Bibliographic record
Abstract
During the 1980s, there were growing concerns, on the part of the public and government, about the natural environment and, therefore, about ecologically sound management of natural resources, particularly in urbanizing areas. There were some improvements to resource management, development and engineering design and land use planning, but this was done in a relatively ad hoc way. It is now recognized that sound environmental management is important for the economic health and stability of successful communities. A general integrated approach to resource management and land use planning that is endorsed by everyone involved can promote consistency and efficiency in both these processes. There are, of course, many environmental benefits of this approach, but economic benefits will also be realized as costs for remediation and cleanup strategies are expected to correspondingly decrease. There are additional economic benefits to Ontario, less directly, of a clean and healthy environment. Subwatershed plans provide important information to the land use decision-making process for the use and management of water and land that compatibly integrate natural systems with changing land uses. Subwatershed plans should reflect the goals of the watershed plan (if there is one), but are tailored to tributary needs and local issues, and provide detailed guidance on site-specific water resource planning issues. This discussion sets out why, when, and how to prepare subwatershed plans; it is intended for the use of all who work in the fields of resource management, land use planning and land development, and for anyone interested in these issues. Its purpose is: 1)To promote an ecosystem-based approach to environmental and land use planning at a subwatershed level.; 2. To foster early, integrated planning for land use, water management, and environmental protection and management on a subwatershed basis.; 3. To assist government and municipal agencies, consultants and the development industry who may be involved in or working in the context of subwatershed planning.; and 4. To enhance the efficiency and effectiveness of the land use plan preparation and review process.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.139 | 0.025 |
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".