Winter Dust Storms Impact the Physical and Biogeochemical Functioning of a Large High Arctic Lake
Bibliographic record
Abstract
We found that a winter of abnormally low snowfall and numerous dust storms from eolian processes acting on exposed landscapes (including a major 4-day dust storm while onsite in May 2014) caused a cascade of impacts on the physical, chemical, and ecological functioning of the largest lake by volume in the High Arctic (Lake Hazen; Nunavut, Canada). MODIS imagery revealed that dust deposited in snowpacks on the lake's ice acted as light-absorbing impurities (LAIs), reducing surface reflectance and increasing surface temperatures relative to normal snowpack years, causing early snowmelt and drainage of meltwaters into the lake. LAIs remaining on the ice surface melted into the ice, causing premature candling and one of the earliest ice-offs and longest ice-free seasons on record for Lake Hazen. Meltwater inputs from snowpacks resulted in dilution of dissolved, and increased concentration of particulate bound, chemical species in Lake Hazen's upper water column. Spring inputs of nutrients increased both heterotrophy and algal productivity under the surface ice following snowmelt, with a net consumption of dissolved oxygen. As climate change continues to alter High Arctic temperatures and precipitation patterns, we can expect further changes in dust storm frequency and severity with corresponding impacts for freshwater ecosystems.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".