Consequences of the senescence growth phase on the performance of treed bioretention cells
Bibliographic record
Abstract
Bioretention cells vegetated with trees were studied to understand the changes in performance for mitigating stormwater quantities and qualities during the senescence period. Stormwater runoff simulations were conducted in a temperate climate for 1.1-, 1.5- and 2-year return period storm events applied to field-based treed bioretention cells planted with a mix of Betula nigra , Betula nana , and Salix lutea trees, and grassed bioretention cells planted with turf grass. Eighteen separate storm events (six for each return period) were applied at various times during the 2020 summer growth period starting with the onset of senescence in late August to early September through to abscission in early October. Changes in water quality and quantity performance were analyzed over the senescence period for several parameters including water volume retention, chemical oxygen demand, total nitrogen (TN), total organic nitrogen (TON), total phosphorus (TP), orthophosphate, and total suspended solids (TSS) using non-parametric statistical tests. These were supported with additional analysis of daily evapotranspiration (ET) and antecedent moisture content (AMC). Correlations with the timing of senescence on the treed bioretention cell's performance were visible and significant (α = 0.05) over the testing period for water retention, TP, TN and orthophosphate. However, these results were not observed, or significant, for the grassed cell. The analysis showed that the treed cell's contribution to contaminant removal is highly correlated with changes in ET and AMC; whereas the grassed cell showed changes correlated only to AMC. This work demonstrates that the senescence period will lead to diminished water quantity retention and changes in nutrient retention and other stormwater contaminants from bioretention cells vegetated with trees.
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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 | 0.000 | 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".