Examining relations among hydrology, carbon, and important catchment characteristics in lakes and rivers of Old Crow Flats, Yukon
Bibliographic record
Abstract
Northern ice- and lake-rich permafrost regions are experiencing changing climate conditions, such as increased precipitation, that have led to various landscape changes (e.g., enhanced hydrological connectivity, catastrophic lake drainage, increased shrub vegetation). These landscape disturbances may alter the biogeochemical cycling of lakes and rivers, especially carbon cycling. Many uncertainties remain regarding how further climate-driven landscape changes will influence the mobilization and cycling of carbon to downstream environments. Old Crow Flats (OCF), Yukon, is a 14,500-km2 watershed with over 8700 thermokarst lakes and ponds that is the traditional territory of the Vuntut Gwitchin First Nation. Both the Vuntut Gwitchin First Nation and researchers have observed landscape changes leading to concerns about how these changes will impact the lake ecosystems and downstream environments. Analysis of dissolved organic and inorganic carbon concentrations and stable carbon isotopes of the 14 long-term monitoring lakes and 25 river sampling locations showed spatial variability in the concentrations and potential sources of carbon in OCF based on lake catchment characteristics and hydrological connectivity. Results presented here act as a baseline of how dissolved carbon concentrations in the lakes and rivers have responded to changing climate conditions over the past decade and identify the potential sources of carbon in the Old Crow Flats drainage network. This research highlights the complexity of carbon cycling and the need to maintain long-term monitoring of relations between climate, landscape characteristics, and surface water across sensitive permafrost regions.
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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 | 0.000 | 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".