Morphology and Sedimentology of Eskers in the Lac de Gras Area, Northwest Territories, Canada
Bibliographic record
Abstract
An integrated dataset, including LiDAR, grain-size analysis, GPR, and augered boreholes, was used to perform a morphological and sedimentological analysis on 28 km of eskers in the Lac de Gras area, NWT. Esker segments were classified based on morphology and surficial grain size, and depositional environments were interpreted based on sedimentological data gathered from GPR and boreholes. Peaked ridges of cobbles and boulders (Type 1) were inferred to deposit subglacially, and flat topped ridges of finer sediment (Type 2, 3, 4) were inferred to deposited deltaically. Deltaic deposits are found overlying Type 1 subglacial deposits, and at the down esker extents of Type 1 ridges. Evidence points to a time transgressive depositional model, with maximum extent of subglacial feeding conduits not exceeding 3 km in the study area. These results have potential implications for mineral exploration. Transport distance within eskers may not substantially exceed that of the subglacial till.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".