{"id":"W2089069271","doi":"10.1017/s1431927613013482","title":"Correlated SEM, FIB-SEM, TEM, and NanoSIMS Imaging of Microbes from the Hindgut of a Lower Termite: Methods for<i>In Situ</i>Functional and Ecological Studies of Uncultivable Microbes","year":2013,"lang":"en","type":"article","venue":"Microscopy and Microanalysis","topic":"Insect and Arachnid Ecology and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Canadian Institute for Advanced Research","funders":"Lawrence Livermore National Laboratory; U.S. Department of Energy; University of Victoria; Laboratory Directed Research and Development; RIKEN; Max-Planck-Institut für Terrestrische Mikrobiologie","keywords":"Hindgut; Bacteria; Biology; Segmented filamentous bacteria; Microorganism; Stable-isotope probing; Protist; In situ; Archaea; Scanning electron microscope; Symbiotic bacteria; Microbial ecology; Botany; Ecology; Symbiosis; Chemistry; Paleontology; Materials science; Biochemistry","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":[0.0002768932,0.0004957313,0.0002243101,0.0005353055,0.0003670753,0.0004099782,0.000527214,0.0004691341,0.0006484156],"category_scores_gemma":[0.0002922679,0.0003038154,0.0002235623,0.0002983373,0.0003858149,0.0002819771,0.000412685,0.0004695719,0.0002748428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003043665,"about_ca_system_score_gemma":0.000314082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001454961,"about_ca_topic_score_gemma":0.003545563,"domain_scores_codex":[0.9998627,0.00001781849,0.00001261485,0.00003964677,0.00004580949,0.00002142153],"domain_scores_gemma":[0.9997541,0.00005668203,0.00004250474,0.00005103454,0.00006219417,0.00003339125],"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":[0.00003173175,0.00001106918,0.0008351243,0.00002966227,0.000005744627,0.00003898557,0.00002239333,0.00007422733,0.996833,0.00007367961,0.00002346846,0.002020906],"study_design_scores_gemma":[0.000008088705,0.0001086923,0.01974045,0.00001037695,0.00001794895,0.0004665085,0.00008523888,0.005365242,0.9724205,0.0001046118,0.001659351,0.00001299634],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8759996,0.001020464,0.1181969,0.0001568207,0.00005042315,0.0001615724,0.0008071489,0.0004284386,0.003178584],"genre_scores_gemma":[0.6612275,0.000936908,0.3337952,0.0001553085,0.00002599228,0.0003221303,0.0009062673,0.0001668279,0.002463885],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001454961,"threshold_uncertainty_score":0.002892971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0122158877960342,"score_gpt":0.3005562310564959,"score_spread":0.2883403432604617,"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."}}