Witnessing the last supper of uncultivated microbial cells with Raman-FISH
Bibliographic record
Abstract
Huang WE, Griffiths RI, Thompson IP, Bailey MJ, Whiteley AS . (2004). Raman microscopic analysis of single microbial cells. Anal Chem 76 : 4452–4458 Article CAS PubMed Google Scholar Huang WE, Stoecker K, Griffiths R, Newbold L, Daims H, Whiteley AS et al . (2007). Raman-FISH: combining stable-isotope Raman spectroscopy and fluorescence in situ hybridization for single cell analysis of identity and function. Environ Microbiol 9 : 1878–1889 Article CAS PubMed Google Scholar Lee N, Nielsen PH, Andreasen KH, Juretschko S, Nielsen JL, Schleifer KH et al . (1999). Combination of fluorescent in situ hybridization and microautoradiography – a new tool for structure-function analyses in microbial ecology. Appl Environ Microbiol 65 1289–1297 CAS PubMed PubMed Central Google Scholar Neufeld JD, Wagner M, Murrell JC . (2007). Who eats what, where and when? Isotope labelling experiments are coming of age. ISME J 1 : 103–110 Article CAS PubMed Google Scholar Ouverney CC, Fuhrman JA . (1999). Combined microautoradiography – 16S rRNA probe technique for determination of radioisotope uptake by specific microbial cell types in situ . Appl Environ Microbiol 65 : 1746–1752 CAS PubMed PubMed Central Google Scholar Podar M, Abulencia CB, Walcher M, Hutchison D, Zengler K, Garcia JA et al . (2007). Targeted access to the genomes of low-abundance organisms in complex microbial communities. Appl Environ Microbiol 73 : 3205–3214 Article CAS PubMed PubMed Central Google Scholar Radajewski S, Ineson P, Parekh NR, Murrell JC . (2000). Stable isotope probing as a tool in microbial ecology. Nature 403 : 646–649 Article CAS PubMed Google Scholar Wagner M, Horn M, Daims H . (2003). Fluorescence in situ hybridisation for the identification and characterization of prokaryotes. Curr Opin Microbiol 6 : 302–309 Article CAS PubMed Google Scholar Wallner G, Fuchs B, Spring S, Beisker W, Amann R . (1997). Flow sorting of microorganisms for molecular analysis. Appl Environ Microbiol 63 : 4223–4231 CAS PubMed PubMed Central Google Scholar Download references
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".