Factors influencing the detection of bacterial cells using fluorescence in situ hybridization (FISH): A quantitative review of published reports
Bibliographic record
Abstract
Abstract Fluorescence in situ hybridization (FISH) is widely used to describe bacterial community composition and, to a lesser extent, to describe the physiological state of cells. One of the limitations of the technique is that the effectiveness of the detection of target cells appears to vary widely. Here, we present a quantitative review of published reports on the percentage of cells detected using the common EUB338 probe (%Eub) in aquatic ecosystems. The %Eub varies from 1 to 100% in the different published reports, with an average of 56%. There is a methodological component in this variation, with a significant effect of the fluorochrome type and the stringency conditions of the reaction. But there is also a strong environmental component, and the type of ecosystem and dominant phylogenetic group significantly influence %Eub. We argue that the optimization of the FISH protocol to describe the phylogenetic composition of bacterial assemblages will probably lead to techniques that are not effective to describe the physiological state of cells.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".