{"id":"W2119413948","doi":"10.1101/013763","title":"Strong spurious transcription likely a cause of DNA insert bias in typical metagenomic clone libraries","year":2015,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Phylogenetic Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; University of Waterloo; Compute Canada; Genome Canada","keywords":"Spurious relationship; Insert (composites); Metagenomics; Biology; Computational biology; Genetics; Transcription (linguistics); Library; DNA; Computer science; Gene; Engineering; Structural engineering; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002125537,0.0008277466,0.001062436,0.001537482,0.0005071162,0.001260907,0.0005633517,0.000844879,0.001920357],"category_scores_gemma":[0.0061912,0.000711998,0.0007730036,0.001959788,0.0007063622,0.000668457,0.0009612088,0.001175634,0.001449148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005006109,"about_ca_system_score_gemma":0.0005756519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005096489,"about_ca_topic_score_gemma":0.0007262605,"domain_scores_codex":[0.9956571,0.000761841,0.0005753346,0.001168633,0.00136574,0.0004713156],"domain_scores_gemma":[0.9924001,0.002689686,0.00192344,0.001105006,0.001572869,0.0003088448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002576339,0.00006073806,0.0133716,0.0003699795,0.00006462925,0.0005345186,0.0002239174,0.0002437318,0.9748087,0.0003381361,0.0001472806,0.009579152],"study_design_scores_gemma":[0.00001793107,0.0002416884,0.03246741,0.00008161406,0.0001346484,0.002118458,0.0001658273,0.002520596,0.9571765,0.0007078192,0.004339017,0.00002856487],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8266716,0.003453284,0.1619359,0.0004784934,0.0002199184,0.0003740926,0.002210768,0.001562943,0.003092984],"genre_scores_gemma":[0.8946425,0.001161964,0.09603134,0.0008615021,0.00007709754,0.0003025301,0.003959184,0.0004858135,0.002478109],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002125537,"threshold_uncertainty_score":0.01124102,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03749631691530223,"score_gpt":0.2346122325727595,"score_spread":0.1971159156574573,"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."}}