Biotic, chemical, and morphometric factors contributing to winter anoxia in prairie lakes
Bibliographic record
Abstract
To assess how features of lakes and their watersheds influence winter oxygen decay rates and the frequency of anoxia in shallow prairie lakes, we measured lake and watershed characteristics for 21 south‐central Alberta lakes and related these to measured oxygen decay rates during 1998–2000. Oxygen decay rates were functions of macrophyte biomass, percentage littoral area, and total phosphorus and ranged from 0.006 to 0.216 mg O 2 m −3 d −1 . Oxygen decay rates were ~4 times higher in shallow polymictic lakes compared to deep, stratified lakes. Within shallow lakes, those classified as turbid had decay rates ~1.5 times higher than those classified as clear. Chlorophyll a was not a predictor of the oxygen decay rate in shallow lakes; however, macrophyte‐derived carbon averaged ~150 times more than phytoplankton‐derived carbon in the shallow lakes we examined. Reasons that lakes frequently or never become anoxic are related to productivity and morphometry; however, processes explaining occasional anoxia appear not to be related to factors we measured.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".