{"id":"W2075910009","doi":"10.1038/nature12245","title":"Tiny enzyme uses context to succeed","year":2013,"lang":"en","type":"letter","venue":"Nature","topic":"Enzyme Production and Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Context (archaeology); Biology; Paleontology","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":[0.002956931,0.0008463798,0.001044659,0.0003077601,0.002360923,0.003402724,0.001439383,0.02056387,0.002555707],"category_scores_gemma":[0.01135718,0.00060646,0.0007549954,0.000297662,0.005668419,0.003654341,0.001950962,0.03030421,0.002867486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002697432,"about_ca_system_score_gemma":0.001359816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001521049,"about_ca_topic_score_gemma":0.002626018,"domain_scores_codex":[0.9983575,0.0003391866,0.0001258133,0.0002630414,0.0006855484,0.0002289298],"domain_scores_gemma":[0.9965771,0.002181502,0.0002222885,0.0003153895,0.000303545,0.0004001787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002489567,0.00006476889,0.0004375581,0.0001257624,0.00004983638,0.002501185,0.0001629739,0.0002652244,0.0021084,0.05163373,0.9024772,0.03992434],"study_design_scores_gemma":[0.0001737786,0.0001050493,0.0003461034,0.00007349084,0.00003830601,0.001634468,0.0001327474,0.001445787,0.005598604,0.09595794,0.8944173,0.00007631358],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.001442107,0.007202874,0.004103583,0.9342635,0.04377414,0.00002258859,0.00007059713,0.0002785075,0.008842126],"genre_scores_gemma":[0.05385647,0.00738328,0.005545934,0.8403465,0.04836511,0.0002007108,0.00006847145,0.0001635459,0.04406984],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02056387,"threshold_uncertainty_score":0.01957136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007090285851013578,"score_gpt":0.2382742187416841,"score_spread":0.2311839328906705,"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."}}