{"id":"W3195484595","doi":"10.1099/mgen.0.000625","title":"Shining light on a deep-sea bacterial symbiont population structure with CRISPR","year":2021,"lang":"en","type":"article","venue":"Microbial Genomics","topic":"Microbial Community Ecology and Physiology","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Natural Environment Research Council; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Sight Research UK; Compute Canada","keywords":"Biology; CRISPR; Population; Evolutionary biology; Genetic diversity; Phylogenetic tree; Locus (genetics); Symbiotic bacteria; Genetics; Ecology; Gene; Bacteria; Symbiosis","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007740755,0.0001727961,0.0002016579,0.00002258606,0.0003186272,0.00004122309,0.0002242683,0.0001800031,0.006991624],"category_scores_gemma":[0.00001610234,0.0001610325,0.00004494335,0.0001164485,0.00007498445,0.00008190644,0.0002261553,0.0003099162,0.0002440866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000227165,"about_ca_system_score_gemma":0.00002776674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002822994,"about_ca_topic_score_gemma":0.01244517,"domain_scores_codex":[0.9989854,0.0001812933,0.0001830227,0.0003138498,0.00005416955,0.0002823314],"domain_scores_gemma":[0.9994664,0.00002992787,0.00008707832,0.0003429792,0.00001170556,0.00006186157],"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":[0.000252199,0.00006664346,0.004174817,0.000005201083,0.00002096788,0.00001631001,0.000628253,0.001969033,0.9897468,0.0001097102,0.002422584,0.0005874906],"study_design_scores_gemma":[0.003338254,0.0009629045,0.2495213,0.00006130304,0.0001400261,0.0004219034,0.0003203304,0.0002944093,0.5763118,0.001829276,0.1652299,0.001568578],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9978208,0.00001076224,0.00007913335,0.0002566406,0.0004263797,0.0001284124,0.00004791148,0.00002674075,0.00120321],"genre_scores_gemma":[0.9960497,0.000007192769,0.001997089,0.001020569,0.0001607281,0.000001924515,0.0005814224,0.00002180525,0.0001595657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.413435,"threshold_uncertainty_score":0.9939161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005480703860007935,"score_gpt":0.1945221730154995,"score_spread":0.1890414691554916,"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."}}