Planning for ecological health and human well-‐being in the Credit River Watershed: Social well-being benefits of urban natural features and areas
Bibliographic record
Abstract
The relationship between ecological systems and well-‐being is nearly intuitive, and it has long been assumed that the outcome of good watershed management is human health and well-‐being. This study seeks to make this relationship more apparent with a focus on the perceived effects of natural features and areas on social well-‐being in the Credit River Watershed, southern Ontario. The use of a survey instrument, inductive analysis, statistical tests for differences and association, and exploratory factor analysis determined that a variety of natural areas are considered by respondents to be important contributors to well-‐being. Streams and river management should be prioritized since visits to these spaces affect the perception of outdoor and social well-‐being relationships more strongly. Sense of community, an aspect of social well-‐being, is cultivated through opportunities for gathering and meetings provided by green space. Though streams and rivers, forests and wetlands, open green spaces, home gardens and functional green space contribute to an aspect of social well-‐being in one way or another, the associations are dependent on the respondent's location and context. Accessibility and distribution of green space, \nas well as diversity of natural features may be key in the differences between the perceived social well-‐being and natural environment relationships. Planning for social well-‐being therefore involves the management of diverse and biodiverse spaces.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".