Importance of Detailed Terrain and Geohazard Information for Pipeline and Infrastructure Developments in Arctic Environments
Bibliographic record
Abstract
Abstract Pipelines and roads represent the arteries of the oil and gas, and mining and transportation industries, respectively. They move product from remote locations to more centralized locations, either for processing or for shipping to refineries and mills for subsequent processing. Proper infrastructure development is critical to the successful development of the sensitive Arctic environment especially true in light of ongoing climate change where the melting of permafrost poses significant issues for development in the Arctic. The harsh Arctic environment presents unique challenges that are not found in more southern latitudes for the oil and gas and transportation sectors, including permafrost and permafrost degradation. It is well acknowledged that the extent of permafrost in northern environments is poorly known and mapped. New tools are being used to help determine the extent of permafrost and to identify areas that are more susceptible to permafrost degradation in light of on-going and future development. One such tool is the use of softcopy mapping to help map terrain and geological modifying processes such as permafrost. Softcopy uses traditional stereo aerial photographs in a digital environment to allow scientists the ability to view the landscape at scales of 1:1,000 from traditional aerial photography that were captured at scales of 1:24,000 to 1:40,000. The advantage of softcopy is that by being able to zoom down to such large scales allows terrain scientists the ability to better determine the soil types (sand, silt or clay), drainage conditions (rapid to very poor) and on-going geological processes such as permafrost as evidenced by frost boils and permafrost degradation as evidenced by presence of thermokarst and thaw slides. Another method often utilized where stereo aerial photography is not available is use of remote sensing datasets such high resolution digital elevation models and satellite imagery which are becoming general available in Arctic regions. These elevation models are used to create hillshade images of varying aspects and photorealistic 3D models to help map terrains. This paper will present a number of examples of where such mapping has been used to assist in pipeline and infrastructure planning in Alaska and Canada's north.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".