The relevance of Eastern Canadian native willows as alternatives to Salix miyabeana in nitrogen leachate-treating vegetative filters
Bibliographic record
Abstract
As waste from engineered landfills decomposes, it produces nitrogen (N)-laden leachate that cannot be directly released into the environment. Plants such as willows have the potential to phytofilter this polluted water but must be able to tolerate large loads of contaminants and flooding conditions. To date, however, it is mainly exotic species that have been used in for the treatment of leachates in a pilot project conducted on a technical landfill. It would be useful to compare the effectiveness of native species from eastern Canada with that of the Salix miyabeana ‘SX64’, a willow cultivar used in the pilot project. Three willows indigenous to Canada: S. amygdaloides, S. bebbiana and S. nigra, were tested alongside S. miyabeana. A mesocosm experiment was conducted under semi-controlled conditions over six weeks to document the impact of various nitrogen overfertilization and flooding treatments on plant development, and to test the plants’ tolerance to these constraints to evaluate their suitability for large-scale vegetative filters. Overall, growth and biomass production of S. nigra and S. amygdaloides were not affected by the treatments. Furthermore, S. nigra was ten times more efficient than the cultivar of S. miyabeana in terms of decontamination capacity. While still in the juvenile phase, S. nigra plants removed the equivalent of 240m3 per hectare of the N-contaminated water initially applied (60 kg of N), under both permanent and cyclic flooding. These results suggest that native species could be given greater in future projects.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".