Evaluating the influence of snow and ice conditions on under‐ice light regimes, dissolved oxygen, and primary production in shallow lakes using controlled manipulative systems
Bibliographic record
Abstract
Abstract Shifts in hydroclimatic regimes associated with global climate change are affecting the timing and duration of winter ice‐cover in temperate and high‐latitude lakes. Less is known on how associated changes in ice quality potentially affect under‐ice ecological processes and related biogeochemistry, particularly in shallow lake systems. This experimental study examined how differing surface ice‐cover types (slushed white ice, unmanipulated ice formation [black], snow‐on‐ice) affected under‐ice light and oxygen regimes and associated chlorophyll a concentrations, and water quality parameters using a combination of aboveground mesocosms and natural ponds over two consecutive winter seasons. In both systems, light transmission under white ice did not differ significantly from black but was significantly greater than snow‐on‐ice. Measures of light extinction coefficients taken at the ice‐water interface in the experimental ponds increased for black, white, and snow‐on‐ice, respectively, with all values being above thresholds for autotrophic activity. Under‐ice dissolved oxygen levels in all mesocosm ice treatments increased continually over time with increasing day length, peaking prior to ice off. Accumulated DO levels were aligned with observed chlorophyll a concentrations, with peaks early after ice onset, and decreases as winter progressed though patterns differed between years. No significant differences were observed in mean chlorophyll a levels in snow‐removed treatment ponds, regardless of whether white or black ice was present while the snow‐on‐ice ponds displayed consistently lower levels. This study highlights the complexities and importance of using manipulative experimental approaches to advance our understanding of how alterations in surface ice‐cover quality affect under‐ice hydro‐ecological processes.
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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".