Compounded Impacts of Global Warming and Anthropogenic Disturbances on Snowmelt in Northern Baffin Island
Bibliographic record
Abstract
Fugitive dust arising from mining operations in the Arctic can be a concern to surrounding communities. The Mary River Mine operation on northwest Baffin Island in the Qikiqtani region, Nunavut, is one example. Yet, the short and long-term impacts of fugitive dust remain poorly understood. Dust lowers snow albedo which can contribute to early snowmelt. This influences the spring snowmelt freshet period, significant to the land-atmosphere interactions, hydrology, ecology, and socioeconomic activities in the Arctic. Here, we map dust extents indicated by snow discoloration and examine for areas of early snowmelt using a 21-year MODIS time series snow cover product in 2000–2020. We found an episode of dust plume extended far beyond the reference dust sampler sites from where Nil dustfall is detected. A snow albedo decrease of 0.014 was seen more than 60 km away from the mine site. Incidents of early snowmelt existed extensively and progressively prior to the Mary River Mine operations; however, localized and even earlier snowmelt also appear around Mine’s operations; we estimated that the snow-off date was advanced by one week and three weeks for the background, and areas around the Mine facilities, respectively, during the 21-year period. Furthermore, the area increase in early snowmelt around the Mine facilities correlates to ore production growth. This study demonstrates rapid changes in early snowmelt beyond observed regional trends when additional drivers are introduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".