{"id":"W4319440180","doi":"10.1002/for.2956","title":"Using a machine learning approach and big data to augment WASDE forecasts: Empirical evidence from US corn yield","year":2023,"lang":"en","type":"article","venue":"Journal of Forecasting","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Yield (engineering); Computer science; Machine learning; Agriculture; Econometrics; Economics; Geography","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":[0.00495659,0.0004447048,0.0002763955,0.001051842,0.0002944348,0.001255316,0.0006443356,0.000697666,0.00104284],"category_scores_gemma":[0.02526953,0.0002176525,0.0004256447,0.001683874,0.0003946492,0.002025943,0.0005439745,0.001301523,0.0003059938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007226291,"about_ca_system_score_gemma":0.0005240529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01527658,"about_ca_topic_score_gemma":0.01466893,"domain_scores_codex":[0.9987102,0.0006726948,0.00006936491,0.0001534088,0.0003341578,0.00006012876],"domain_scores_gemma":[0.9706945,0.02229185,0.002258048,0.001634024,0.002822138,0.0002993484],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006757287,0.0005654649,0.6152837,0.0001992452,0.0004742641,0.000290947,0.0002404199,0.2652698,0.0008622779,0.002548202,0.006220165,0.1073697],"study_design_scores_gemma":[0.00005510951,0.0003180655,0.2210553,0.0001280869,0.0001247898,0.00007629994,0.0005121906,0.7653681,0.002024923,0.005888529,0.004388674,0.00005994576],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9823707,0.000949373,0.008664318,0.00195131,0.0001175562,0.00003608222,0.001373604,0.0001614871,0.004375564],"genre_scores_gemma":[0.9961139,0.0002221305,0.002464169,0.00006484197,0.00003398583,0.0000087697,0.0008940055,0.000008376571,0.0001898921],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01527658,"threshold_uncertainty_score":0.0303753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6783027688674481,"score_gpt":0.3642247543382261,"score_spread":0.314078014529222,"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."}}