Permafrost modelling in the Mackenzie Valley
Notice bibliographique
Résumé
The actual distribution and character of permafrost across Canada is poorly understood and in most cases is represented by the highly generalized Permafrost Map of Canada which describes only broad zones of continuous, discontinuous, or sporadic permafrost. While this map is useful for visualization on a small scale (eg 1:7 000 000), it is inadequate for addressing climate change issues because it contains no information about the actual distribution of frozen vs. unfrozen ground, probable ranges of ground temperature, or local/regional variations in permafrost thickness. Modeling at small scale (1 km resolution) may be suitable for policy development, but intermediate scale modeling (30 m resolution) is more appropriate for planning purposes. High-resolution information about permafrost and associated geotechnical characteristics is required by engineers, regulators, and community stakeholders responsible for assessing potential risks to infrastructure and traditional northern lifestyles in the face of climate change. The Geological Survey of Canada has developed transient numerical modeling to help estimate the current distribution and thickness of frozen ground in the Mackenzie River valley and to generate time-series predictions of future impacts to permafrost under a progressively warming climate. A one-dimensional finite element heat conduction model (T-ONE), integrated into an ArcGIS spatial analysis platform, enables pseudo 3-dimensional modeling of ground thermal conditions across extensive geographic areas. Ground and surface temperatures from instrumented boreholes and active layer measurements were used to calibrate the model and validate outputs. The model is physically-based, incorporating key climate and terrain factors considered to exert significant influence on the ground thermal state. Current permafrost characteristics as well as predictions of possible impacts of climate change are necessary for engineers and decision-makers who are responsible for the maintenance and planning of infrastructure projects. This model was used to predict current ground thermal conditions and permafrost characteristics along NWT highways and roads, and potential climate-induced changes to permafrost that may be realized over practical engineering time frames. These predictions were used to identify areas within the transportation corridors that would have significantly greater thaw depths leading to possible subsidence and terrain instability. The application of transient numerical modeling to geo-statistical methods can be used to generate secondary knowledge products. Using a Weight-of-Evidence based landscape-process model, multiple terrain factors such as geology, permafrost, topography/topology and surface hydrology are used to identify and map terrain susceptibility to various types of terrain instability, including retrogressive thaw flows and rotational slides. For the T-ONE transient results, limited ground truthing and statistical validation of modeling outputs have established a reasonable level of confidence in model performance within the broader Mackenzie Valley. Several data and knowledge gaps remain, such as surface organic layer thickness and properties, snow cover, up to date forest fire distribution, as well as location and persistence of air temperature inversions. Also, the correlation between geomorphologic units andmoisture/ice content is mostly based on expert knowledge. A more rigorous approach to quantify the amount and distribution of frozen and unfrozen water at various scales would help resolves some latent heat issues.
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 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| 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 ».