Analysis of growing season carbon and water fluxes of a subalpine wetland in the Canadian Rocky Mountains: implications of shade on ecosystem water use efficiency
Bibliographic record
Abstract
Mountain regions are an important regulator in the global water cycle through their disproportionate water contribution. Often referred to as the “Water Towers of the World”, mountains contribute 40 to 60% of the world’s annual surface flow. Shade is a common feature in mountains, where complex terrain cycles land surfaces in and out of shadows over daily and seasonal scales. This study investigated turbulent water and carbon dioxide fluxes over the snow-free period in a subalpine wetland in the Canadian Rocky Mountains, from June 7th to September 10th, 2018. Shading had a significant and substantial effect on water and carbon fluxes at our site. Each hourly increase of shade per day reduced evapotranspiration (ET) and gross primary production (GPP) by 0.42 mm and 0.77 gCm-2, equivalent to 17% and 15% per day, respectively, over the entire study period. However, during only peak growing season, when leaves were fully out and mature, shade caused by the local complex terrain, reduced ET and increased GPP, likely due to enhanced diffuse radiation. The overall result was increased water use efficiency at the site during periods of increased shading during the peak growing season. In addition to incoming solar radiation (Rg), temporal variability in ET was found to relate to temporal variability in soil temperature, moisture and vapour pressure deficit. Shade impacted the curvature and intercept of the nonlinear ET-Rg relationship at our site. In contrast, temporal variability in GPP at our site was dependent largely on Rg only. Our findings suggest that shaded subalpine wetlands can store large volumes of water for late season runoff and are productive through short growing seasons.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".