{"id":"W2001258853","doi":"10.2134/agronj2000.9261047x","title":"Forecasting Spring Wheat Yield Using Time Series Analysis","year":2000,"lang":"en","type":"article","venue":"Agronomy Journal","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Exponential smoothing; Yield (engineering); Series (stratigraphy); Mathematics; Smoothing; Statistics; Growing season; Moving average; Time series; Environmental science; Meteorology; Agronomy; Geography; Geology","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":[0.000444797,0.0003521132,0.0002037267,0.0008833379,0.0001274229,0.0004263986,0.0002715165,0.0001916625,0.0006089404],"category_scores_gemma":[0.001349186,0.0001258407,0.000269692,0.001045529,0.00006718931,0.0003932474,0.0001472856,0.0002315142,0.0001569113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00082983,"about_ca_system_score_gemma":0.0004931951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0602488,"about_ca_topic_score_gemma":0.06545332,"domain_scores_codex":[0.9998776,0.00002767283,0.00001075765,0.00003264026,0.00003726661,0.0000140931],"domain_scores_gemma":[0.9997175,0.0001275036,0.00004290785,0.00001692486,0.00008335285,0.00001181688],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009160725,0.00007242503,0.07463237,0.00004619332,0.0001123742,0.0001414209,0.00007187477,0.8018938,0.007324679,0.001392594,0.001488864,0.1127318],"study_design_scores_gemma":[0.000004100769,0.0000180872,0.0214889,0.000003509996,0.00001289169,0.00001007991,0.00001983037,0.9766572,0.0009776728,0.0003717509,0.0004289423,0.000007093729],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8655056,0.0002616885,0.1288221,0.0001705277,0.00003552952,0.00005191693,0.001374067,0.0005910934,0.003187528],"genre_scores_gemma":[0.9796923,0.0002049261,0.0180675,0.000008691498,0.0000101654,0.00001992781,0.0009612088,0.00001663952,0.001018631],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0602488,"threshold_uncertainty_score":0.1197962,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156117939978058,"score_gpt":0.2102536638380146,"score_spread":0.1946418698402088,"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."}}