{"id":"W2050699592","doi":"10.1016/j.envsoft.2008.02.008","title":"Modelling crop productivity and variability for policy and impacts of climate change in eastern Canada","year":2008,"lang":"en","type":"article","venue":"Environmental Modelling & Software","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"University of Guelph","keywords":"Climate change; Environmental science; Index (typography); Productivity; Geography; Physical geography; Statistic; Climatology; Ecology; Statistics; Mathematics; Economics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001903939,0.0001982492,0.0002603817,0.00001405049,0.0001633085,0.0000141507,0.0000848439,0.00008865059,0.00000981329],"category_scores_gemma":[0.00003480759,0.00009495366,0.00003536906,0.0001043834,0.0001329152,0.0002051866,0.0001013526,0.0001059896,3.276136e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001653045,"about_ca_system_score_gemma":0.00001452944,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1572856,"about_ca_topic_score_gemma":0.06044481,"domain_scores_codex":[0.9987594,0.0000438247,0.0002282479,0.0004028186,0.0001782511,0.0003873865],"domain_scores_gemma":[0.9994986,0.0001629597,0.0001094673,0.00006938473,0.000009934649,0.0001496327],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002533476,0.0004902176,0.9224421,0.0002637117,0.00002283375,0.00001466813,0.00340139,0.02992456,0.01475972,0.00005951504,0.00001355543,0.02835437],"study_design_scores_gemma":[0.001298505,0.0006372525,0.7000127,0.0003002639,0.00005006176,0.0001177256,0.0005970276,0.2906089,0.002832439,0.001590497,0.0006850817,0.001269563],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975682,0.0003362033,0.0003514357,0.0004914165,0.00002767065,0.0005635103,0.0006357987,0.00001983021,0.000005864442],"genre_scores_gemma":[0.9976777,0.001213551,0.0007665979,0.0000699165,0.0001483952,0.00003200859,0.00007853223,0.000003137113,0.00001009689],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2606843,"threshold_uncertainty_score":0.9566996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04278828562494558,"score_gpt":0.2264685359285205,"score_spread":0.1836802503035749,"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."}}