The Impact of Birch Seedlings on Evapotranspiration From a Mined Peatland: An Experimental Study in Southern Quebec, Canada
Bibliographic record
Abstract
Dense stands of birch ( Betula spp.) on abandoned peat workings have often been identified as potential barriers to site restoration, but little research has been conducted to evaluate their impact on water resources. The objective of this experimental study was to determine whether birch seedlings established on an abandoned mined peatland in eastern Canada had a significant impact on evapotranspiration. Transpiration rates from birch seedlings planted in containers filled with Sphagnum compost were measured gravimetrically. Unplanted containers were used to similarly measure evaporation rates from bare peat. On average, the measured rates of evaporation (per unit area) from peat were 2.5 times the rates of transpiration from birch leaves. However, if the total leaf area of a dense birch population established on an abandoned mined peatland is considered, the total amount of water lost through birch transpiration could be higher than that lost by evaporation from the peat surface. This study provides a rough estimate of potential water losses due to birch seedling transpiration, and indicates that a dense population of birch on a mined peatland may influence site hydrology even at the early establishment phase (seedlings). Consequently, recently abandoned mined peatlands should be restored rapidly to prevent the establishment of birch 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".