Impacts of Climate Change and Intensive Lesser Snow Goose (Chen caerulescens caerulescens) Activity on Surface Water in High Arctic Pond Complexes
Bibliographic record
Abstract
Rapid increases in air temperature in Arctic and subarctic regions are driving significant changes to surface waters. These changes and their impacts are not well understood in sensitive high-Arctic ecosystems. This study explores changes in surface water in the high Arctic pond complexes of western Banks Island, Northwest Territories. Landsat imagery (1985–2015) was used to detect sub-pixel trends in surface water. Comparison of higher resolution aerial photographs (1958) and satellite imagery (2014) quantified changes in the size and distribution of waterbodies. Field sampling investigated factors contributing to the observed changes. The impact of expanding lesser snow goose populations and other biotic or abiotic factors on observed changes in surface water were also investigated using an information theoretic model selection approach. Our analyses show that the pond complexes of western Banks Island lost 7.9% of the surface water that existed in 1985. Drying disproportionately impacted smaller sized waterbodies, indicating that climate is the main driver. Model selection showed that intensive occupation by lesser snow geese was associated with more extensive drying and draining of waterbodies and suggests this intensive habitat use may reduce the resilience of pond complexes to climate warming. Changes in surface water are likely altering permafrost, vegetation, and the utility of these areas for animals and local land-users, and should be investigated further.
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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 | 0.001 |
| 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 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".