Assessing the Role of Integrated Research and Monitoring Tools in Remediation Efforts at Great Lakes‐St. Lawrence River Basin Areas of Concern: A Case Study of the Bay of Quinte Remedial Action Plan
Bibliographic record
Abstract
In accordance with the Great Lakes Water Quality Agreement, the governments of Canada and the United States have agreed to support the remediation of 43 Areas of Concern (AOC) where “failure [to meet objectives of the agreement] has caused or is likely to cause impairment of beneficial use or of the area’s ability to support aquatic life.” A Remedial Action Plan (RAP) has been created for each AOC that outlines how impairments are to be addressed. This presentation will focus on one such plan, the Bay of Quinte RAP, as a case study to explore the role of research and monitoring in realizing the aims of the policy. Results will be based on a literature review encompassing existing works about Great Lakes RAPs, the Bay of Quinte watershed, multi‐party monitoring and the relationship between science and policy, along with semi‐structured interviews with project leaders and community stakeholders to determine how practice at the Bay of Quinte compares to theory and to practice at other Areas of Concern. The results will be instructive for any groups planning a multi‐stakeholder undertaking particularly those involved in any of the 40 other RAPs still underway on our Great Lakes.
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 | 0.041 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| 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".