Geophysical Characterization of Permafrost Distribution in the Yukon River Basin, Alaska
Bibliographic record
Abstract
Geophysical tools including airborne electromagnetic (HEM), time domain electromagnetic (TDEM), DC resistivity and continuous resistivity profiling (CRP) are used to evaluate permafrost distribution in the Yukon River Basin in the area of Fort Yukon, Alaska. Permafrost is a primary control on hydraulic processes in interior Alaska, but its distribution is poorly characterized. Major questions about the distribution of permafrost limit understanding of hydrology such as the thickness of the permafrost layer, the geometry of taliks and their role in the hydrogeologic framework, and the connection between surface water and groundwater. Electrical and electromagnetic geophysical technologies can be used to characterize permafrost distribution by exploiting the high resistivity contrast of frozen and liquid water phases. These techniques can provide improved spatial coverage at a higher resolution and lower cost than traditional direct sampling techniques, which remain sparse in the area. In an effort to develop geophysical techniques for characterizing permafrost and inform hydrologic models, initial campaigns of geophysical data collection were conducted during the summer of 2010. HEM data was collected from a helicopter in the region surrounding Fort Yukon, Alaska, including the Yukon River and the Porcupine River. Using initial results of the HEM surveys, sites were chosen to collect ground based geophysics to investigate anomalies that appear to relate to the hydrogeologic framework of the area. The ground based geophysical campaign included TDEM, DC resistivity, and CRP. Initial results from this campaign show that the techniques can be successfully used to map thickness and distribution of permafrost in the area.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".