Changes in mixing depth reduce phytoplankton biomass in an Arctic lake: Results from a whole-lake experiment
Bibliographic record
Abstract
Research has revealed contradictory responses of primary producers in Arctic lakes to increasing temperatures, making it unclear how future warming and climate change will alter lake productivity. We conducted a whole-lake manipulation to examine the effect of altered thermal structure on phytoplankton biomass in a lake in Greenland, one of the most rapidly warming regions of the world. Deepened lake mixing (from 4 to 8 m) in the experimental lake led to significant declines in phytoplankton biomass in the sediment traps despite warmer surface waters, indicating that changes in mixing depth may negate a positive, direct effect of warming on primary producer biomass. Light limitation (induced by deeper mixing) of phytoplankton played a greater role than temperature in structuring these Arctic lake phytoplankton communities. To put the manipulation in context, we surveyed twenty-four lakes across western Greenland to determine the strongest regional predictors of phytoplankton density. Across the landscape, lake chemistry and light attenuation in the epilimnion were the most important predictors of algal assemblages in the survey lakes. Though temperature can directly influence lake ecosystems, multiple factors will affect mixing depths of Arctic lakes, potentially leading to variable effects of warming on phytoplankton biomass and community structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".