{"id":"W3111825697","doi":"","title":"Assessing the use the Canadian Precipitation Analysis Data for Monitoring and Modelling the Impacts of Weather on Agricultural Production","year":2019,"lang":"en","type":"article","venue":"AGU Fall Meeting Abstracts","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Agricultural productivity; Precipitation; Production (economics); Environmental science; Agriculture; Meteorology; Climatology; Natural resource economics; Environmental resource management; Geography; Economics","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":[0.001107341,0.0001600173,0.0001601325,0.00001969578,0.0006878064,0.000626083,0.0004286327,0.00008748243,0.000001655429],"category_scores_gemma":[0.0005980973,0.00003730026,0.00007280905,0.0004671416,0.00004751179,0.0005690905,0.00005978078,0.0001981606,0.000002776551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006027349,"about_ca_system_score_gemma":0.00001492233,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.20585,"about_ca_topic_score_gemma":0.4218551,"domain_scores_codex":[0.9987094,0.0001076986,0.0002553778,0.000332564,0.0002782823,0.0003167062],"domain_scores_gemma":[0.9978922,0.001295656,0.0003120068,0.0002332531,0.0001838911,0.00008301588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006529618,0.0001373879,0.4236986,0.00007927712,0.0006439258,0.000001432728,0.007260814,0.3697495,0.1845765,0.00005559737,0.001642553,0.01208904],"study_design_scores_gemma":[0.00005500434,0.00005137586,0.9883513,0.0001136196,0.0002027575,0.000003613717,0.005141757,0.003995443,0.00119819,0.00002050272,0.000736504,0.0001298846],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930215,0.0001215727,6.504359e-7,0.005721419,0.0002014094,0.0006801204,0.00006884652,0.00002075165,0.0001636547],"genre_scores_gemma":[0.9991161,0.00004894362,0.0001226426,0.00006804805,0.0003911388,0.00001362648,0.0001661283,0.000002033704,0.00007136309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5646527,"threshold_uncertainty_score":0.7994383,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1282605559638904,"score_gpt":0.305901770858776,"score_spread":0.1776412148948856,"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."}}