Characterising Runoff Generation Processes in a Lake‐Rich Thermokarst Landscape (Old Crow Flats, Yukon, Canada) using δ<sup>18</sup>O, δ<sup>2</sup>H and d‐excess Measurements
Bibliographic record
Abstract
ABSTRACT Application of novel hydrological methods for assessing runoff generation in remote northern landscapes is necessary to identify the consequences of climate variability and change. In Old Crow Flats, a lake‐rich thermokarst landscape in northern Yukon Territory (Canada), local land users have concerns over the effects of recent lake drainage and fluctuating river discharge on their traditional way of life. In the absence of hydrometric stations, we evaluate the utility of isotopic monitoring of the lower Old Crow River, which is fed by several tributaries and drains the flats, for tracking runoff generation. Isotopic ‘snapshots’ obtained from 2007, 2008 and 2009 during the recession limb of the spring freshet hydrograph provided characteristic patterns of deuterium excess (d‐excess) along the Old Crow River. River sampling in June 2007 captured a pulse of evaporatively enriched lake water originating from a rainfall‐triggered catastrophic lake drainage event, identified by decreased d‐excess values. June 2008 was marked by negligible variability in d‐excess values along the same reach of the river, consistent with minimal export of lake waters after a winter of below‐normal snow accumulation. In contrast, rising d‐excess values along the study reach in June 2009 indicate enhanced rainfall‐generated runoff. River isotope sampling could be used to monitor spatial and temporal variability in runoff generation processes in the Old Crow Flats and other northern lake‐rich landscapes drained by rivers. Copyright © 2014 John Wiley & Sons, Ltd.
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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) | 0.001 | 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".