{"id":"W3022962625","doi":"10.1101/2020.04.28.066233","title":"Plant pathogen infection risk tracks global crop yields under climate change","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Plant-Microbe Interactions and Immunity","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Biotechnology and Biological Sciences Research Council; Canadian Institute for Advanced Research; Bill and Melinda Gates Foundation","keywords":"Crop; Food security; Crop yield; Climate change; Tropics; Crop productivity; Crop production; Yield (engineering); Productivity","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003987219,0.0002050231,0.0001766515,0.000347152,0.0001076358,0.000592309,0.0001189833,0.0003219429,0.001662978],"category_scores_gemma":[0.0008404075,0.00007414776,0.0002151229,0.0005435313,0.0001318951,0.0004036987,0.0002350477,0.000276322,0.000572303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004159794,"about_ca_system_score_gemma":0.0001350985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003305396,"about_ca_topic_score_gemma":0.002545312,"domain_scores_codex":[0.9998883,0.00002102109,0.000005468961,0.00004512943,0.00001864928,0.00002140713],"domain_scores_gemma":[0.9994841,0.0001013388,0.0002573148,0.00004049997,0.00006941627,0.00004728879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004572477,0.00005973946,0.8867992,0.0000890033,0.0002237882,0.0001202801,0.00009141659,0.0329472,0.05913002,0.0008460024,0.001961777,0.01727429],"study_design_scores_gemma":[0.000006000569,0.0001302907,0.9699708,0.000008333499,0.00004034133,0.0001214053,0.00009476187,0.02077457,0.00615476,0.0007086579,0.001971571,0.00001850039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9933318,0.0003200056,0.001758207,0.0001305157,0.0000087056,0.000004569234,0.002613365,0.00008789058,0.001744891],"genre_scores_gemma":[0.9980876,0.0001368819,0.0004367273,0.00002811328,0.000004466693,0.000003264657,0.0009754154,0.00001123084,0.0003161375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003305396,"threshold_uncertainty_score":0.006572306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03419631716168642,"score_gpt":0.226376963413289,"score_spread":0.1921806462516026,"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."}}