{"id":"W6911111585","doi":"10.5061/dryad.p8cz8wb0h","title":"Data from: Impacts of weather anomalies and climate on plant disease","year":2024,"lang":"en","type":"dataset","venue":"DRYAD","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Climate change; Maladaptation; Agriculture; Disease; Precipitation; Ecosystem; Vulnerability (computing); Global warming; Outbreak","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":[0.0007673535,0.001397101,0.000898767,0.001829572,0.0004593022,0.001313138,0.001507236,0.001382845,0.01937092],"category_scores_gemma":[0.002931422,0.0004752566,0.001042978,0.003249664,0.0002765892,0.0007042328,0.001083846,0.0009637705,0.01845274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008424388,"about_ca_system_score_gemma":0.0009947413,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02921314,"about_ca_topic_score_gemma":0.04754017,"domain_scores_codex":[0.9994488,0.0000996964,0.00008639466,0.0001644465,0.0001293142,0.00007135071],"domain_scores_gemma":[0.998863,0.0003114668,0.0002402918,0.000223069,0.0002509218,0.0001112777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002626755,0.00007592941,0.01495828,0.00203317,0.0002225282,0.00009647713,0.00008445178,0.002239292,0.0006124211,0.0006477014,0.9726259,0.006141224],"study_design_scores_gemma":[0.0007782278,0.00006937254,0.06489468,0.0004442087,0.0001236512,0.000177805,0.0001969731,0.00362757,0.0009511241,0.001239548,0.9274171,0.00007965077],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009404779,0.0001138599,0.00006957633,0.00005844438,0.00002215994,0.00000677585,0.9981646,0.0002167963,0.0004072458],"genre_scores_gemma":[0.001962698,0.00006846522,0.0003023712,0.00003286385,0.00000852606,0.00003810896,0.9972373,0.00002947518,0.0003200387],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02921314,"threshold_uncertainty_score":0.06480217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04343047024973249,"score_gpt":0.3148468517872859,"score_spread":0.2714163815375534,"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."}}