The initial biometerology of the constructed Sandhill Fen Watershed in Alberta, Canada
Notice bibliographique
Résumé
Resource extraction has led to the loss of boreal landscapes.Recently, novel integrated landscapes have been constructed to return ecological functions lost due to disturbance.The Sandhill Fen Watershed (SFW) is one of the first of two such projects in the boreal plains ecozone.The SFW is a mix of upland, midland and lowland topographical features designed to develop into integrated boreal plains ecosystems.This thesis quantifies the initial carbon cycling dynamics within the SFW and contextualizes this development with studies on conventional reclamation projects and undisturbed ecosystems.Construction, vegetation seeding and planting of the SFW was mostly completed by 2012, and an initial wetting occurred in 2013.Methane fluxes remained low over the first three years.However, mobile ions of sulphur declined over this same period and could indicate shifting reduction-oxidation conditions favourable for future rates of methanogenesis.In the lowland, biogeochemical fluxes were indicative of highly reduced soil conditions.This was confirmed in 2015 with reduction-oxidation potential monitoring.Downscaled eddy covariance flux measurements showed that vegetation increased evapotranspiration (ET) rates in the lowland relative to non-vegetated water surfaces.Since there were no other substantial outflows of water from the SFW, greater rates of ET may lower the water table in the future and limit the carbon storage potential of the ecosystem through peat accumulation.Measurements of the CO2 exchange between the surface and the atmosphere showed that, with no other substantial carbon outputs, by its third year the SFW lowland had become a net carbon sink and had daily, monthly, and annual carbon cycling behaviour similar to undisturbed boreal wetlands.Net sequestration of CO2 in the I am grateful to have had such wonderful support and guidance from my supervisor, Elyn Humphreys.Elyn was immensely helpful in the articulation, development, and completion of this thesis and was an awesome role model.The Carleton Biomet lab was my home for over four years and everyone who has passed through it has enriched my life in some way.Stacey Strilesky deserves isolated praise for humoring me in our extensive talks on mining, micrometeorology, and this new field of novel ecosystem study we have found ourselves in.I would like to thank Sean Carey, and his Watershed Hydrology Group at McMaster University.They always made me feel part of their lab.Specifically, this research would not have been possible without the efforts of Erin Nicholls, Haley Spennato, Kelly Biagi, Chelsea Thorne, Jessica Sara, and Arthur Szybalski who sacrificed one, sometimes multiple, of their summers to help collect the data I used.I would like to thank Mike Treberg, not only for building, installing and maintaining most of our field equipment, but also for passing along some skills in the fine craft of DIY field work.Mike's innovations are legendary within both lab groups, and if I only learned enough to have a fraction of his problem-solving capacity I am worlds more capable than I was before I started this project.Gord Drewitt also has my gratitude for helping with countless emails sharing his micrometeorological knowledge and helping collect some of the chamber data despite having sworn to never again touch gas flux chambers.Thanks to Brendan O'Neill, Doug Pawson and Tara Cater, who helped me struggle through more than one long day/week/month/term in the Geography PhD offices.I am grateful for the beers, coffee, lengthy chats, but mostly the friendships forged over our time iv in the Department of Geography and Environmental Studies (DGES).Special thanks to Anna Crawford, who not only helped me through the solidarity of PhDs in the DGES, but also gave me a chance to escape my research for a little while to travel into the fiords of Baffin Island to help study ice islands.I found the trip deeply inspiring and spending time with her research did wonders for thinking about my own.Natalie
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».