Permafrost modelling in the Mackenzie Valley
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".