{"id":"W3100994719","doi":"","title":"How well do crop modeling groups predict wheat phenology, given calibration data from the target population?","year":2021,"lang":"en","type":"preprint","venue":"University of Southern Queensland ePrints (University of Southern Queensland)","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Extrapolation; Phenology; Calibration; Population; Statistics; Mathematics; Ecology; Biology","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004455872,0.000548088,0.0009154707,0.00006449948,0.0006436444,0.0001785366,0.002548149,0.0008945595,0.001862408],"category_scores_gemma":[0.00006161253,0.0003189528,0.0004159081,0.0003201145,0.0003737154,0.0003734529,0.002952913,0.0007368139,0.0001017626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001184379,"about_ca_system_score_gemma":0.00006627948,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06640247,"about_ca_topic_score_gemma":0.04122557,"domain_scores_codex":[0.9967725,0.0005059328,0.000320957,0.001258129,0.000645035,0.0004974479],"domain_scores_gemma":[0.99741,0.0002354456,0.0008383301,0.0008913941,0.0003914531,0.0002333727],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.002049832,0.001099704,0.8651112,0.0005016226,0.002644493,0.0003358052,0.09880964,0.01047373,0.008230747,0.0000558022,0.006356236,0.004331245],"study_design_scores_gemma":[0.003017551,0.0002078815,0.122393,0.001478758,0.001433007,0.00001891185,0.8047703,0.05507734,0.00005530231,0.002922608,0.006497621,0.002127768],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9631568,0.0003454483,0.001269783,0.007198899,0.0001600861,0.000593583,0.02693811,0.0001366869,0.0002006426],"genre_scores_gemma":[0.9842478,0.0004297489,0.0009752192,0.00005409954,0.0002955837,1.539403e-7,0.01313969,0.0000106857,0.0008470112],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7427182,"threshold_uncertainty_score":0.9999263,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04085974511956759,"score_gpt":0.2015939354250623,"score_spread":0.1607341903054947,"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."}}