Evaluation of a Citizen-Science Highway Wildlife Monitoring Program
Notice bibliographique
Résumé
The Crowsnest Pass in southwestern Alberta, Canada has been highlighted as a critical area for wildlife movement. There are plans to upgrade Highway 3, which cuts through the Pass, to four lanes, with resulting increased traffic volume and speed. Currently, highway traffic volume is between 2,500 to 10,500 vehicles/day. Highway 3 may already be acting as a barrier to large carnivore and ungulate movements patterns, and wildlife mortality from animal/vehicle collisions on Highway 3 is approximately 109 large mammal deaths reported annually for a 46km stretch within the Pass. Detailed wildlife movement information in the Pass is limited.To assist in understanding wildlife movement patterns along the highway to support decision-making for mitigation, a community based monitoring project was developed. The Alberta research institute Miistakis Institute of the Rockies created Road Watch in the Pass (RW), which allows local citizens to enter their wildlife observations along Highway 3 through an interactive web-based mapping tool. Over 1220 observations have been collected in over sixteen months, including 11 species of ungulates and carnivores.This innovative approach to data collection would benefit from an analysis to determine whether the citizen reports are accurately representing visible wildlife activity along Highway 3. There are likely biases in citizen reports, based on unequal sampling effort involving location and frequency of travel. To identify and address these biases, this study compares spatial and temporal wildlife observation data from RW to a systematically gathered dataset using various statistical approaches.We began systematic data collection in May 2006 and will continue through May 2007 to examine spatial and temporal characteristics of large mammal species movement (bighorn sheep, elk, moose, mule deer, white-tailed deer and carnivore species) along the highway. A 46-km stretch of Highway 3 was driven as a strip transect. When we observed an animal along or crossing the highway, UTM location, species, date, time, and other data were recorded. Each hour within the 24-hour period were sampled equally across a full year, allowing temporal analysis. Similar data from RW reports provided by citizens during the same period were extracted from the RW database. From May 2006 to March 2007, 395 transects were driven totaling over 395 hours of data collection and resulting in 681 wildlife observations. Spatial and temporal comparisons will be made between systematically gathered data and concurrent Road Watch data. Analysis will include examination of spatial association between the two data collection processes, comparison of spatial distribution, comparison of hourly and seasonal temporal distribution, effect of any biases on spatial or temporal distribution or species composition, and other analyses.The Road Watch program is an important use of citizen involvement in transportation science. After analysis of RW’s accuracy in representing visible wildlife activity in the Crowsnest Pass, this study will provide suggestions and stipulations to improve the scientific rigor of this unique citizen-science program.
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 | 0,012 | 0,016 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
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 ».