Characterizing Hydro-Limnological Relationships in the Shallow Thermokarst Lakes of the Old Crow Flats, Yukon Territory
Bibliographic record
Abstract
The vulnerability of arctic wetlands to a changing climate is an increasing concern of northern Aboriginal communities, parks managers, and conservation authorities because these lake-rich areas provide food and habitat for a myriad of wildlife populations. For more than a decade, researchers have recognized that both the hydrology and the ecology of lakes and ponds within regions of permafrost will be altered significantly by climatic warming (Rouse et al., 1997). Shallow thermokarst lakes, formed in lowland regions by thawing of ice-rich permafrost, are particularly susceptible to hydro-climatic change because they lack sufficient storage and depend predominantly on seasonal input waters to offset evaporation (Woo and Guan, 2006). Annual precipitation is especially important in thermokarst lake water budgets because snowmelt in spring and rainfall in the ice-free season replenish water supplies and sustain lake water levels (Boike et al., 2008; Plug et al., 2008). In northwestern Canada, research in regions of continuous permafrost has shown that thermokarst lakes experience net water losses from evaporation in warm, dry years and net water gains in cool, wet years (Labrecque et al., 2009). Changes to either the timing and quantity of snowmelt or the frequency and amount of rainfall will likely cause major shifts in lake hydrology throughout the Arctic. It is also likely that such shifts in hydrology will initiate changes in lake water chemistry and biota. However, the specific relationships between hydrological processes and limnological characteristics in Arctic freshwaters are generally underinvestigated and remain poorly understood. Integrated hydro-ecological evaluations of thermokarst lake responses to seasonal variations in hydrology (i.e., snowmelt, rainfall, and evaporation) are needed in order to better understand the overall effects of a changing hydrology on Arctic freshwater ecosystems. This knowledge will help park managers, Aboriginal governments, and conservation authorities develop the predictive capacity to anticipate and monitor future climate-driven changes in Arctic wetlands. The primary objective of my research is to investigate linkages among climate, hydrology, and limnology in thermokarst lakes of Old Crow Flats, a dynamic lake-rich landscape in northern Yukon Territory designated by the Ramsar Convention as a Wetland of International Importance (Fig. 1). Old Crow Flats is the traditional territory of the Vuntut Gwitchin First Nation (VGFN) and is one of northwestern Canada’s largest wetlands within a region of continuous permafrost. Over the past few decades, the Vuntut Gwitchin have witnessed unprecedented changes in Characterizing Hydro-Limnological Relationships in the Shallow Thermokarst
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".