Human and climate global-scale imprint on sediment transfer during the Holocene
Bibliographic record
Abstract
C ages to assess the relative changes in lake-watershed erosion rates over the last 12,000 y. Estimated soil erosion dynamics were then complemented with land cover reconstructions inferred from 43,669 pollen samples and with climate time series from the Max Planck Institute Earth System Model. Our results show that a significant portion of the Earth surface shifted to human-driven soil erosion rate already 4,000 y ago. In particular, inferred soil erosion rates increased in 35% of the watersheds, and most of these sites showed a decrease in the proportion of arboreal pollen, which would be expected with land clearance. Further analysis revealed that land cover change was the main driver of inferred soil erosion in 70% of all studied watersheds. This study suggests that soil erosion has been altering terrestrial and aquatic ecosystems for millennia, leading to carbon (C) losses that could have ultimately induced feedbacks on the climate system.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".