From Arctic ponds to the “Northern Great Lakes”: Algae as first responders of climate‐driven regime shifts
Bibliographic record
Abstract
Arctic freshwater ecosystems are on the "frontline" of climate change, but due to a lack of direct long-term monitoring data, indirect approaches, such as algal-based paleolimnology, must be used to reconstruct past limnological conditions. Our understanding of the responses of small- to mid-sized Arctic lakes to climate warming has increased over the last ~30 years. However, until recently, little was known about even the basic limnological conditions of Canada's "Northern Great Lakes," such as Lake Hazen, Great Bear Lake, and Great Slave Lake. In this summary, we show that a continuum of algal changes, observable in the sedimentary archives of shallow ponds to very large Arctic lakes, signals the crossing of key aquatic thresholds linked to changing ice covers and thermal regimes, declining wind speeds, and other climate-related variables. With recent accelerated warming, even the largest and most resilient Arctic waterbodies are now fundamentally different than they were just a few decades ago. These changes will undoubtedly cascade throughout the food web leading to important changes for local Indigenous populations as well as the global community.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".