Carbon burial over the last four millennia is regulated by both climatic and land use change
Bibliographic record
Abstract
These data are from the publication: Mao, J., Burdett, H. L., McGill, R. A. R., Newton, J., Gulliver, P., & Kamenos, N. A. (2020). Carbon burial over the last four millennia is regulated by both climatic and land use change. Global Change Biology, https ://doi.org/10.1111/gcb.15021 Abstract: Carbon sequestration by sediments and vegetated marine systems contributes to atmospheric carbon drawdown, but little empirical evidence is available to help separate the effects of climate change and other anthropogenic activities on carbon burial over centennial time scales. We used marine sediment organic carbon to determine the role of historic climate variability and human habitation in carbon burial over the past 5071 years. There was centennial-scale sensitivity of carbon supply and burial to climatic variability, with Little Ice Age cooling causing an abrupt ecosystem shift and an increase in marine carbon contributions compared to terrestrial carbon. Although land use changes during the late 1800s did not cause marked alteration in average carbon burial, they did lead to marked increases in the spatial variability of carbon burial. Thus, while carbon burial by vegetated systems is expected to increase with projected climate warming over the coming century, ecosystem restructuring caused by abrupt climate change may produce unexpected change in carbon burial whose variability is also modulated by land use change.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".