Recharge and Preservation of Laurentide Glacial Melt Water in the Canadian Shield
Bibliographic record
Abstract
Abstract Ground water inflows to drifts ranging from 700 to 1615 m below ground surface at the Con Mine, Yellowknife, Northwest Territories, Canada, were used to study deep hydrogeological flow regimes in Shield terrain. Salinity trends are due to mixing between low‐TDS ground water and deep Ca(Na)‐C1 brines (>290 g/L) likely derived from Devonian sea water. C1 ‐ −δ 18 O relationships demonstrate that all inflows are a mixture of three distinct components: modern meteoric ground water (δ 18 O ∼−18.9 ± 0.1% o ), brine (δ 18 O ∼−10% o ), and an isotopically depleted water (δ 18 O ∼−28% o ). The origin of this third endmember is attributed to glacial melt water injected into the subsurface during ablation of the Laurentide Ice Sheet at ca. 10 ka. A mechanism is proposed where high hydrostatic pressure in the ablation zone imposes strong downward gradients beneath the ice sheet margin. Numerical simulation with the SWIFT II finite‐difference code recreates the observed salinity gradients within a modeled 50‐year interval, corresponding with the rate of retreat of the ice sheet across the landscape at this time. The persistence of this melt water in the subsurface for some 10,000 years following retreat of the ice and decay of the steep hydraulic gradients highlights the importance of gradient, in addition to permeability, as a major control on ground water flow and transport in deep crystalline settings.
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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".