Assessing hypolimnetic oxygen concentrations in Canadian Shield lakes: Deriving management benchmarks using two methods
Bibliographic record
Abstract
The ability to predict hypolimnetic dissolved oxygen concentrations in lakes and to track changes in concentrations over time in response to known environmental stressors is critical for effective lake management. The background concentrations of deepwater oxygen, in particular, provide important management benchmarks for assessing the impact of current and future shoreline residential development on water quality. Background can be defined as the conditions that exist in the absence of, or prior to, human influence. We compare 2 models commonly used to predict end-of-summer, volume-weighted hypolimnetic oxygen (VWHO) concentrations in Canadian Shield lakes. The paleoecological and empirical models are evaluated in their ability to predict present-day VWHO concentrations, and then compared in their predictions of background VWHO concentrations and in predictions of changes in VWHO from background to present-day conditions. The predictive power of the 59-lake paleoecological model (jackknifed r2 = 0.51, RMSEP = 2.18 mg/L) is comparable to other models that have used chironomids to predict the degree of hypolimnetic anoxia in lakes but is lower than that produced by the empirical modelling approach (r2 = 0.87, SE = 1.04 mg/L). However, this discrepancy may be offset by the enhanced realism of the paleoecological model, including its ability to predict declines in VWHO over time. The combined use of the paleoecological and empirical modelling approaches may allow lake managers to examine changes in deepwater oxygen concentrations in response to a single “targeted” stressor (e.g., residential shoreline development) and to multiple environmental stressors (e.g., climate change, hydrological management).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".