{"id":"W4213385553","doi":"10.3410/f.733837595.793555489","title":"Faculty Opinions recommendation of Efficient proximity labeling in living cells and organisms with TurboID.","year":2019,"lang":"en","type":"dataset","venue":"Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature","topic":"Biotin and Related Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; World Wide Web; Information retrieval; Data science","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":[0.000815685,0.0003502576,0.0005613203,0.0001453038,0.0001199757,0.00004806593,0.0005463967,0.0006736922,0.0000323358],"category_scores_gemma":[0.00156811,0.0002054316,0.000163614,0.0007551175,0.0002597515,0.00001382194,0.0005165709,0.0006671877,0.000006209346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003263392,"about_ca_system_score_gemma":0.0002369308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002455859,"about_ca_topic_score_gemma":0.000009474381,"domain_scores_codex":[0.9976817,0.0002453024,0.0007632538,0.0005584584,0.0005076819,0.0002436508],"domain_scores_gemma":[0.9966654,0.00005332538,0.0007112911,0.0007119435,0.001749586,0.0001085048],"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":[0.00001217426,0.0002420362,0.00002573396,0.004130724,0.0001129526,1.112221e-7,0.00009972072,0.000001134754,0.0001438327,0.00001365489,0.9949238,0.0002941749],"study_design_scores_gemma":[0.0004484922,0.000179605,0.00106412,0.008850283,0.00009154748,0.0000159539,0.00005286325,0.00001045887,0.0004621629,0.000001146488,0.9885812,0.000242124],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003008547,0.002593393,0.00002159109,0.02764059,0.0004057244,0.001003889,0.968268,0.000007460268,0.00002926168],"genre_scores_gemma":[0.0001365951,0.001222103,0.0005605909,0.001046123,0.00009582307,0.00004525394,0.9964145,0.00001847921,0.000460468],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02814656,"threshold_uncertainty_score":0.8377256,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01534761267526377,"score_gpt":0.3019080900744566,"score_spread":0.2865604773991928,"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."}}