Supercharged MPNs? Automated Determination of High-Throughput Most Probable Number (htMPN) Using Chip-Based 3D Digital PCR
Bibliographic record
Abstract
htMPN uses chip-based fluorescence acquisition and is a simple and compact tool for automatic viable cell enumeration with applications in microbiological research. This method applies to a wide range of anaerobic or facultative anaerobic species and improves accuracy by reducing the number of pipetting steps. In addition, the method offers an additional tool for single-cell microbiology. The single cell time-to-detection times have been used as an important criterion for the physiological state of bacterial cells after sublethal stress, and htMPNs support the acquisition of such data with an unprecedented number of cells. In particular, htMPN provides an anaerobic environment and enables a long incubation time to increase the recovery rate of sublethally injured cells. Given its reproducibility and reliability, our approach can potentially be applied to quantify viable cells in samples from environmental, clinical, or food samples to reduce the risk of underestimation of the number of viable bacterial cells.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.003 |
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".