{"id":"W4404473993","doi":"10.1111/1755-0998.14042","title":"Probe Capture Enrichment Sequencing of <scp><i>amoA</i></scp> Genes Improves the Detection of Diverse Ammonia‐Oxidising Archaeal and Bacterial Populations","year":2024,"lang":"en","type":"article","venue":"Molecular Ecology Resources","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Institute of Genetics; Japan Science and Technology Agency; Japan Agency for Marine-Earth Science and Technology; Japan Society for the Promotion of Science; Ministry of Education, Culture, Sports, Science and Technology","keywords":"Biology; Metagenomics; Amplicon; Ammonia monooxygenase; Computational biology; Primer (cosmetics); Deep sequencing; Genetics; Phylogenetic tree; Gene; 16S ribosomal RNA; DNA sequencing; Pyrosequencing; Archaea; Polymerase chain reaction; Genome","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004018027,0.0008919762,0.0005613717,0.000541519,0.0002555146,0.0005573799,0.0003515596,0.0006571568,0.0007143139],"category_scores_gemma":[0.000581018,0.0002863191,0.0007038775,0.0004069764,0.0003835228,0.0003945344,0.0005831923,0.0005953815,0.0007384588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002126019,"about_ca_system_score_gemma":0.0003491204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008024934,"about_ca_topic_score_gemma":0.001241446,"domain_scores_codex":[0.9993038,0.00007730773,0.0000334269,0.0002862167,0.0002067525,0.0000923492],"domain_scores_gemma":[0.9997899,0.00007430331,0.00004070941,0.00001729188,0.00005584679,0.00002184574],"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":[0.00001579744,0.000007348867,0.0003722458,0.00002704685,0.00000382036,0.00001237018,0.00001267286,0.00007416397,0.9970066,0.00002999818,0.00002583409,0.002412094],"study_design_scores_gemma":[0.000005552827,0.0001787709,0.0104472,0.00001022236,0.00003128696,0.0001483662,0.00003780498,0.005124208,0.9817378,0.00009573965,0.002163007,0.00002007905],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7797681,0.001099005,0.2134581,0.0001975118,0.00007115174,0.000215772,0.001672938,0.001188096,0.002329436],"genre_scores_gemma":[0.8204461,0.001040662,0.168976,0.0004921981,0.00004523334,0.0004199015,0.004455335,0.0002526648,0.003871982],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008919762,"threshold_uncertainty_score":0.00238955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01047079115368623,"score_gpt":0.2174074734453172,"score_spread":0.206936682291631,"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."}}