The fractional land cover estimates from the Boreal-Arctic Wetland and Lake Dataset (BAWLD), 2021.
Bibliographic record
Abstract
The Boreal and Arctic Wetland and Lake Dataset (BAWLD) provides estimates of fractional land cover of 19 land cover classes within 0.5° ×0.5° grid cells. The total area of the BAWLD domain is 25 500 000 kilometers squared (km2), i.e. 17% of the global land surface. The domain includes the boreal and tundra biomes, as well as areas of rocks and glaciers at greater than 50° North (N). The dataset is comprised of 23,469 0.5° ×0.5° grid cells. Each grid cell includes information on the fractional cover of five wetland classes, seven lake classes, three river classes, along with glacier, rockland, tundra, and boreal forest classes. Estimates of land cover fractional extents are based on an expert assessment, and a subsequent extrapolation to the full study region using random forest analysis. The dataset also includes an assessment of the uncertainty of the fractional cover estimates, represented by the 95% high and low estimates for fractional land cover. Each grid cell is further classified as one of fifteen “wetscapes”, which are defined by a characteristic land cover composition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.023 |
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".