Landscape design for soir conservation under land use and climate change
Bibliographic record
Abstract
Soils and landscapes evolve simultaneously. Soil evolution is controlled by redistribution and transformation processes influenced by topographic and climatic parameters, with also a major contribution of management strategies. The perennial landscape features have a strong influence on soil spatial distribution (geometry) and soil genesis. Building landscapes which enhance soil resilience to degradation processes and increase soil services appears as a promising way to adapt to forthcoming climatic and land use evolutions. The presentation aims to synthetize major results from a research program nicknamed Landsoil which focused on the evolution of agricultural soils over medium time scales (decades to centuries) in relation to changing conditions of land use and climate. Precise study of the soil 3D organization in three contrasted landscapes (Brittany, Touraine, Languedoc-Roussillon) enabled to link soil redistribution in space to landscape components (field geometry, hedges or ditches network) and their past evolution. A dynamic and high resolution spatial modeling approach was developed coupling erosion processes and soil organic matter evolution and was calibrated over past evolution using dating techniques (Cs137, C14, OSL). The resulting Landsoil model was afterwards applied in a prospective manner under different scenarios of land use and climate change over the 21th century. Indicators of soil vulnerability and soil resilience were defined and tested by the comparison of several prospective scenarios applied on a same landscape and by comparison of the contrasted landscapes
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".