Characterization of commercially formulated microbial products using molecular & biochemical methods of analysis
Bibliographic record
Abstract
Commercially formulated products containing bacteria cultures as their active ingredient, referred to as "Bioproducts", are available to the public throughout Canada for various domestic applications. Bioproducts are difficult for governments to regulate as manufacturers often do not provide sufficient information of their composition. This study attempted to provide regulators with useful research tools to better screen uncharacterized bioproducts by examining Biotize and Cycle. Both are liquid formulations used as biological amendments for aquariums. Investigations used culture-based methods with fatty acid methyl ester (FAME) analysis, denaturing gradient gel electrophoresis (DGGE), real-time polymerase chain reaction (PCR) and DNA-microarray analysis, to characterize microbial composition. All bacteria indicated on the manufacturer's label were identified for Biotize, but inappropriate culture conditions failed to isolate de-nitrifying bacteria in Cycle. Both bioproducts contained bacterial species not listed on the manufacturers' label. For Cycle these additional bacterial specks included three 'Mycobacterium spp'. and ' Pseudomonas aeruginosa', all of which are opportunistic pathogens. Single antibiotic and multidrug resistance were observed in both bioproducts. Real-time PCR with SYBR Green failed to detect 'Campylobacter jejuni, Escherichia coli' O157:H7, 'Listeria monocytogenes ' or 'Salmonella enterica' in either bioproduct.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".