Bibliographic record
Abstract
This data and R code accompanies a submitted journal article. The manuscript abstract is as follows: Mountains have a critical role in freshwater supply for downstream populations. As the climate changes, groundwater stored in mountains may help buffer the impacts to declining water resources caused by decreased snowpack and glacier recession. However, given the scarcity of groundwater observation wells in mountain regions, it remains unclear how mountain groundwater is being impacted by climate change across ecoregions. This study quantifies temporal trends in mountain groundwater levels and explores how various climatic, physiographic, and anthropogenic factors affect these trends. We compiled data from 171 public groundwater observation wells within mountain regions across Canada and the United States, for which at least 20 years of monthly data is available. The Mann-Kendall test for monotonic trend revealed that 54% of these wells have statistically significant temporal trends (p < 0.05) over the period of record, of which 69% were negative and therefore indicating overall declining groundwater storage. Wells in the western mountain ranges showed stronger trends (both positive and negative) than the eastern mountain ranges, and higher elevation wells showed fewer negative trends than the low elevation (< 400 m asl) wells (p < 0.05). Correlation, Kruskal-Wallis tests, stepwise multiple linear regression and random forest regression were used to identify factors controlling groundwater trends. Statistical analysis revealed that lower-elevation mountain regions with higher average annual temperatures and lower average annual precipitation have the greatest declines in groundwater storage under climate change. Trends in temperature and precipitation, and ecoregion were also important predictors on groundwater level trends, highlighting geographic differences in how mountain wells are responding to climate change. Furthermore, sedimentary bedrock aquifers showed markedly more negative trends than crystalline bedrock aquifers. The findings demonstrate that the impact of climate change on mountain water resources extends to the subsurface, with important implications for global water resources.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.026 |
| Science and technology studies | 0.001 | 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) | 0.043 | 0.029 |
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".