{"id":"W4252332928","doi":"10.3410/f.732388947.793546084","title":"Faculty Opinions recommendation of Structural Insights into Yeast Telomerase Recruitment to Telomeres.","year":2018,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Genetics, Aging, and Longevity in Model Organisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Youth Innovation Promotion Association; National Institutes of Health; Youth Innovation Promotion Association of the Chinese Academy of Sciences; Ligue Contre le Cancer; National Institute of General Medical Sciences; Chinese Academy of Sciences; National Natural Science Foundation of China","keywords":"Telomere; Telomerase; Yeast; Computational biology; Cellular Aging; Biology; Genetics; Evolutionary biology; DNA; Gene","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":[0.001089404,0.003000716,0.001719369,0.005009847,0.0009770734,0.002651986,0.002918224,0.002156499,0.1201868],"category_scores_gemma":[0.007070043,0.0006425845,0.001809733,0.008018758,0.0003832118,0.001657949,0.002252162,0.002152246,0.09195698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001508795,"about_ca_system_score_gemma":0.004299566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01695449,"about_ca_topic_score_gemma":0.04985256,"domain_scores_codex":[0.9990277,0.0001263629,0.0001075591,0.0002682854,0.0003373111,0.0001328141],"domain_scores_gemma":[0.9972365,0.0007465476,0.0003440373,0.000444764,0.0007304092,0.00049773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001319807,0.00003206391,0.001445541,0.003379804,0.0001634167,0.00006306203,0.00003551833,0.0004471785,0.0005779973,0.0007126527,0.9874555,0.005555294],"study_design_scores_gemma":[0.0002405058,0.00001571918,0.003129793,0.0004742757,0.00009440202,0.00005716022,0.0000391733,0.0004629458,0.0005624464,0.001034857,0.9938631,0.0000255551],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001786198,0.0002587884,0.0001021882,0.0001035797,0.00003810963,0.00001091079,0.9974062,0.000684055,0.001217545],"genre_scores_gemma":[0.0004458523,0.0002197278,0.000330129,0.00005313139,0.000007175563,0.00003181795,0.9982796,0.000110716,0.0005219126],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1201868,"threshold_uncertainty_score":0.4020647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03458372600375682,"score_gpt":0.3541406044063621,"score_spread":0.3195568784026053,"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."}}