Linking Global Land Use/Land Cover to Hydrologic Soil Groups From 850 to 2015
Bibliographic record
Abstract
Abstract The characteristics of soils in part determine human‐induced land use and land cover (LULC) change, and together, soil properties and LULC directly impact global water, energy, and biogeochemical cycles. Plant health and the exchange of energy, water, and biogeochemical components at the surface interface are partly controlled by soil properties. Different soil types modify vegetation responses to existing climate forcings, and each soil type also responds differently to the same land‐use practice. Currently, Earth system models often use single soil columns with averaged properties. This leads to uncertainties in assessing LULC impacts. To improve the estimates of land surface change in Earth System Models, we link an existing LULC data set to four hydrologic soil groups from 850 to 2015, based on demonstrated soil preferences for eight LULCs under current conditions. We conclude that humans prefer hydrologic soil groups in order from B, D, C, to A. This ranking was applied to construct the history of LULC on each soil type at the 0.5° grid resolution. Results primarily distribute croplands to hydrologic soil group B in 850, while hydrologic soil group A has the most undisturbed area. Over time, good soils (hydrologic soil groups B and D) experience increased use for cropland areas, while poor soils (hydrologic soil groups C and A) are occupied predominantly by increasing areas in grazing land and secondary nonforests. The results provide better land surface characteristics to improve Earth systems modeling.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".