Assessing the Use of Poplar Tree Systems as a Landfill Evapotranspiration Barrier with the SHAW Model
Bibliographic record
Abstract
The use of poplar tree systems (PTS) as evapotranspiration barriers on decommissioned landfills is gaining attention as an option for leachate management. This study involved field-testing the Simultaneous Heat and Water (SHAW) model for its ability to reliably estimate poplar transpiration, volumetric soil water content, and soil temperature at a landfill located in southern Ontario, Canada. The model was then used to estimate deep drainage and to ascertain the influence of a young PTS on the soil water balance of the landfill cover. The SHAW model tended to underestimate poplar transpiration [mean difference (MD) ranged from 0.33 to 3.55 mm on a daily total basis] and overestimate volumetric soil water content by up to 0.10 m3 m(-3). The model estimated soil temperature very well, particularly in the upper 1 m of the landfill cover (MD ranged from -0.1 to 1.6 x degrees C in this layer). The SHAW model simulations showed that deep drainage decreased appreciably with the presence of a young PTS largely through increased interception of rainfall, and that PTS have a good potential to act as effective evapotranspiration barriers in northern temperate climate zones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 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".