State-of-the-Wilderness Reporting in Ontario: Models, Tools and Techniques
Bibliographic record
Abstract
As in other parts of the world, Ontario’s agricultural and industrial growth has marked the decline of wilderness. Aboriginal peoples used fire to clear land for agricultural purposes. European settlers accelerated the removal of trees, built roads and created communities in the pursuit of timber, farms and better lives. Consequently, population growth, intensive agriculture and an expanding industrial base have significantly reduced the quality and quantity of wilderness. In a mere 300 years, just 15 generations, wilderness in Ontario has been relegated to the more remote and isolated parts of the province. Like many other societies, Ontario values wilderness for different reasons. Some Ontarians view it as a storehouse of natural resources, to be used for social and economic gain. Others see it as a living system, replete with natural wonders and opportunities for discovery, where people live in harmony with nature. Most would agree that wilderness is vast, remote and unspoiled. To many others, however, wilderness can be a small, isolated ravine or a wood lot within a highly developed urban setting. While our opinions vary greatly, Ontarians are passionate about wilderness. Oracle Research reported in 1996 and 1998 that 97% of people polled believed that protecting wilderness areas was very important and 86% believed that as much as 20% of existing publicly owned land should be set aside for wilderness protection. In a another study, Manifest Communications (1996) reported that 81% of people polled agreed that provincial parks were very important to Ontario’s identity and that wilderness is the defining characteristic in people’s sense of what makes Ontario’s parks special and unique. This paper provides a brief history and status report on wilderness protection in Ontario. It outlines a framework for state-of-the-wilderness reporting; describes an ecosystem classification model used to determine the distribution, nature and status of wilderness; describes a model to identify and delineate remaining wilderness; and shows how recent Crown land-use planning has contributed to wilderness protection. The application of Ontario’s Natural Resource Information System (NRVIS) and related ARC/INFO GIS tools to the framework are illustrated. The paper also presents some preliminary ideas on a wilderness quality index designed to allow natural resource managers to measure the quality and quantity of the wilderness condition and experience.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".