Multi-Objective Modeling to Improve Wetland Diversity around Lake Ontario
Bibliographic record
Abstract
Since 1960, the outflows of Lake Ontario have been controlled and water levels managed by the structures within the St. Lawrence Seaway and Hydropower Project. These operations have enabled the generation of hydroelectric power, commercial navigation from the Atlantic Ocean to the Great Lakes and benefits through a reduction of flooding of properties along the lake's shoreline. However, the resulting reduced range of levels negatively affected the lake's wetland diversity compared to that which existed pre-project. The recently completed International Lake Ontario-St. Lawrence River Study proposed alternative operational plans which improve conditions for all interests and increase ecological benefits, principally wetland diversity; creating no disproportionate loss to any interest. The Study used a Shared Vision Planning approach along with an Integrated Ecological Response Model to investigate alternative management plans and determine their impact in terms of environmental performance indicators. This paper describes the estimated impact that current lake level management has had on wetlands and selected biota and the alternative plans developed by the Study Board to improve conditions. It also describes the adaptive management procedures proposed to assure that the anticipated results would be fulfilled.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".