Comparaison des réponses de quatre espèces de saule à divers traitements d’inondation et de surfertilisation en azote
Bibliographic record
Abstract
My master's work was done within the context of the PhytoVaLix project, a research project that brings together researchers from the Université de Montréal and private companies. The goal of this project is to develop a technology for the phytotreatment of leachate from engineered landfill sites using willows to replace conventional techniques. A filtering plantation of miyabeana willow (Salix miyabeana) is currently in place on the experimental site. The willows are watered with leachate so that they use ammoniacal nitrogen, the main pollutant, for their growth. In my study, I focused on the morphological and physiological responses developed by four willow species when subjected to various treatments combining flooding and nitrogen overfertilization. The study aimed to compare the potential of these species for nitrogenous water phytoremediation as well as two leachate application methods. Responses to flooding and overfertilization with nitrogen varied and reveal much about the strategies adopted by each species. The biomass of S. miyabeana, S. amygdaloides and S. nigra was not impacted by the leachate treatments, while S. bebbiana suffered greatly from the flooding periods. The native species S. nigra and S. amygdaloides positively stood out in their ability to remove nitrogen from leachate (>75 kg/ha). It would be relevant to continue research on these two with the aim of using them in projects where leachate phytotreatment and biodiversity go hand in hand.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".