Contributions of landscape ecology, multifunctionality and wildlife research toward sustainable forest management in the Greater Toronto Area
Bibliographic record
Abstract
Forested lands in southern Ontario are threatened by a myriad of demands. In order to capture the multi-scale, multi-use and multifunction reality of forests within such intense human-nature interdependent landscapes, an integrative approach to sustainable forest management is necessary. Such forest management may be possible by combining the framework of landscape ecology with an understanding of forest multifunctionality. Within the Greater Toronto Area, the management of forests is provided by several agencies; some are responsible for 1) geological landscapes (e.g., the Niagara Escarpment), 2) for watersheds (e.g., Conservation Authorities) and 3) for political regions (e.g., York Region). In this paper, case studies reflecting important management issues are introduced. Wildlife research is then presented to link these issues to landscape ecology and forest multifunctionality in order to illustrate a means of enhancing sustainable forest management. Key words: landscape ecology, multifunctionality, multifunctional approach, sustainable forest management, Greater Toronto Area, wildlife function, integrative forest management
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".