Water level data, Tasiapik Valley, Nunavik, Canada (2014-2021)
Bibliographic record
Abstract
This dataset includes groundwater level data from four wells located on a permafrost mound (lithalsa) and its surroundings, within the Tasiapik Valley, near the village of Umiujaq, Nunavik, Quebec, Canada. The water levels are measured continuously within an unconfined sandy aquifer at the top (1 well; Sommet 1/TOP 1) and the side (2 wells; Côté 1/SIDE 1, Côté 2/SIDE 2) of the permafrost mound. Additionally, the water level is measured in a well (Silt 1/SILT 1) installed in a confining silt layer located below the superficial aquifer. Data were collected from 16 July 2014 – 28 June 2021 with Solinst Levelogger® water level dataloggers. Soils stratigraphy and well construction details are given below. These data support the article Fortier et al. (2023).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.007 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.253 |
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; both teacher heads agree on what is shown here.
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".