{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008793664,0.0002569153,0.0001872289,0.0001247859,0.0004744741,0.0001131746,0.0003529166,0.0001446019,0.00006669233],"category_scores_gemma":[0.0001983361,0.0002133377,0.0001848544,0.0001862958,0.00005398628,0.00001180023,0.0001404914,0.000305527,0.00002175377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000533295,"about_ca_system_score_gemma":0.00003922448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006942511,"about_ca_topic_score_gemma":0.000001323755,"domain_scores_codex":[0.9984357,0.0001351312,0.0003618153,0.0003005063,0.0002466647,0.0005202147],"domain_scores_gemma":[0.9992585,0.00008134556,0.000148769,0.0002892065,0.00007117417,0.0001509698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001268451,0.00002763063,0.0002948891,0.00002175669,0.00008079084,0.000008530301,0.0001424404,0.7450767,0.2423245,0.000002639688,0.0008611878,0.01103206],"study_design_scores_gemma":[0.001350413,0.0004863936,0.001009761,0.00002604249,0.0001157254,0.00007917185,0.0004229302,0.2978141,0.6865962,0.0001211697,0.01159209,0.000386015],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01532056,0.0006799814,0.982334,0.0005705854,0.0006831956,0.0003059335,0.000003396155,0.00004563557,0.00005666748],"genre_scores_gemma":[0.974189,0.001357376,0.0221542,0.0001577083,0.001390443,0.00006987568,0.00008502847,0.0001024574,0.0004938847],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9601799,"threshold_uncertainty_score":0.8699658,"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."}}