Recent Advances (2018–2023) and Research Opportunities in the Study of Groundwater in Cold Regions
Bibliographic record
Abstract
ABSTRACT Increasing greenhouse gas levels drive extensive changes in Arctic and cold‐dominated environments, leading to a warmer, more humid, and variable climate. Associated permafrost thaw creates new groundwater flow paths in cold regions that are causing unprecedented environmental changes. This review of recent advances in groundwater research in cold environments has revealed that a new paradigm is emerging where groundwater is at the center of these changes. Groundwater flow and associated heat and solute transport are now used as a basis to understand hydrological changes, permafrost dynamics, water quality, integrity of infrastructure along with ecological impacts. Although major advances have been achieved in cold regions' cryohydrogeological research, the remaining knowledge gaps are numerous. For example, groundwater as a drinking water source is poorly documented despite its social importance. Lateral transport processes for carbon and contaminants are still inadequately understood. Numerical models are improving, but the highly complex physical‐ecological changes occurring in the arctic involve coupled thermal, hydrological, hydrogeological, mechanical, and geochemical processes that are difficult to represent and hamper quantitative analysis and limit predictive capacity. Systematic long‐term observatories where measurements involving groundwater are considered central are needed to help resolve these research gaps. Innovative transdisciplinary research will be critical to comprehend and predict these complex transformations.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".