Comparison of Planktonic and Sessile Bacteria Counts Using ATP and DNA Based Methods
Bibliographic record
Abstract
Abstract Microbiologically influenced corrosion (MIC) is a term used to describe corrosive damage to metals caused by microbes. Corrosion damage is costly to operations, the environment and life. As such, monitoring for, and diagnosing MIC as part of a complete corrosion mitigation strategy is of paramount importance. Traditional MIC diagnostic techniques employ culture-based methods aimed at enumerating microbes presumed to be associated with MIC. Culture-based diagnostics are time consuming and may severely under estimate populations. Moreover, microbes mediating corrosion typically exhibit a sessile lifestyle, yet traditional culture techniques are designed to enumerate planktonic, free floating microbes, thus leading to a disconnect between the assay and reality. As such, culture-independent and genetics-based assays have been developed to allow for the enumeration and identification of microbes associated with MIC. These tests range from field tests such as ATP and Hydrogenase assays to complex lab tests such as fluorescence microscopy and polymerase chain reaction (PCR) based assays. The majority of corrosion is mediated by sessile microbes residing in biofilm on surfaces and underneath corrosion deposits. However, despite the variety of highly accurate testing methods, the data collected is still not representative; planktonic sampling is still far more common than sessile. As planktonic and sessile counts vary widely, only sampling the planktonic population can lead to underestimates of microbial abundance and subsequent mitigation plans may not be designed appropriately. This paper compares planktonic and sessile counts using a variety of testing methods (including culture media, ATP assay and nucleic acid analysis). Data will be presented from a series of case studies including both lab work and field assessments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".