Exploring runoff processes using chemical, isotopic and hydrometric data in a discontinuous permafrost catchment
Bibliographic record
Abstract
Hydrometric, isotopic and hydrochemical data were used to investigate runoff generation in a discontinuous permafrost headwater catchment. Research was undertaken between 10 April and 8 July 2008 within Granger Basin, a 7.6 km2 sub-catchment of the Wolf Creek Research Basin, Yukon Territory, Canada. The objectives of this research were to utilize hydrometric, stable isotope and hydrochemical methods to: (i) establish water balance components and (ii) couple water balance information with stable isotope and hydrochemical information to provide an enhanced understanding of runoff sources and pathways. The water balance components were snowmelt (152 mm), precipitation (68 mm), evaporation (88 mm), discharge (173 mm) and change in storage (−41 mm). The runoff ratio was high compared with previous years in this catchment. Using two-component hydrograph separation, pre-event water represented ∼73% of total discharge during freshet. End-member mixing diagrams suggested three contributing sources to streamflow in the following order: groundwater, soil water and snowmelt water. Concentration versus discharge diagrams identified the dilution of weathering ions during melt, while the ratio of potassium to calcium in streamwater suggests early contributions of pre-event water to discharge. Results from this research support previous work that pre-event water dominates freshet, yet the role of deeper groundwater is highlighted as an important contribution.
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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 | 0.000 | 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".