Impact of de-icing salt runoff in spring on bioretention efficiency
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract We investigated the effect of de-icing salt in stormwater runoff on bioretention system hydrology and filtration of contaminants. Salt runoffs during the snow melt period were simulated in 20 mesocosms planted with 1 of 3 plant species (Cornus sericea, Juncus effusus and Iris versicolor) or left unplanted, and then watered with semi-synthetic stormwater runoffs supplemented with 4 NaCl concentrations (0, 250, 1,000 or 4,000 mg Cl/L). All bioretention mesocosms, irrespective of treatment, were efficient in reducing water volume, flow and pollution level. There was no phytotoxic effect of NaCl on plants, even at the highest NaCl concentration tested. Water volume reduction and flow rate were influenced by plant species, but salt concentration had no effect. Salt runoffs significantly increased the removal of some metals, such as Cr, Ni, Pb and Zn, but had no effect on nutrient removal. Because snowmelt laden with de-icing salt is of short duration and occurs during plant dormancy, plants in bioretention may be less affected by de-icing salt than previously thought, provided that salinity decreases rapidly to normal levels in the soil water. The long-term effects of de-icing salt and general performance of bioretention should be further studied under full-scale conditions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it