Analysis of the bacterial community within carrot wash water
Bibliographic record
Abstract
Vegetables are washed after harvest to remove unwanted organic and inorganic particles, but wash water contaminated with certain pathogenic microorganisms can potentially contaminate produce. In this study, the microbial diversity of wash water was analyzed in samples taken from a carrot-processing facility. A 16S rRNA gene library with 427 clones was constructed and analyzed by amplified rDNA restriction analysis. For taxonomic classification, the 16S rRNA gene nucleotide sequences of 94 amplified rDNA restriction analysis fingerprints were determined. Each fingerprint indicates a distinct group of microorganisms. The nucleotide sequences were assigned to corresponding reference species. The most prevalent genus was Tolumonas , with 26% of the clones, followed by Acinetobacter and Flacobacterium , with 11% each. The latter two genera contain species that are known to cause nosocomial infections. The fourth most common genus was Arcobacter , comprising 9% of all clones. Some species of Arcobacter are considered to be emerging food pathogens, mainly associated with the contamination of meat products. So far, they have not been considered as contaminants of fresh produce. Based on the sequence data, an Arcobacter-specific PCR assay was developed to facilitate the detection of vegetable-associated Arcobacter strains.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".