Loon Nest Viability Model: A Performance Indicator for Improving Water-Level Regulation of Large Water Bodies
Bibliographic record
Abstract
Rule curves dictating target water levels for management have been implemented in several water bodies in North America over the last 70 years or more. Anthropogenic alterations of water levels are known to affect several components of wetland ecosystems. Evaluating the influence of rule curves on biological components with simple performance indicators could help harmonize water level management with wetland integrity. We assessed the potential of using the probability of common loon nest viability as a performance indicator of long-term impacts of rule curves on nesting wetland birds. We analyzed the outcome of rule curves on the probability of loon nest viability in Rainy Lake and Namakan Reservoir, 2 regulated water bodies located along the Ontario-Minnesota border. The analysis was focused on 4 hydrological time series between 1950 and 2013: 2 sets of time series simulating rule curves used to manage the water bodies in the past decades (referred to as the 1970RC and 2000RC), one of the historical measured water levels, and one of computed natural water levels. The probability of loon nest viability under the 1970RC was 2× higher than under natural conditions in both water bodies. The probability was also 2× higher under the 2000RC than under the 1970RC in the Namakan Reservoir but not in Rainy Lake. The rule curves generally improved conditions for nesting loons in both water bodies. The presented performance indicator can be used to evaluate future rule curves before they are implemented in the Rainy-Namakan or other similar systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".