Deepwater dissolved oxygen shows little ecological memory between lake phenological seasons
Bibliographic record
Abstract
Depletion of deepwater dissolved oxygen (DO) in lakes has become increasingly prevalent and severe because of many external stressors, potentially threatening human-derived ecosystem services ranging from drinking water quality to fisheries. Using year-round, high-frequency DO data from 12 dimictic lakes, we compared 3 measures of deepwater DO depletion during winter and summer: DO depletion rate, DO minimum, and hypoxia duration. Hypoxia (DO < 3 mg L−1) occurred in over half of the lakes and persisted an average of 83% longer in summer than in winter. While we found no difference in DO depletion rates between winter versus summer, these rates were significantly related to lake morphology in winter but trophic state in summer. In assessing cross-seasonal linkages, we found limited evidence for significant legacy effects in deepwater DO availability. Only fall mixing efficacy significantly responded to the previous summer’s minimum DO saturation, but it always reached moderate to high DO replenishment levels (>65%) regardless of the previous summer’s DO depletion severity. This lack of ecological memory in deepwater DO depletion across seasons suggests that deepwater DO largely resets during spring and fall mixing periods in most years in these dimictic lakes. Understanding the patterns and drivers in deepwater DO depletion in both winter and summer is a key step forward for predicting future chemical and biological consequences of seasonal DO depletion and managing lake ecosystem health, as well as the effects that climate change may have on these patterns.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".