An Assessment of Variability of Pulp Mill Wastewater Treatment System Bacterial Communities using Molecular Methods
Bibliographic record
Abstract
Abstract The DNA fingerprinting techniques, 16S-restriction fragment length polymorphism (16S-RFLP), ribosomal intergenic spacer analysis (RISA) and repetitive extragenic palindrome PCR (Rep-PCR), were used for analyzing the bacterial communities of seven pulp and paper wastewater treatment systems. All three methods generate DNA fingerprints that can be compared using the computer-assisted program, Gelcompar©. Community similarity coefficients were based on quantitative determinations of both the positions of the DNA bands and the band intensities in order to compare the relative differences in the populations. Unique 16S-RFLP DNA fingerprints were observed for each mill suggesting that individual mills contained phylogenetically different communities. However this method was not sensitive enough to detect differences within a mill treatment system from different locations or from different sampling times. The RISA method, which generated more complex fingerprints than 16S-RFLP, could, for some mills, discern differences between samples. The Rep-PCR technique, however, showed the highest degree of resolution and produced not only distinct patterns for each mill but also distinct fingerprints for the temporal and spatial samples from some of the treatment systems. The sensitivity of this method might potentially be used to monitor the stability of the bacterial community within a secondary treatment system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 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".