{"id":"W2919193161","doi":"10.5958/2395-146x.2019.00001.2","title":"Impact of climate change on disease scenario in crops","year":2019,"lang":"en","type":"article","venue":"Agricultural Research Journal","topic":"Plant Pathogens and Resistance","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Climate change; Agroforestry; Disease; Biotechnology; Agronomy; Geography; Environmental science; Biology; Medicine; Ecology; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006671552,0.0004423964,0.000285887,0.0009252526,0.0005373947,0.001541178,0.0003793636,0.0008815398,0.003783854],"category_scores_gemma":[0.001747401,0.0001240058,0.0007566603,0.001529864,0.0002956007,0.001059546,0.000733691,0.0007108462,0.0004275491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001591047,"about_ca_system_score_gemma":0.000866314,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02264711,"about_ca_topic_score_gemma":0.01324584,"domain_scores_codex":[0.9995267,0.0001177783,0.00003178475,0.00009333936,0.0001014578,0.000128907],"domain_scores_gemma":[0.9989039,0.0001819764,0.0002693734,0.00005503722,0.0003832144,0.0002065133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008679738,0.0002156522,0.8019146,0.0009330229,0.0008083215,0.003709994,0.0007835107,0.08138327,0.01085646,0.008882913,0.02604145,0.06360291],"study_design_scores_gemma":[0.00003457908,0.0003345318,0.8904752,0.0002183417,0.0003188804,0.001085329,0.002678079,0.04182758,0.002620762,0.005912293,0.05436416,0.0001302465],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9293163,0.009331296,0.003774447,0.008911332,0.0008170775,0.0001188557,0.01764508,0.0003480991,0.02973758],"genre_scores_gemma":[0.992985,0.002440411,0.0005922785,0.0004043787,0.00009491343,0.00002310149,0.00272687,0.00002044213,0.0007126614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02264711,"threshold_uncertainty_score":0.04503059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0708541963918444,"score_gpt":0.333598065426786,"score_spread":0.2627438690349416,"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."}}