Mapping recreation use patterns and forest values : a Canadian boreal forest case study / by Perrine, Lesueur.
Bibliographic record
Abstract
People attribute values to the places they use for forest recreation. Such values are often difficult to access and even more difficult to incorporate in forest management and planning. As potential sources of conflict in forest management, understanding the different values attached to specific \nforest places is important for resource managers. Past research has tended to focus on surveybased methods of eliciting these values and has largely neglected both their contextual nature and spatial distribution. More recently, several projects have explored a wider variety of elicitation \nmethods and experiment with various ways of spatially representing forest values. \nDevelopments in Geographic Information System (CIS) technology and especially its accessibility through the World-Wide-Web have led to significant growth in the use of public participation GIS (ppGIS). This growth is occurring in both developed and developing nations where the spatial representation of physical and social attributes is central to planning issues. \nAlthough problems still remain in terms of accessibility and ease of use, the rapid growth of this technology and its increasing success in enhancing public involvement processes in managing natural resources has assured its place in planning technology. \nThis study focused on understanding the nature and mapping the spatial distribution of forest values in the Boreal forest surrounding five Northwestern Ontario communities. A web-based survey was created using GIS-maps and a list of forest values to allow participants to mark \nlocations in the study area and indicate their associated values. The survey provided respondents with the flexibility to mark specific sites (e.g., fishing spots), linear features (e.g., rivers) and also areas (e.g., lakes). Moreover, respondents were able to choose a scale that was most appropriate \nfor their mapping purposes. However, due to low internet speeds in the communities, some participants encountered difficulties with loading the map and using the mapping tools. To overcome this issue, a paper version of the survey was provided. A random sample of 750 people was invited to participate in the web-survey (50%) or in the paper survey (50%). The online and paper survey response rates were respectively of 31 per cent and 21 per cent. \nThe survey responses were used to produce a density map showing the spatial pattern of valued places, a High Use Areas map and associated forest values within these areas. Analyses of forest values and use characteristics (i.e., activity and frequency of use) of the sites helped to \ninterpret the use patterns on the map. The spatial representation of the values assigned to special places in a working forest, allowed the integration of recreational values and use characteristics into forest planning at the local and regional levels. Several High Use Areas were located in specially designated management areas that recognise the importance of recreational use. The remaining High Use Areas occur along major access roads for industrial forestry which highlights the significance of forestry operations in providing access to forests to local \nrecreationists. The recognition of these High Use Areas and their characteristics provides important information for including recreational perspectives into forest and land use planning. \nStudy area : Red Rock, Nipigon, Schreiber, Terrace Bay, Marathon. Top recreational uses are : fishing, hunting, hiking, wildlife viewing, motor-boating, canoeing, kayaking.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".