Potential Effects of Climate Change-Induced Low Water Levels on Rural Communities in the Upper Credit River
Bibliographic record
Abstract
In the water sector, the persistence of human impacts and associated costs of climate events point clearly to the need to identify strategies for coping with climate variability and change, and to develop an enhanced capacity to respond effectively (Hofmann et al., 1998). Rural communities – especially those in the rural-urban fringe – are challenged by the need to balance human uses (e.g., rural industry, recreation, municipal water supply) and ecosystem protection (e.g., maintenance of base flow to support fisheries, protection of wetlands that depend on shallow groundwater aquifers). Key stakeholders comprising rural communities in the rural-urban fringe include municipal water managers, rural residents and industries, farmers, golf course operators, anglers, and conservation groups. Not only are these people and groups experiencing increasing conflict and competition over water, particularly groundwater (Kreutzwiser and de Loë, 1998), but also they must cope with capacity-related challenges. Two issues are particularly important: • First, not much is known about the impacts of climate-induced water shortages on rural communities in Canada and the ecosystems upon which
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".