Arctic warming drives striking 21st century ecosystem shifts in Great Slave Lake (Subarctic Canada), North America’s deepest lake
Bibliographic record
Abstract
Great Slave Lake, one of the world’s largest and North America’s deepest lake, has undergone an aquatic ecosystem transformation in response to 21st-century accelerated Arctic warming that is unparalleled in at least the past two centuries. Algal remains from a series of high-resolution palaeolimnological records retrieved from the West Basin provide baseline limnological data that we compared to historical limnological and phycological surveys undertaken on Great Slave Lake between the 1940s and 1990s. We document the rapid restructuring of algal community composition ca. 2000 CE that is consistent with recent increases in regional air temperature, as well as declines in ice cover and wind speed, that would collectively alter habitats for aquatic biota (e.g. thermal regime, vertical mixing, turbidity, light and nutrients). This new limnological regime initiated the first observation of scaled chrysophytes and favoured the rapid proliferation of small planktonic cyclotelloid diatoms that replaced the long-established dominance of large filamentous Aulacoseira islandica in West Basin sedimentary assemblages. Such rapid transformations in the primary producers of this socio-ecologically valuable “northern Great Lake” may have widespread implications for the entire food web with unknown consequences for aquatic ecosystem functioning and fisheries, which many northern and Indigenous communities depend upon.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".