Mapping recreation use patterns and forest values : a Canadian boreal forest case study / by Perrine, Lesueur.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».