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Enregistrement W7139481808

UBC Climate Resilient Gardens

2024· report· en· W7139481808 sur OpenAlexaffabout
Hannah Tahami, Kat Seow, Farbod Alirezae, Liliana Henriksson

Notice bibliographique

RevuecIRcle (University of British Columbia) · 2024
Typereport
Langueen
Domaine
Thématique
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésExtreme weatherFood securityClimate changeMicroclimateAgricultureGlobal warmingFood systemsGreenhouse gasFood processing
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Unsustainable human activities are intensifying the effects of global warming through the emission of greenhouse gasses (GHG). These GHG emissions have caused the global surface temperature to increase by 1.1°C between 2011-2020 compared to the temperature between 1850-1900 (IPCC, 2023). The Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) emphasizes with high confidence that human-caused climate change greatly influences extreme climate and weather across the globe. Extreme weather events cause losses and damage to nature and people, leading to consequences such as decreased food and water security (IPCC, 2023). With a growing global population placing increasing pressure on agricultural systems to maximize production, measures must be taken to reduce the risk imposed by extreme climate and weather. We need to prioritize mitigation and adaptation strategies (Fróna et.al., 2019; City of Vancouver, 2012; 2020). Localizing food production can help support community food security and literacy (Ziervogel & Ericksen, 2010). Consequently, the purpose of our research was to identify strategies to increase the climate resiliency of food systems and landscapes at the UBC Vancouver Campus. This was accomplished through a literature review, field research on the microclimates of four garden sites and group interviews with gardeners and garden managers. The previous iteration of this project identified climate-resilient food plants suitable for growth to support climate-ready food gardens at the UBC Vancouver campus (McLeod et al., 2023). We expanded on this project by addressing the knowledge gap regarding management strategies focused on the impacts of extreme weather events. Specifically, we addressed the events of heat domes, cold snaps, high winds and flooding. Our research expanded past food-plant production to include the broader ecosystem, microclimate, and community aspects. Our literature review focused on how extreme weather events affected food availability in Metro Vancouver, British Columbia, with emphasis on the significance of food sovereignty. Gillett and colleagues (2022) define food sovereignty as a concept that stresses the importance of everyone having the right to healthy and locally suitable food that is produced in an environmentally friendly way. The review of policy documents and peer-reviewed sources revealed significant gaps in policy, in particular that UBC is lacking a specific strategy to address extreme weather events for campus food growing activities. It was suggested that bringing communities together and allowing them to be more involved in planning and managing local gardens, will help improve resilience to extreme weather and reduce food insecurity (Drolet, 2011). Sohail and Chen (2022) emphasize the importance of gathering detailed climate data to improve the current understanding of extreme weather to better overcome challenges presented by them. Using the principles of Community-Based Action Research (CBAR), our research and recommendations prioritized the inclusion of key stakeholders and community members in every stage of the research process focusing on identifying and bolstering the existing strengths within the community (Gullion & Tilton, 2020). We recommend that UBC Campus and Community Planning along with our clients, the UNA and Botanical garden, distribute our toolkit to campus food growers. We also propose the development of an in-person campus food garden networking opportunity through workshops to improve communication and share knowledge between campus food growers to increase community resilience to managing the impacts of extreme weather events. We recommend that future SEEDS delves deeper into prolonged microclimate assessments of more campus locations, looking into plant breeding to breed more climate-ready crops, and extending research regarding our campus food gardens and their future regarding managing more severe and often extreme weather events. A UBC Climate-Ready Toolkit (Appendix D) was developed based on findings from our research in addition to a publication of this report to the UBC SEEDS library. This UBC Climate Ready Toolkit contains an audit of four garden site microclimates and a framework on how to collect microclimate data, information on the potential impacts of extreme weather events on UBC gardens and a management plan to build local resilience to extreme weather events. Disclaimer: “UBC SEEDS provides students with the opportunity to share the findings of their studies, as well as their opinions, conclusions and recommendations with the UBC community. The reader should bear in mind that this is a student project/report and is not an official document of UBC. Furthermore readers should bear in mind that these reports may not reflect the current status of activities at UBC. We urge you to contact the research persons mentioned in a report or the SEEDS Coordinator about the current status of the subject matter of a project/report.”

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,245
Score d'incertitude au seuil0,820

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,002
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0060,001
Communication savante0,0040,001
Science ouverte0,0010,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,2450,047

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.

Tête enseignante Opus0,016
Tête enseignante GPT0,217
Écart entre enseignants0,201 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission2
Résumé présentoui

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