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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.027 |
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".