Climatically controlled chemical and biological development in Arctic lakes
Bibliographic record
Abstract
We investigated the factors controlling lake evolution in Arctic ecosystems using a multiproxy paleolimnological approach on a small lake on Baffin Island, Arctic Canada. Lakewater pH was inferred from fossil diatom assemblages, whereas primary production was assessed from sediment concentrations of diatom valves and spectrally inferred chlorophyll a. Our reconstructed limnological variables registered synchronous changes and showed a close coupling to Holocene climatic fluctuations, as inferred by numerous independent paleoclimate proxies. Without exception, our highest pH and production values occurred during warm intervals, and vice‐versa. A return towards paleolimnological conditions of the warm early Holocene has occurred since the midtwentieth century, corresponding to climate warming following the Little Ice Age. Maximum recent values of our reconstructed parameters are either directly comparable to, or in some cases exceed, values attained during the Holocene Thermal Maximum, 8000–10,000 years ago. Our data suggest that climate has a first‐order influence on primary production and the regulation of in‐lake DIC dynamics (and hence on lakewater pH) through its modulation of lake ice cover. We conclude that direct forcing by climate is more important than catchment processes in controlling the chemical and biological development of ice‐dominated Arctic lake ecosystems, at the scale of the Holocene.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".