A multisite dataset of near-surface soil temperature, active-layer thickness, and soil and vegetation conditions measured in northwestern Canada, 2016-2017
Bibliographic record
Abstract
Quantifying and understanding spatial variation in permafrost conditions at the landscape-scale is important for land use planning and assessing the impacts of permafrost thaw. This report documents detailed field data observed at 110 sites in two areas in northwestern Canada from 2016 to 2017. One area is a northern boreal landscape near Inuvik and the other is a tundra landscape near Tuktoyaktuk. The observations include near-surface soil temperatures (Tnss) at 107 sites, and active-layer thickness, soil and vegetation conditions at 110 sites. The data set includes the original Tnss records, the calculated daily, monthly, and annual averages of Tnss, soil and vegetation conditions at these sites, and photographs taken in the field. This data set will be useful for understanding the spatial heterogeneity of permafrost and validating modelling and mapping products.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".