{"id":"W4392966768","doi":"10.20944/preprints202403.0999.v1","title":"A Method to Estimate Climate Drivers of Maize Yield Predictability Leveraging Genetic-by-Environment Interactions in the US and Canada","year":2024,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Genetically Modified Organisms Research","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Food and Agriculture; University of Nebraska-Lincoln; U.S. Department of Agriculture","keywords":"Predictability; Yield (engineering); Climate change; Environmental science; Econometrics; Environmental resource management; Natural resource economics; Climatology; Computer science; Agronomy; Economics; Statistics; Mathematics; Ecology; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00108443,0.0002409981,0.0002988919,0.00002764526,0.00009925747,0.00004789486,0.0006975528,0.0001070985,0.0007526198],"category_scores_gemma":[0.0001606492,0.0001097634,0.00007463631,0.0001408357,0.00008845405,0.00001959647,0.003489112,0.0008621895,0.00004225071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002150331,"about_ca_system_score_gemma":0.00008804045,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.359351,"about_ca_topic_score_gemma":0.2417619,"domain_scores_codex":[0.997381,0.0003605686,0.0004449678,0.0008842223,0.0005144448,0.0004147369],"domain_scores_gemma":[0.9988748,0.0005223502,0.00008782212,0.0003121596,0.00003891085,0.0001639662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003059437,0.0001007974,0.7248313,0.000141778,0.00004515877,0.00002061082,0.0007868563,0.002810719,0.2654664,0.00002195589,0.0001181474,0.005625708],"study_design_scores_gemma":[0.00004429558,0.00002835828,0.9758039,0.00009431702,0.00003111745,0.000009061602,0.0003628955,0.0005246571,0.0208618,0.0006531975,0.001404614,0.0001817846],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942221,0.00008228656,0.0000887849,0.00395565,0.0001044914,0.0008589499,0.0002131469,0.00002273936,0.0004518547],"genre_scores_gemma":[0.9978986,0.0001003231,0.001595457,0.0001270073,0.00003590313,0.0001442557,0.00001867852,0.000003332448,0.00007649187],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2509726,"threshold_uncertainty_score":0.8240658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09957676913333435,"score_gpt":0.3442940673459027,"score_spread":0.2447172982125683,"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."}}