{"id":"W4388778220","doi":"10.1007/978-981-99-4863-5_8","title":"Climate Change Impact on Plants","year":2023,"lang":"en","type":"book-chapter","venue":"","topic":"Plant responses to elevated CO2","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Climate change; Crop productivity; Agriculture; Agricultural productivity; Crop; Greenhouse gas; Environmental science; Productivity; Crop production; Yield (engineering); Vegetation (pathology); Effects of global warming; Agronomy; Agroforestry; Natural resource economics; Biology; Ecology; Global warming; Economics; Medicine","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":[0.00008674059,0.0002862331,0.000171358,0.0002516614,0.0002993588,0.0009783892,0.0002725621,0.0004684328,0.02663726],"category_scores_gemma":[0.0001368773,0.0001345367,0.0001676801,0.0006486669,0.0004121183,0.0006935724,0.0004297553,0.0006468028,0.004488898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008661007,"about_ca_system_score_gemma":0.0003116953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004392637,"about_ca_topic_score_gemma":0.007185622,"domain_scores_codex":[0.9999461,0.000006393428,0.000001043268,0.0000116573,0.00002922407,0.000005572428],"domain_scores_gemma":[0.9999677,0.00001563766,0.000002039827,0.000004515792,0.000006759666,0.000003330473],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001332147,0.0001058676,0.0008374294,0.0008673154,0.00003597297,0.0003927325,0.0008533349,0.005633641,0.02488625,0.2150663,0.1861245,0.5650634],"study_design_scores_gemma":[0.000002395389,0.00003043859,0.003951746,0.0001309628,0.000009549545,0.0001498129,0.0001598395,0.0003839165,0.001702309,0.03358128,0.9598913,0.000006483174],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00801492,0.05540227,0.001933541,0.002815406,0.001439028,0.00001552525,0.000372077,0.00009486231,0.9299124],"genre_scores_gemma":[0.1195255,0.1020827,0.001430719,0.001839257,0.001260326,0.00002328158,0.0005349275,0.000150556,0.7731528],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.02663726,"threshold_uncertainty_score":0.08911049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07175845507756196,"score_gpt":0.2544269691339096,"score_spread":0.1826685140563476,"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."}}