamics); Agricultural economics; Agricultural engineering; Agricultural science; Engineering; Computer science; Geography; Environmental science; Forestry; Economics; Systems engineering","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":[0.0003516944,0.00006718341,0.0001366701,0.000003811552,0.00006510333,0.00002142544,0.0003261943,0.00006162139,0.0006889771],"category_scores_gemma":[0.0001724874,0.00001862277,0.00002188646,0.0001738368,0.000059481,0.0003432724,0.0001377802,0.00003773607,0.0000103251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004470215,"about_ca_system_score_gemma":0.000005421722,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01329447,"about_ca_topic_score_gemma":0.008500802,"domain_scores_codex":[0.9994053,0.00004218558,0.0002256756,0.0001639733,0.00007484004,0.00008797606],"domain_scores_gemma":[0.9992225,0.0002773894,0.0002477173,0.00007903504,0.0001434166,0.0000299165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00007786987,0.0001815972,0.02622239,0.00001875867,0.00006898845,3.810113e-7,0.0001057003,0.0002723472,0.8406088,0.0004524861,0.01042416,0.1215665],"study_design_scores_gemma":[0.00005254017,0.0002348909,0.944792,0.00002408513,0.00001741639,0.000004137633,0.0002069046,0.01350729,0.03831008,0.00007875908,0.00261171,0.0001602099],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972185,0.00006585268,0.00003320212,0.0001918273,0.00006982464,0.00006853013,0.0001894772,0.000009017535,0.002153791],"genre_scores_gemma":[0.9986681,0.00002376526,0.0008155566,0.0000354595,0.00009144322,2.652546e-7,0.0002554356,2.290291e-7,0.0001097126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9185696,"threshold_uncertainty_score":0.9932761,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1920935641028921,"score_gpt":0.3061904688574759,"score_spread":0.1140969047545838,"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."}}