Treatment of log yard runoff with a continuous fixed film bioreactor
Bibliographic record
Abstract
Runoff is generated at log yards when precipitation comes into contact with logs, wood debris, and equipment at outdoor wood processing, sorting and storage facilities. Log yard runoff, which can be toxic and have high levels of biochemical oxygen demand (BOD), chemical oxygen demand (COD), and tannin and lignin (T&L), is a potential threat to nearby receiving environments. Runoff samples were collected from two sawmills located in British Columbia (BC). The runoff samples collected had BOD ranging from 16 to 371 mg/L, COD from 230 to 2660 mg/L, and tannin and lignin from 200 to 680 mg/L of tannic acid. Four runoff samples were acutely toxic according to the Microtox® toxicity test. A continuous lab-scale fixed film bioreactor was used to treat the runoff over a range of temperature (5–30 °C) and hydraulic retention times (4–24 h). The reactor was capable of treating runoff with BOD removal ranging from 73.0% to 97.6%, COD removal ranging from 48.0% to 76.9%, and tannin and lignin removal ranging from 27.8% to 60.1%. There appeared to be a transitional change in the makeup of the microbial community in the reactor as the temperature decreased from 15 °C to 10 °C. The reactor was able to remove all acute toxicity at 30 °C, but at lower temperatures treated runoff remained acutely toxic.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".