Estimates of bacterial production using the leucine incorporation method are influenced by differences in protein retention of microcentrifuge tubes
Bibliographic record
Abstract
The most widely used methods to determine bacterial production involve measuring the incorporation of radioactive precursors, such as leucine, into macromolecular pools. The leucine method that involves incubation and extraction within a single microcentrifuge tube has become a widely used technique because of its relative convenience, precision, and low cost. We observed a discrepancy in parallel determinations of leucine incorporation for the same water samples that lead us to explore aspects of the method including tube‐washing methods, operator differences, and differences among tube brands. Operators and washing methods had minimal effects on rate measurements, but results were strongly dependent on tube brands. Differences in tube performance were observed consistently in comparisons from a variety of freshwater and marine environments. Microcentrifuge tubes differed in protein retention with the consequence that estimates of leucine incorporation in a given sample could vary by as much as 60% depending on the tube used. There was no simple relationship between tube plastics or manufacturer and tube performance. We advise researchers to check the protein retention of tubes and to use the same brand of tube during field studies to minimize this potential source of variation.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".