Evaluating macrophyte selection and germination protocols to enhance nutrient sequestration in engineered wetland models
Bibliographic record
Abstract
Research examining contaminant sequestration using engineered wetlands has been conducted for many years but the implementation of sustainable, biodiverse strategies is still in its infancy. A major gap in knowledge still exists regarding the kinds of macrophytes to be selected, especially the inclusion of non-invasive native flora. There is a lack of information about macrophyte selection criteria and germination protocols. Thus, this study attempted to redress this dearth in knowledge. The first part of this thesis critically assessed a list of macrophytes provided by Environment Canada (1996) and created “selection criteria” for choosing specific macrophytes. Germination protocols were then compiled to determine and outline optimized germination protocols for these aquatic macrophytes. In the second part of this study, two different constructed wetlands models were designed for laboratory purposes (a “floating” constructed wetland model and a “stationary” constructed wetland model). Water samples were assed for biological impact and phosphorus concentration.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".