Total mercury, methylmercury, nitrogen, carbon, hydrogen, and sulfur concentrations in a degrading lithalsa field near Kangiqsualujjuaq (Nunavik, Canada)
Bibliographic record
Abstract
This dataset contains the data used for the scientific article " Methylmercury concentrations in a degrading lithalsa field: effects of thermokarst development and revegetation", submitted to Science of the Total Environment (Cardinal et al., 2025, submitted). Samples were collected in September 2022 from a degrading lithalsa field at the margin of Tasialuk Lake, Kangiqsualujjuaq, Nunavik, Canada. The dataset includes analyses of soil total mercury (THg), methylmercury (MeHg), nitrogen (N), carbon (C), hydrogen (H), sulfur (S), loss on ignition, grain size distribution, and key environmental variables associated with the soil samples. Additionally, surface water samples from thermokarst ponds were analyzed for filtered and unfiltered THg and MeHg. Soil samples were collected at depths of 10, 20 and 30 cm from different geomorphic units, including degrading lithalsas, rim ridges, and thermokarst ponds at various stages of revegetation. Vertical profiles were also collected from a lithalsa (390 cm deep, with permafrost from 115–390 cm), a revegetated thermokarst pond (60 cm deep), and a rim ridge (90 cm deep). Recorded environmental parameters include water table depth, thaw depth, wetness, and organic matter thickness.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".