Global land use/land cover and soils relations from 850 to 2015 (LUSoils v.1)
Bibliographic record
Abstract
This dataset links human land use and land cover types from the Land-Use Harmonization (LUH2) dataset (Lawrence et al., 2016) to four hydrologic soil groups from 850 to 2015 derived from the SoilGrids250m soils dataset (Hengl et al., 2017). These groups represent sandy soils (hydrologic group A) consisting of texture classes sand, sandy loam, and loamy sand; silty soils (hydrologic group B) consisting of loam, silty-loam, and silt; a mixed sand-silt-clay soils (hydrologic group C); and clayey soils (hydrologic group D) represented by clay, sandy-clay, clay-loam, silty-clay, and silt-clay-loam texture classes from the SoilGrids250m dataset. This dataset makes it possible to better link LULCs to soil types typically used for these activities potentially improving the simulation of water, energy and biogeochemical processes in Earth System Models. Additionally, it lays the foundation for simulating LULC impacts on soils that have different vulnerabilities and responses to human uses of soils.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.075 | 0.150 |
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".