The relationship between microbial population ATP and quantitative PCR bioburdens in diesel fuel microcosms
Bibliographic record
Abstract
Historically, fuel microbiology studies have relied on culture data. Potentially relevant but unculturable bacteria were not detected. Although ATP can quantify total microbial bioburdens in fuels, it cannot differentiate among the taxa present. Quantitative PCR (qPCR) testing promises to fill this gap by quantifying targeted amplicon sequences thereby detecting both culturable and non-culturable taxa and quantifying specifically targeted taxa. In this study, fluid samples drawn from the fuel, interface and water phases of fuel over water microcosms were tested for cellular ATP concentration ([cATP]) and qPCR bioburdens. Additionally, surface swab samples from steel corrosion coupon surfaces exposed to each of these three phases were collected and tested for total ATP concentration ([tATP]) and qPCR bioburdens. Statistical relationships between ATP and qPCR bioburdens were examined. Correlation coefficients between the two variables were matrix dependent and ranged from negligible (| r |=0.2) to strong (| r |=0.7). When results were categorized into negligible, moderate and heavy bioburdens, parameter agreement was again matrix dependent. Percentage agreement between [ATP] and qPCR gene copies ranged from 11 % to 89 % – with qPCR-bioburden ratings typically being greater than ATP-bioburden ratings.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".