Under-ice and open-water ecosystem metabolism in temperate water bodies
Bibliographic record
Abstract
Winter, historically a largely un-monitored season, is influential and changing. There is evidence of the importance of under-ice phytoplankton in temperate lakes, but it is currently unknown if high winter phytoplankton biomass translates to high productivity and what influence it has on year-round lake metabolism. Winters are getting shorter, but our ability to forecast change is hindered by our limited understanding of under-ice processes. Here, we compare under-ice and open-water rates of areal gross production (AGP) and areal respiration (AR) from 3 Canadian reservoirs and one large lake using oxygen (O2) δ18O-O2 models and fluorometry. During the open-water season, AGP was 5× greater than under-ice rates, with AR rates 8× higher than measured during winter. Open-water samples indicated autotrophy (P:R= 1.10) with heterotrophy dominant under ice (P:R= 0.67). Consistent with current assumptions, the cold under-ice environment is associated with low primary productivity. Our results challenge the assumption that mean water column irradiance is lowest during the winter in dimictic water bodies; we find similar light conditions during the open-water season. Winter mean light is regulated by snow thickness; upon manual snow removal, we observe a 67 % increase in under-ice mean water column irradiance. The first-ever under-ice application of the δ18O2-method indicated that AGP responded to improvements in light. This study reveals further insights into the importance of under-ice metabolism on year-round processes in a changing 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".