Molecular MIC Diagnoses from ATP Field Test: Streamlined Workflow from Field to 16S rRNA Gene Metagenomics Results
Bibliographic record
Abstract
Abstract Microbiologically influenced corrosion (MIC) describes the negative impacts of microbial growth within various industries. While culture based growth tests are the traditional means of assessing MIC, they are not accurate. New culture-independent assays have been developed; one of which is the adenosine triphosphate (ATP) assay. This field assay allows for indirect enumeration of microorganisms; however, it does not divulge which microorganisms are present. Molecular methods such as 16S metagenomics (16S rRNA gene sequencing and data interpretation) are often used for characterizing the microbial populations; however, it has not been widely adopted due to prohibitive costs and complex workflows that are not feasible in the field. Microbial communities are highly sensitive to changes in their environment, and change in composition as a result of sample storage, transport, and handling, ultimately diminishing the quality of the knowledge gained from sequencing the community. Recently, the presence of microbial DNA in physical elements (filter and filtrate) of the ATP assay were found, suggesting the ATP assay itself may be used to acquire DNA for metagenomic analyses in the field. The ability to bundle the ATP MIC diagnostic assay with DNA acquisition for metagenomics would reduce the cost and labor intensity of DNA extraction, and alleviate complex sample storage and handling logistics that together may substantially improve resultant molecular assay accuracy and accessibility to the industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.011 |
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".