The Influence of Tree Infilling on Energy Partitioning, Vegetation Water Use, and Soil Water State in Sparse Conifer Stands of the Taiga Shield Ecoregion
Bibliographic record
Abstract
Abstract Climate warming and permafrost thaw induced land cover change are well documented in much of the circumpolar north. The extensive exposure of Precambrian continental crust in Canada's Taiga Shield ecoregion could mean impacts of land cover change documented in other regions without this feature are not transferable. This study examined energy partitioning with eddy covariance measurements, vegetation water use with stable isotopes and soil water state with time domain reflectometry sensors in conifer stands in the Taiga Shield ecoregion. The goal was to determine how changes in forest density with climate warming could influence water budget response and soil water state. Paired measurements of sensible and latent heat imply evaporative processes in denser canopies are controlled more by radiative than the aerodynamic factors predominant in sparser canopies. As denser canopies become more prevalent on the landscape, this switch in relative importance of evapotranspirative processes will lead to a reduction in inter‐annual variability of evapotranspiration. The dominant tree species (black spruce, tamarack and jack pine) were all quick to draw water from shallow soils after spring thaw and rainfall. Stand structural changes resulted in older, more evaporatively enriched water prevalent in soils below dense canopies. While there is evidence for forest infilling, widespread lakes and exposed bedrock restricts extensive expansion of forested land covers. The insufficient difference between sparse and dense canopy evapotranspiration suggest a fundamental change in how water cycles in Taiga Shield catchments is unlikely, which is notably different than previously investigated landscapes in Alaska and the Canadian Taiga Plains.
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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".