Data for: Permafrost thaw induces short term increase in vegetation productivity in northwestern Canada
Bibliographic record
Abstract
This dataset contains active layer thickness and normalized difference vegetation index (NDVI) data for 135 permafrost monitoring sites located across a 10° latitudinal transect of the Northwest Territories, Canada. Included are two .csv files that contain the yearly active layer thickness and NDVI values for each site and the rate of change through time for both of those variables at each site. The rates of change were divided into early and late time periods (Early, 1984 to 2000; Late, 2001 to 2019). Additional site characteristics and climatic variables are included in the data files. Active layer thickness data was derived from ground thermal and thaw tube data that was collected by the Geological Survey of Canada across the network of permafrost monitoring sites. An R script outlining the statistical analyses for the publication “Permafrost thaw induces short term increase in vegetation productivity in the northwestern Arctic-Boreal” is included, as well as a text file which includes the code used to calculate NDVI from a collection of Landsat images processed in Google Earth Engine.
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 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 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 | 0.013 |
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".