Contrasting the ecological effects of decreasing ice cover versus accelerated glacial melt on the High Arctic's largest lake
Bibliographic record
Abstract
Lake Hazen, the High Arctic's largest lake, has received an approximately 10-fold increase in glacial meltwater since its catchment glaciers shifted from net mass gain to net mass loss in 2007 common era (CE), concurrent with recent warming. Increased glacial meltwater can alter the ecological functioning of recipient aquatic ecosystems via changes to nutrient budgets, turbidity and thermal regimes. Here, we examine a rare set of five high-resolution sediment cores collected in Lake Hazen between 1990 and 2017 CE to investigate the influence of increased glacial meltwater versus alterations to lake ice phenology on ecological change. Subfossil diatom assemblages in all cores show two major shifts over the past approximately 200 years including: (i) a proliferation of pioneering, benthic taxa at approximately 1900 CE from previously depauperate populations; and (ii) a rise in planktonic taxa beginning at approximately 1980 CE to present-day dominance. The topmost intervals from each sequentially collected core provide exact dates and demonstrate that diatom regime shifts occurred decades prior to accelerated glacial inputs. These data show that diatom assemblages in Lake Hazen are responding primarily to intrinsic lake factors linked to decreasing duration of lake ice and snow cover rather than to limnological impacts associated with increased glacial runoff.
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 | 0.000 | 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".