Laboratory studies on ochre formation and removal from geotextile filters
Bibliographic record
Abstract
The clogging of drainage systems due to the formation of ochre is considered a major threat to the performance of filters and drainage systems. Aiming to understand the factors leading to clogging, column tests were conducted using geotextile filters under three different filter submersion conditions and ferric citrate percolation to stimulate ochre production. After 76 days, in half of the column tests, the ferric citrate was changed to d-glucose in the percolation fluid to remove the ochre by the reductive dissolution of iron. The concentrations of dissolved oxygen (DO), Fe(II), Fe(III), and pH were monitored as a function of time. Ochre-clogged geotextile filters and the effects of reductive dissolution were observed by scanning electron microscopy coupled to energy-dispersive spectroscopy. The results indicated that ochre formation decreased substantially in the submerged filters due to the lower availability of oxygen for microbial aerobic activities. Percolation with d-glucose led to low DO content, reduced pH in the percolation fluid, and stimulation of a pre-existing population of iron-reducing bacteria, which reduced the Fe(III) to soluble Fe(II), reversing clogging in geotextile filters. This fundamental research may indicate a path for a new procedure for mitigation and removal of ochre of drainage systems.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".