Effect of Soil Type and Vegetation on the Performance of Evapotranspirative Landfill Biocovers: Field Investigations and Water Balance Modeling
Bibliographic record
Abstract
The water balance performance of evapotranspirative landfill biocovers (ET-LBCs) under Canadian cold climate conditions is evaluated by constructing seven lysimeters at a field site in Alberta, Canada, and monitoring water balance for 1 year. Two granular media types (topsoil and compost mixture) and three types of vegetation (native grass species, alfalfa, and Japanese millet) were used. The results suggested that as plants became established, the average percolation as a percentage of water input [percolation ratio (PR)] decreased in all lysimeters. Between the lysimeters subjected to rainfall simulation events, the lysimeters with Japanese millet transmitted the lowest amount of percolation (10%–17%), followed by native grass species (23%–28%), and alfalfa (25%). Under the same vegetation coverage, lysimeters with a compost mixture transmitted lower or equal percolation compared with lysimeters with topsoil. Water balance predictions made using Hydrus-1D and commercial model SEEP/W were compared with water balance data from lysimeters over the growing season. The predictive capabilities of the models decreased under high intensity rainfall events and with the occurrence of preferential pathways associated with plant roots.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".