A Comparison of the Microbial Populations in a Culture-Dependent and a Culture-Independent Analysis of Industrial Water Samples
Bibliographic record
Abstract
Culture-dependent and culture-independent microbiological methods are two approaches used to study microbial community composition. Culture-dependent methods have been the standard method used for many years but have limited utility with unculturable microorganisms. Culture-independent methods, including molecular techniques, enable direct analysis of microbial DNA without requiring cultivation. Both culture-dependent and -independent methods have roles in advancing our understanding of microbiology, and a combination of these approaches often yields a comprehensive depiction of the microbial diversity within a dynamic system. Bacterial activity reaction tests (BARTs) are a common culture-dependent test used to identify bacteria growing in industrial water samples. In this study, next-generation sequencing (NGS) was used to identify the taxa growing in BARTs and compared with the BART reaction patterns. Additionally, several water samples were analyzed by both BART and NGS analysis to determine whether the bacteria found in the water were also present in the BARTs. The results showed overall agreement between NGS and BARTs, though, in some cases, the most abundant taxa found in the water samples differed from those in the BARTs. This highlights the need for further study into the microbial community dynamics of culture-dependent tests to determine whether they are representative of the original sample.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".