Contribution of standardized indexes to understand groundwater level fluctuations in response to meteorological conditions in cold and humid climates
Bibliographic record
Abstract
Understanding the fluctuations in groundwater levels in response to meteorological conditions is challenging, especially given the slow transit time associated with groundwater reservoirs and the short duration of time series for groundwater levels. Nevertheless, this knowledge is crucial for water resource management, especially given that global warming will drastically impact the hydrological dynamics in cold and humid climates. The objective of this work was to quantify how standardized indexes contribute to understanding groundwater level fluctuations in response to meteorological conditions in cold and humid climates and with short time series (10 to 23 years). The relationships between the standardized precipitation index (SPI), standardized temperature index (STI), global climate indexes, and standardized groundwater index (SGI) were analyzed. The reactivity of groundwater levels was examined between 2000 and 2022 using groundwater level measurements from 152 wells located between 46°N and 52°N in the province of Quebec (Canada). The results showed that the available time series were sufficient to provide new insights into the role of precipitation and temperature on groundwater fluctuations, demonstrating the usefulness of the indexes. One of the main contributions of this study was that hydrogeological systems in cold and humid climates go through an annual reset due to the prolonged freezing period. This annual reset was one of the drivers isolating year-to-year hydrogeological conditions, contributing to short-duration droughts.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".