Communicating research findings and monitoring data in support of management: A case study of the Bay of Quinte Remedial Action Plan
Bibliographic record
Abstract
The Bay of Quinte is a nearly enclosed bay in Lake Ontario which has been impacted by multiple industrial contaminant events and persistent eutrophication. As a result, it became one of 43 Great Lakes Areas of Concern (AOC) identified and supported by the International Joint Commission (IJC) for remediation. The Bay of Quinte Remedial Action Plan (RAP) relies on data from Project Quinte, a long-term monitoring program, to set targets, assess the status of beneficial use impairments and evaluate restoration progress. The ability of organizations to communicate relevant and timely scientific research and monitoring to decision-makers has recently emerged as an important issue in the literature. This article explores the issue of communicating research and monitoring information for the purpose of aiding decision-making through a case study of the Bay of Quinte RAP. Research included semi-structured interviews with scientists, regulators and community stakeholders involved with the Bay of Quinte RAP, observational research, document analysis and literature review. Findings indicate that multiple and diverse techniques are used to communicate research and monitoring data to decision-makers. Furthermore, our findings indicate that accurate tracking of trends, valuing high quality monitoring, promoting stakeholder cooperation, collaborating with other groups implementing RAPs and informing management and decision-making are key beneficial outcomes of shared science about the Bay of Quinte. Lessons learned emphasize the importance of administrative support and institutional memory; integration of ecosystem models; consistent long-term monitoring; and public engagement. These lessons are instructive for stakeholders conducting ecosystem restoration, planning or management, particularly those involved in any of the other RAPs ongoing on the Great Lakes.
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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 | 0.023 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.024 | 0.011 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.005 |
| 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".