{"id":"W4404427765","doi":"10.5376/gab.2024.15.0013","title":"Advancements in Gene Editing Technologies for Mosquito Research","year":2024,"lang":"en","type":"article","venue":"Genomics and Applied Biology","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Genome editing; Biology; Computational biology; Computer science; Gene; Genetics; CRISPR","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.002608689,0.0006863323,0.0006455499,0.001114185,0.0004844428,0.001657265,0.0005649937,0.0007994731,0.003754208],"category_scores_gemma":[0.001479583,0.0004156229,0.0007298068,0.000864364,0.0006928517,0.00135404,0.001104807,0.002339453,0.001914938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009069411,"about_ca_system_score_gemma":0.0009860571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007361619,"about_ca_topic_score_gemma":0.0008538105,"domain_scores_codex":[0.9984522,0.0003130378,0.0001140432,0.0003251054,0.0006935794,0.0001020606],"domain_scores_gemma":[0.9989867,0.0003408063,0.0001751289,0.0001601308,0.000233743,0.0001034451],"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":[0.0001424223,0.000104892,0.0007934897,0.001676599,0.00006448568,0.0003529837,0.0003576173,0.001054691,0.6984985,0.02934142,0.005563298,0.2620496],"study_design_scores_gemma":[0.00003266423,0.0005640993,0.002121865,0.0004109187,0.000110343,0.002001826,0.0001807805,0.003150007,0.4637124,0.00785033,0.5197639,0.0001008549],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1022769,0.2705157,0.5373534,0.01313702,0.0038757,0.0005948994,0.001692993,0.002873643,0.06767976],"genre_scores_gemma":[0.2533585,0.3747633,0.3282668,0.00253406,0.001226774,0.0005056015,0.002017298,0.0007250858,0.03660262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003754208,"threshold_uncertainty_score":0.01379621,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01979385932603014,"score_gpt":0.379463701319589,"score_spread":0.3596698419935589,"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."}}