Thermal and isotopic evidence for surface and subsurface water contributions to baseflow in a high Arctic river
Bibliographic record
Abstract
Abstract Seven longitudinal water temperature tow surveys were conducted to attempt to identify the location of surface and subsurface river water exchanges along the length of the West River at the Cape Bounty Arctic Watershed Observatory, Melville Island, Nunavut, Canada (74°55′ N, 109°35′ W). Water temperature data were collected using a calibrated thermistor with an accuracy of ±0.002 °C (resolution <0.00005 °C) along the river during July 2014 in conjunction with stable water isotope sampling to support the thermal data and to determine the extent of surface water mixing from different sources such as precipitation, snowmelt, and surface/subsurface water contributions to the river. Atmospheric conditions were found to be the main contributor to seasonal temperature variance in the river, whereas tributary inflows and residual channel snow also had important thermal effects to river temperatures. Residual channel snow was a sustained source of cold water during much of the 2014 summer season (June–August) and substantially offset downstream warming. The longitudinal temperature profiles indicate notable changes to the thermal state of the river, which are interpreted to be indicative of subsurface and surface water exchange through inputs of relatively cold or warm water. Broadly, surface inflows were found to provide warmer water relative to the West River, and contributed to downstream warming of the river, along with downstream enrichment of δD and δ18O. Subsurface inflows provided cooler water relative to the river, and contributed to downstream depletion of δD and δ18O and downstream cooling of river temperatures. These results demonstrate that localized changes in river temperature, in conjunction with isotopic tracers, can be used to track channel–slope water interactions in Arctic hydrological systems, work previously limited to alpine and temperate settings.
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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 | 0.001 | 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".