The Role of Watershed Planning and Governance in Human Health and Well-being in the Credit River Watershed in southern Ontario
Bibliographic record
Abstract
The role of watershed planning and governance is crucial in ecosystems health, and human health and well-being. This project particularly focuses on the assessment of the 12 human health and well-being indicators (identified in the governance stakeholders' workshop organized by CVC in collaboration with York University on November 6, 2014) and the identification of the roles of the governance stakeholders in monitoring these indicators. In this project, the indicators have been assessed in light of the information solicited through in-person and telephone interviews with the governance stakeholders working in municipalities, conservation authorities, public health agencies, not-for-profit organizations, community organizations and academic institutions mainly within the Region of Peel. The interviews were conducted using a semistructured questionnaire which comprised questions pertaining to the mandate of the organizations of the stakeholders with respect to human health and well-being, general benefits of measuring these indicators, specific benefits to vulnerable groups for measuring the indicators, weaknesses of measuring the indicators, and uses of these indicators for different purposes. \n \nThe interviews' results identify the relationships between the indicators and partnerships among different stakeholders in monitoring or implementing these indicators. The results also impart that some indicators (e.g. % people using natural space) are hard to define and measure, some indicators (e.g. air quality index) can be employed at both local and provincial levels, some indicators (e.g. % imperviousness) are quantitative and cannot be easily understood by the general public, and some indicators (e.g. land cover change) are considered master variables and cannot be measured alone. The results also identify the need for defining natural and green spaces for consistent application of the indicators.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".