{"id":"W2519507161","doi":"10.1371/journal.pone.0162310","title":"Disentangling the Effects of Precipitation Amount and Frequency on the Performance of 14 Grassland Species","year":2016,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Ecology and Vegetation Dynamics Studies","field":"Environmental Science","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto Mississauga; University of Toronto","keywords":"Precipitation; Grassland; Biomass (ecology); Environmental science; Climate change; Agronomy; Ecology; Biology; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008602902,0.0003269899,0.0005305796,0.0005304233,0.0002992795,0.0004351373,0.0004280282,0.000333486,0.0004651694],"category_scores_gemma":[0.0009538534,0.0003244164,0.0003449629,0.0003504768,0.000435761,0.0004403193,0.000670317,0.0003840758,0.00009032156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003841399,"about_ca_system_score_gemma":0.0002289784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002354254,"about_ca_topic_score_gemma":0.005348748,"domain_scores_codex":[0.9993966,0.0002391759,0.00007072946,0.0001543186,0.00008006539,0.00005913513],"domain_scores_gemma":[0.9982692,0.0009067573,0.000320052,0.0001350933,0.0001459761,0.00022294],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.001575287,0.0004777284,0.276658,0.0001799657,0.0004309619,0.0001569141,0.000456484,0.001284813,0.7045585,0.00008877806,0.00004921134,0.01408331],"study_design_scores_gemma":[0.00001804652,0.0008941467,0.9902794,0.000002998449,0.00005792637,0.00005729241,0.000102427,0.001331642,0.006967146,0.00005252095,0.000224476,0.0000119149],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9997548,0.00003208924,0.0001054691,0.000003439868,6.710991e-7,0.000003647212,0.0000305129,0.000002324031,0.00006697137],"genre_scores_gemma":[0.9985033,0.00004000388,0.001000357,0.00002804879,0.000002960606,0.00002392078,0.0002635356,0.000006918734,0.0001309727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002354254,"threshold_uncertainty_score":0.00468111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185068925169688,"score_gpt":0.1857311854690287,"score_spread":0.1738804962173318,"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."}}