Effects of long‐term sample storage on the detection of bacterial cells using fluorescence in situ hybridization
Bibliographic record
Abstract
Fluorescence in situ hybridization (FISH) is widely used to characterize bacterial community structure. However, a major limitation of the FISH technique is that the effectiveness of target cell detection varies widely over ecosystem types and with differences in methodology. Samples collected at sea often are stored for weeks or months before analysis using FISH, therefore quantifying the effects of storage on the detection of bacterial cells is crucial for comparing studies of bacterial community structure from diverse regions and ecosystems. Presented are the results of a 12‐month time‐course study during which replicate seawater samples were prepared, stored frozen, and hybridized after 0, 1.5, 3, 6, and 12 months to determine the effects of long‐term sample storage on hybridization efficiency and the characterization of community structure. The time‐dependent slope of the probe for Bacteria, but not the Cytophaga‐Flavobacteria cluster or the α‐ and γ‐Proteobacteria, was significantly different from zero, with a 6.3% change in target cell detection per year. This change in detection was small and within the typical error reported for bacterial counting. We conclude that during this 12‐month time‐course study there was a minimal effect of long‐term storage on the detection of bacterial cells using FISH.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".