Model development in support of the Lake Ontario Cooperative Science and Monitoring Initiative
Bibliographic record
Abstract
Abstract The Cooperative Science and Monitoring Initiative aims to generate data and information to support environmental management in the Laurentian Great Lakes of North America. On a rotating basis, efforts are focused on each lake every five years. In this study, we developed a state-of-the-art hydrodynamic and ecological modeling framework to aggregate data collected during these initiative years and from other sources, and to simulate lake eutrophication processes in Lake Ontario, with an emphasis on nearshore conditions. Model calibration and validation were based primarily on data from three initiative years (2008, 2013, and 2018). This integrated model provides a framework for data organization, understanding complex lake process interactions, and guiding future data collection. It is designed as a management support tool that can simulate lake responses to changes in loading conditions, such as sensitivity of nearshore water quality to Niagara River phosphorus loads. Its designated aim is to support evaluation of management questions in Lake Ontario by providing quantitative evaluation of the relative benefits of potential nutrient loading abatement strategies to mitigate eutrophication in the nearshore. This framework is also well suited for possible future expansion to address management issues on a whole-lake basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".