Atmospheric, soil, surface and groundwater data from the St Denis National Wildlife Area, Saskatchewan, Canada
Bibliographic record
Abstract
The St Denis National Wildlife Area database contains data for the prairie research site, St Denis National Wildlife Research Area, and includes atmosphere, soil, and groundwater. The meteorological measurements are observed every 5 seconds, and half-hourly averages (or totals) are logged. Soil moisture data comprise volumetric water content, soil temperature, electrical conductivity and matric potential for probes installed at depths of 5 cm, 20 cm, 50 cm, 100 cm, 200 cm and 300 cm in all soil profiles. Additional data on snow surveys, pond and groundwater levels, and water isotope isotopes collected on an intermittent basis between 1968 and 2018.The metadata table provides location information, information about the full range of measurements carried out on each parameter and GPS locations that are relevant to the interpretation of the records. **Please note: This dataset is linked to an ESSD paper at https://doi.org/10.5194/essd-11-553-2019. The authors kindly request that you reference this paper in addition to the dataset.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| 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) | 0.026 | 0.013 |
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".