Limits to Tourism and Recreation in Water Catchments
Bibliographic record
Abstract
Increase attention has been focussed on tourism and recreation access to public lands reserved for specific purposes, such as water catchments. Land based activities such as hiking, horse riding, motor and mountain biking, abseiling and off-road driving as well as water based activities (canoeing, fishing, swimming) have all been deemed by water managers as a risk to drinking water quality. Increasing demand has increased pressure for tourism and recreation access to these areas. The question then becomes, what level of risk to drinking water quality is posed by these activities? Also, what is the most appropriate management regime for tourism and recreation in water catchments?This paper is based on a review of the legislative, historical and current framework for managing public water catchment areas and drinking water sources in South Western Australia. The review includes an assessment of catchment management regimes in other states of Australia as well as in the United Kingdom and Canada. Management regimes range from total exclusion (as practiced in Western Australia) to managed tourism and recreation use of water catchments (as in Queensland and Victoria). Management of water catchments requires high levels of co-operation between government agencies responsible for land management, water quality and tourism and recreation and the adoption of integrated catchment management strategies is essential.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".