Zero‐Flow Dynamics for Headwater Streams in a Humid Forested Landscape
Bibliographic record
Abstract
ABSTRACT Much of our understanding on temporary headwater streams is from arid and sub‐humid environments. We know less about zero‐flow periods in humid headwater catchments that experience seasonal snow cover. Our study characterised the temporal and spatial patterns of zero‐flow periods for forested headwater streams in a snow‐dominated landscape. We used 36 years of streamflow data from 13 headwater catchments within the Turkey Lakes Watershed located on the Canadian Shield in Ontario, Canada, near the eastern shores of Lake Superior. These headwater catchments differ substantially in their number of May–November zero‐flow days (0–166 days per year) despite being clustered in a small geographical area with similar geology, physiography and vegetation cover. The catchments also experience similar continental climatic conditions with relatively even precipitation inputs throughout the year (mean annual precipitation of 1210 mm/year). Inter‐annual variability in the number of zero‐flow days was primarily associated with May–November precipitation and evapotranspiration. Despite the large seasonal snowpacks that form in this region, the amount of snow did not appear to influence the extent of zero‐flow periods. We found that between‐catchment variability in zero‐flow occurrences was related to differences in catchment area and catchment properties typically associated with greater groundwater influence. Our study suggests that occurrences of zero‐flows in headwater streams can be highly variable even over small geographical regions and that flow permanence may be more sensitive to spring to fall weather conditions than the influence of snow due partly to the shallow soils typically found on the Canadian Shield.
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 | 0.001 |
| 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) | 0.001 | 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".