An overburden thickness model for Lac de Gras and Aylmer Lake, Northwest Territories, Canada
Bibliographic record
Abstract
Please click here to download the map associated with this article. Much of northern Canada is covered by variable thicknesses of surficial sediment. Geological maps portray these sediments using subjective terminology such as till, marine sediments, esker or organics etc. surficial sediment and bedrock geology units are primarily derived from air photo interpretation. In the Lac de Gras and Aylmer Lake area of the Canadian Northwest Territories, there is limited primary depth-to-bedrock information, and thus a traditional overburden thickness model is difficult to acquire. A model can however be developed using inferred unit thickness information obtained from published 1:125,000 surficial geology maps and a digital elevation model. The modelling process is based on the construction of a bedrock elevation database that is subtracted from a digital elevation mode to provide an overburden thickness. The bedrock elevation database is derived by assigning each surficial unit an approximate thickness and subsequently subtracting this thickness from the each cell of the digital elevation mode. The resulting dataset represents a best approximation of the buried bedrock surface with a cell size determined by the digital elevation mode. This model may be used for a number of applications such as planning regional geophysical or geochemical surveys where data quality is affected by variable overburden thickness.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".