Data for: Contribution of standardized indexes to understand groundwater level fluctuations in response to meteorological conditions in cold and humid climates
Bibliographic record
Abstract
The dataset contains all the data used in the associated article “Contribution of standardized indexes to understand groundwater level fluctuations in response to meteorological conditions in cold and humid climates” by Dubois and Larocque (2024). The dataset contains (1) the shapefile of the study area, (2) the metadata of the 152 wells used in the analyses, (3) the pretreated monthly time series of groundwater levels, (4) the aggregated monthly precipitation for each geological region, (5) the aggregated daily temperature for each geological region. The period covered corresponds to the 2000-2022 period and the geological regions corresponds to the Appalachians north, Appalachians south, St. Lawrence Platform, Canadian Shield north, and Canadian Shield south. Groundwater time series and interpolated climate data were provided by the Quebec Ministry of the Environment (Ministère de l’Environnement et de la Lutte contre les changements climatiques, de la Faune et des Parcs - MELCCFP). The study area is located between 46°N and 52°N in the province of Quebec (Canada; 980 000 km2). The study area includes three geological provinces, the metasedimentary Appalachian Province, the sedimentary basin of the St. Lawrence Platform, and the metamorphic Grenville Province (named the Canadian Shield, its overlying region). The three geological units were further subdivided using the 47°N line as a subjective and approximative line dividing the warmer, southern Quebec (temperature>4°C) and the colder, northern Quebec (average temperature<4 °C). The resulting geological regions and sub-regions are the Appalachians North, the Appalachians South, the St. Lawrence Platform, the Canadian Shield North, and the Canadian Shield South.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.029 | 0.023 |
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".