Characterization of contrasting flow and thermal regimes in two adjacent subarctic alpine headwaters in Northwest Canada
Bibliographic record
Abstract
Abstract Alpine headwaters in subarctic regions are particularly sensitive to climate change, yet there is little information on stream thermal regimes in these areas and how they might respond to global warming. In this paper, we characterize and compare the hydrological and thermal regimes of two subarctic headwater alpine streams within an empirical framework. The streams investigated are located within two adjacent catchments with similar geology, size, elevation and landscape, Granger Creek (GC) and Buckbrush Creek (BB), which are part of the Wolf Creek Research Basin in the Yukon Territory, Canada. Hydrometeorological and high‐resolution stream temperature data were collected throughout summer 2016. Both sites exhibited a flow regime typical of cold alpine headwater catchments influenced by frozen ground and permafrost. Comparatively, GC was characterized by a flashier response with more extreme flows, than BB. In both sites, stream temperature was highly variable and very responsive to short‐term changes in climatic conditions. On average, stream temperature in BB was slightly higher than in GC (respectively 5.8 and 5.7°C), but less variable (average difference between 75th and 25th quantiles of 1.6 and 2.0°C). Regression analysis between mean daily air and stream temperature suggested that a greater relative (to stream flow) groundwater contribution in BB could more effectively buffer atmospheric fluctuations. Heat fluxes were derived and utilized to assess their relative contribution to the energy balance. Overall, non‐advective fluxes followed a daily pattern highly correlated to short‐wave radiation. G1enerally, solar radiation and latent heat were respectively the most important heat source and sink, while air–water interface processes were major factors driving nighttime stream temperature fluctuations.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".