An automated bacterial concentration and recovery system for pre-enrichment required in rapid Escherichia coli detection
Bibliographic record
Abstract
One of the biggest challenges in rapid low concentration bacterial detection is the pre-concentration or pre-enrichment, which aims to increase bacteria concentration and reduce sample volume for easy bacterial detection. In practical bacterial detection, large-volume water samples with a pathogenic bacterial concentration of less than 1 CFU/mL have to be tested rapidly. The reported biosensors either have insufficient detection limit or have limited capability of handling a sufficiently large water sample. Therefore, a high-performance automated pre-enrichment process is strongly demanded in rapid practical bacterial detection. In this paper, a practical high performance automated bacterial concentration and recovery system (ABCRS) based on the combination of a ceramic membrane and tangential flow filtration technique was presented with short processing time (less than one hour), low pre-enrichment limit (≤0.005 CFU/mL), high concentration ratio (≥ 500), high recovery efficiency (~ 90%), and small final retentate volume (≤ 5 mL).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".