{"id":"W4387965927","doi":"10.1111/1755-0998.13881","title":"CRISPR‐based diagnostics detects invasive insect pests","year":2023,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of General Medical Sciences; Agriculture and Agri-Food Canada; Animal and Plant Health Inspection Service; Indian Agricultural Research Institute; National Institutes of Health; Indian Council of Agricultural Research; U.S. Department of Agriculture","keywords":"Biology; CRISPR; Computational biology; Identification (biology); Molecular diagnostics; Biotechnology; Gene; Genetics; Ecology","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":[0.0004007362,0.0005503453,0.0004041492,0.000452254,0.0001833045,0.0004895171,0.0004054972,0.0007989366,0.001297115],"category_scores_gemma":[0.0004831941,0.0002312395,0.0003520447,0.0001539577,0.0003889378,0.0002504252,0.0005089151,0.0006509462,0.0006243102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003414106,"about_ca_system_score_gemma":0.0002303209,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003730153,"about_ca_topic_score_gemma":0.0009116394,"domain_scores_codex":[0.9994412,0.00008441103,0.00004850758,0.0001532233,0.0002038041,0.00006884095],"domain_scores_gemma":[0.9996176,0.0001186014,0.0001262307,0.0000492969,0.00004456425,0.00004373298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002278583,0.00001651434,0.0004562073,0.00005821168,0.000006557795,0.00004485904,0.00001279425,0.0001506648,0.9943212,0.0001452812,0.0001388969,0.004626054],"study_design_scores_gemma":[0.000007786052,0.0001855842,0.002230796,0.00001098198,0.00001699145,0.0002779076,0.00002479497,0.001650277,0.9910446,0.0001191819,0.004418705,0.0000122972],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8122014,0.002972072,0.1666552,0.0008281438,0.0002314126,0.0003180185,0.002195539,0.004406144,0.01019207],"genre_scores_gemma":[0.9086653,0.001567252,0.08138867,0.000423024,0.0000308146,0.0001700893,0.001640777,0.0001591542,0.005955084],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001297115,"threshold_uncertainty_score":0.004339278,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00859471678461473,"score_gpt":0.2688894441509841,"score_spread":0.2602947273663694,"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."}}