A whole‐lake experiment confirms a small centric diatom species as an indicator of changing lake thermal structure
Bibliographic record
Abstract
Abstract In many lakes across the Northern Hemisphere, paleolimnological records have revealed that the relative abundances of the small centric diatom, Discostella stelligera, changed over the past century, with these widespread shifts attributed to climate change. Specifically, small‐scale experiments and current spatial distribution patterns suggested that this species is more abundant when lake mixing depths are shallower, but a direct test of this hypothesis at the whole‐lake scale was lacking. We conducted a whole‐lake mixing manipulation in a remote arctic lake that normally has relatively shallow thermal stratification and abundant D. stelligera populations during the summer. We employed a “Before‐After‐Control‐Impact” design using an experimental lake and a control lake. Lake thermal structure and diatom populations were monitored in both lakes in summer 2013 without manipulating either lake, and again in summer 2014 when the experimental lake was manipulated to achieve deeper mixing depths. The abundance of D. stelligera declined during the manipulated period of deeper mixing in 2014, while it increased during the same time frame in the control lake. The same pattern was not observed for the four other diatom taxa found in both lakes in both years. Our results confirm the use of D. stelligera as an indicator of changing lake thermal structure, and suggest that the broader application of this tool to lake sediment records will yield greater insight into longer‐term variability in the response of lake ecosystems to climate.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".