{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003230924,0.0001915189,0.0004876938,0.00007178081,0.0001323414,0.0000159329,0.0001001937,0.0001547745,0.0000284208],"category_scores_gemma":[0.00007524353,0.0001325848,0.0001308519,0.0001107057,0.0005943906,0.00001405882,0.0001612313,0.0001003597,3.356881e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007486621,"about_ca_system_score_gemma":0.00002709389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002516827,"about_ca_topic_score_gemma":0.0002140042,"domain_scores_codex":[0.9988577,0.0001294112,0.0004334167,0.0003462475,0.00003446612,0.0001987734],"domain_scores_gemma":[0.9992033,0.0002238923,0.00021903,0.0001619756,0.0001609242,0.00003085156],"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":[0.0001490615,0.0001436061,0.1153399,0.00004837755,0.0002846262,7.149892e-7,0.0002276244,0.000002738217,0.8816605,0.000003176382,0.0006329913,0.001506621],"study_design_scores_gemma":[0.0008014677,0.0001692811,0.06072148,0.00004896864,0.0003713607,0.00001333445,0.0006521218,0.00003163868,0.9366688,0.00008970223,0.000288838,0.0001430707],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838943,0.01513577,0.0004172283,0.0001300139,0.00007273466,0.0002583896,0.00008141739,0.000002794558,0.000007324874],"genre_scores_gemma":[0.9910378,0.001323758,0.007109709,0.0001439329,0.00002031988,0.00003010638,0.00008569381,0.000009700472,0.0002389368],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05500822,"threshold_uncertainty_score":0.5406653,"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."}}