Ground temperature monitoring and permafrost distribution mapping, Coffee Mine Project, Yukon
Bibliographic record
Abstract
Tetra Tech completed several geotechnical investigation programs in support of the proposed development of the Coffee Mine Project (the Project) owned by Newmont.The Project is located in west-central Yukon, in the discontinuous permafrost zone.A key objective was to acquire permafrost data, including ground temperature and ground ice content information, that would allow design of the mine infrastructure.Tetra Tech's geotechnical investigations, completed between 2015 and 2019, consisted of coring overburden and bedrock with a helicopter-transportable diamond drill rig.Chilled drilling fluid was used to minimize thermal disturbance of permafrost and recover undisturbed frozen core samples.Twenty multi-bead ground temperature cables and numerous single-bead thermistor strings were installed in some of the 103 geotechnical boreholes completed to determine ground thermal conditions and monitor changes in permafrost temperatures.The data collected allowed for accurate mapping of permafrost distribution.The surface appearance of terrain units where permafrost conditions were confirmed was extrapolated, and slope aspect and geobotanical indicators of permafrost occurrence were also used to map permafrost extent in areas lacking boreholes.Calculations of permafrost distribution within the Project Area show that approximately 61% of the mapped area is underlain by warming, locally ice-rich permafrost.Geospatial machine-learning models were created based on terrain derivatives (slope, aspect, elevation, topographic position index, and landforms), available orthoimage information (vegetation indices), and were compared to the newly mapped permafrost distribution (about 8,000 ha) to predict presence/absence of permafrost in the larger Project Area (about 39,000 ha).The model accuracy was 58% for the broader area.1
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".