Investigation of Evapotranspiration in a Bioretention System through Soil Moisture Content
Bibliographic record
Abstract
Bioretention systems control stormwater runoff through infiltration, groundwater recharge, and water loss via evapotranspiration (ET) processes. The significance of ET as a volume reduction method has been limited in research. This study at a University of Calgary research facility in Okotoks, Alberta, assessed ET in the “40” soil media bioretention system. Soil moisture content sensors installed at 20cm and 40cm depths provided ET estimates. Seasonality impacted ET with the highest estimations occurring in July 2018 and the lowest in September 2018. 20cm ET estimations were generally higher than 40cm ET estimations due to shallow vegetation root systems. ANOVA tests showed woody, herbaceous, and turf grass vegetation types were not significant at 20cm on ET while woody and turf grass vegetations were significant at 40cm. The Hargreaves and Penman-Monteith equations do provide suitable upper and lower limits of ET estimation at 20cm. ET, at 20cm, reduced over 100% of water volumes and was capable of reducing antecedent moisture content in smaller storm events; 26 – 60% of water volumes were reduced in large storm events. At 40cm, ET reduced between 21 – 67% and over 100% of water volumes and was capable of slightly reducing antecedent moisture content in smaller events; 0-50% of water volumes were reduced in large storm events.
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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) | 0.001 | 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".