Oxygen dynamics in a boreal lake responds to long‐term changes in climate, ice phenology, and DOC inputs
Bibliographic record
Abstract
Abstract Boreal lakes are impacted by climate change, reduced acid deposition, and changing loads of dissolved organic carbon (DOC) from catchments. We explored, using the process‐based lake model MyLake, how changes in these pressures modulate ice phenology and the dissolved oxygen concentrations (DO) of a small boreal humic lake. The model was parametrized against year‐round time series of water temperature and DO from a lake buoy. Observed trends in air temperature (+0.045°C yr−1) and DOC concentration (0.11 mg C L−1 yr−1, +1% annually) over the past 40 years were used as model forcings. A backcast of ice freezing and breakup dates revealed that ice breakup occurred on average 8 days earlier in 2014 than in 1974. The earlier ice breakup enhanced water column ventilation resulting in higher DO in the spring. Warmer water in late summer led to longer anoxic periods, as microbial DOC turnover increased. A long‐term increase in DOC concentrations caused a decline in lake DO, leading to 15% more hypoxic days (<3 mg L−1) and 10% more anoxic days (<15 µg L−1) in 2014 than in 1974. We conclude that climate warming and increasing DOC loads are antagonistic with respect to their effect on DO availability. The model suggests that DOC is a stronger driver of DO consumption than temperature. The browning of lakes may thus cause reductions in the oxythermal habitat of fish and aquatic biota in boreal lakes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".