Visitor Statistics for Conservation Authorities in Ontario: Current Status and Methods
Bibliographic record
Abstract
In an effort to quantify the importance of recreation in Conservation Areas in Ontario, a survey package with a standard set of definitions and instructions was sent to all 36 Conservation Authorities. One major goal was to compile a database of information on data ranging from the number of visitors received each year to the variety of recreation activities available at each Conservation Area. No such data province-wide was currently available. All 36 Conservation Authorities submitted data. Results indicated that the Conservation Authorities in Ontario have responsibility more than 11.6 million hectares of watershed land, and have just over 10 million people living within their boundaries. In 2000 there were just over 5.7 million people visiting nearly 500 Conservation Areas that encompass almost 75,000 hectares of land. There are 21 Conservation Authorities that operate 61 campgrounds that contain over 8,000 primitive and improved campsites. The survey revealed several problems. The definition for “Conservation Area ” is not standardized, with many names used, such as wetlands, wildlife areas and forests. Other evidence suggests there are a large number of Conservation Authority owned areas and several thousand hectares of land not reported. Additionally, 286 Conservation Areas did not report any visitor statistics. Therefore, the database is incomplete. Future studies will have to adjust the survey and the definitions in order to collect the missing data. One way to achieve this goal would be the widespread implementation in Ontario Conservation Authorities of a progressive public use measurement system and reporting system as developed by Hornback and Eagles (1). Each Conservation Authority could adapt the system to fit their specific needs and, in turn, produce data that is more accurate and beneficial for managers. 1.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.038 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".