{"id":"W4384120674","doi":"10.1089/crispr.2023.0019","title":"CRISPR-Cas-Based Biomonitoring for Marine Environments: Toward CRISPR RNA Design Optimization Via Deep Learning","year":2023,"lang":"en","type":"article","venue":"The CRISPR Journal","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Heart, Lung, and Blood Institute; Universidad de La Frontera; Agencia Nacional de Investigación y Desarrollo; Ministry of Business, Innovation and Employment; National Institutes of Health; National Science Foundation","keywords":"CRISPR; Biomonitoring; Computer science; Computational biology; Biochemical engineering; Environmental science; Biology; Ecology; Engineering; Genetics","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":[0.0007533859,0.0008266593,0.0006508515,0.0002828287,0.0002696629,0.0009893558,0.0006579703,0.0008219128,0.001492953],"category_scores_gemma":[0.001313325,0.0003721132,0.0006686855,0.0003177923,0.0006217667,0.0005457014,0.0006501548,0.001409515,0.0004911462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007203292,"about_ca_system_score_gemma":0.001170435,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001889227,"about_ca_topic_score_gemma":0.003654681,"domain_scores_codex":[0.9996885,0.0000714366,0.00002005859,0.00008654222,0.00009633374,0.00003724087],"domain_scores_gemma":[0.9997301,0.0001457624,0.00003692068,0.00002106671,0.00004133789,0.0000248405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001689477,0.0001682822,0.002185523,0.0006710962,0.0001179563,0.0002087265,0.000119625,0.6537371,0.1659329,0.02055503,0.003138708,0.1529963],"study_design_scores_gemma":[0.00003213501,0.0002279686,0.0003521762,0.00004099666,0.00003654761,0.0000796721,0.00004345877,0.9266,0.05168987,0.0104909,0.01036918,0.00003710822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04897659,0.002161045,0.9396912,0.0008554038,0.00008171928,0.00009244354,0.0002252426,0.001842415,0.006074034],"genre_scores_gemma":[0.4420424,0.003112803,0.5482302,0.0008699858,0.00003480554,0.0002979709,0.0006103811,0.0003578934,0.004443549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001889227,"threshold_uncertainty_score":0.005226314,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01962043929224555,"score_gpt":0.3120887192727826,"score_spread":0.2924682799805371,"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."}}