{"id":"W2999234005","doi":"10.29173/hsi239","title":"Query into the Future of Gene Editing: Possibilities and Apprehensions","year":2017,"lang":"en","type":"article","venue":"Health Science Inquiry","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Ted Rogers Centre for Heart Research; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Computer science; Genome editing; Information retrieval; Computational biology; Data science; Gene; World Wide Web; Biology; Genetics; Genome","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.03485553,0.001350458,0.002622112,0.002390896,0.004739109,0.01401999,0.004972879,0.01352554,0.008731923],"category_scores_gemma":[0.03241152,0.0006505979,0.001187997,0.001501924,0.04502242,0.03741543,0.006626652,0.02122732,0.002171157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00522232,"about_ca_system_score_gemma":0.008004467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002804393,"about_ca_topic_score_gemma":0.003779993,"domain_scores_codex":[0.9917843,0.003898362,0.0004409395,0.000824479,0.00240458,0.0006473009],"domain_scores_gemma":[0.9583827,0.03064637,0.001343082,0.002235067,0.005433018,0.001959651],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003068236,0.0001322383,0.0007636177,0.0007991437,0.00005178355,0.0006506011,0.003086454,0.001429092,0.001213991,0.87731,0.06631722,0.04793898],"study_design_scores_gemma":[0.00003017785,0.00006461296,0.000256039,0.0005325861,0.00001471433,0.000360827,0.004775059,0.0006489775,0.0002667569,0.7980418,0.1949329,0.00007556513],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.003755862,0.238389,0.0119709,0.7112182,0.007316643,0.00003555892,0.000147516,0.0001193114,0.02704695],"genre_scores_gemma":[0.2213519,0.395909,0.02053804,0.2945806,0.04896145,0.0003604737,0.000285638,0.0002584846,0.01775425],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03485553,"threshold_uncertainty_score":0.1843359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271472240631317,"score_gpt":0.3825576610390751,"score_spread":0.3554104369759434,"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."}}