{"id":"W3017369941","doi":"10.1093/jipm/pmaa002","title":"Biological Control of Lepidopteran Pests in Rice: A Multi-Nation Case Study From Asia","year":2020,"lang":"en","type":"article","venue":"Journal of Integrated Pest Management","topic":"Insect-Plant Interactions and Control","field":"Agricultural and Biological Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Australian Centre for International Agricultural Research; Agriculture and Agri-Food Canada; Ministry of Agriculture of the People's Republic of China; Department for International Development","keywords":"Integrated pest management; Trichogramma; Biological pest control; Pest control; European union; Biology; Natural enemies; Agroforestry; Agricultural science; PEST analysis; Biotechnology; Beneficial insects; Sustainability; Toxicology; Business; Agronomy; Ecology; Horticulture; International trade","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000314558,0.0001338306,0.0003222765,0.00005111703,0.00004978655,0.0000457132,0.0002033481,0.00005530986,0.0003400624],"category_scores_gemma":[0.00006590922,0.00004922123,0.0001248307,0.0003246892,0.00001716793,0.0001243833,0.00002465384,0.0002637661,0.00001067942],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005103087,"about_ca_system_score_gemma":0.000007741869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002212642,"about_ca_topic_score_gemma":0.001199891,"domain_scores_codex":[0.9986631,0.0002223956,0.000629531,0.0001613588,0.0001880838,0.000135579],"domain_scores_gemma":[0.9991472,0.0001648119,0.00038941,0.0000339425,0.0001886347,0.0000760374],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.005058403,0.009728267,0.3324324,0.00004604008,0.001642495,0.03013302,0.004524006,0.001253437,0.2202599,0.0002939003,0.001476527,0.3931516],"study_design_scores_gemma":[0.014363,0.01230352,0.801316,0.0003746164,0.0005168239,0.001200337,0.124268,0.03013226,0.001506318,0.000118719,0.01305827,0.0008422093],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974428,0.00007987205,0.0004989073,0.001176237,0.0001562912,0.0003488495,0.00004571404,0.00001065615,0.0002406486],"genre_scores_gemma":[0.9993001,0.00003577326,0.0002235565,0.0002600374,0.0001370531,0.000009392287,0.00001295912,8.344012e-7,0.00002025094],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4688835,"threshold_uncertainty_score":0.3723445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03689916321432313,"score_gpt":0.2542664660500986,"score_spread":0.2173673028357755,"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."}}