TEMPO® TVC for the Enumeration of Aerobic Mesophilic Flora in Foods: Collaborative Study
Bibliographic record
Abstract
The automated system for enumeration of total viable count (TVC) in foods, TEMPO TVC, uses a dehydrated culture medium and an enumeration card containing 48 wells across 3 different dilutions for the automatic determination of the most probable number (MPN). The alternative method was compared in a multilaboratory collaborative study to AOAC Method 966.23 for determination of aerobic plate count for nondairy products and the Standard Methods for the Examination of Dairy Products (SMEDP) Standard Plate Count for dairy products. Five food types, raw ground beef, raw ground chicken, cooked whitefish fillets, bagged lettuce, and milk, were analyzed for TVC by 14 collaborating laboratories throughout the United States and Canada. Three lots of naturally contaminated food products representing a wide range of counts were tested for each of the 5 food types. The study demonstrated that the overall repeatability, reproducibility, and mean log counts of the TEMPO TVC method were statistically comparable to those of the 2 standard methods at the 5% level.
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 | 0.013 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".