{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002953436,0.000322613,0.0004304067,0.003020882,0.0001334983,0.0006314375,0.000575732,0.000376446,0.00107723],"category_scores_gemma":[0.01234052,0.000265584,0.0003550567,0.007642112,0.0002165472,0.0008674585,0.0005068417,0.0003832359,0.001480225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004277352,"about_ca_system_score_gemma":0.0005436759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01389434,"about_ca_topic_score_gemma":0.01026602,"domain_scores_codex":[0.9970967,0.0009215564,0.0003279737,0.0005072601,0.001023935,0.0001227065],"domain_scores_gemma":[0.9843625,0.00743646,0.001912642,0.002117144,0.003850229,0.0003211511],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002408704,0.0002095139,0.8395841,0.0006192956,0.0003281457,0.0001360849,0.0002538367,0.009991404,0.003080721,0.0007704827,0.01514809,0.1296375],"study_design_scores_gemma":[0.00000933585,0.0002153735,0.9589698,0.00007930714,0.00005809826,0.0001314824,0.0003839092,0.0147499,0.00180402,0.0003202167,0.02324804,0.00003045895],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8786749,0.003756968,0.01816741,0.0007476795,0.00007619308,0.00009595173,0.09163051,0.0005438669,0.006306561],"genre_scores_gemma":[0.8416729,0.002956033,0.007943681,0.0001251627,0.00006337449,0.00008832389,0.1451803,0.00005485159,0.001915314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01389434,"threshold_uncertainty_score":0.02762693,"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."}}