Carbon cycle implications of terrestrial weathering changes since the last glacial maximum
Bibliographic record
Abstract
We examine the importance of the rock weathering feedback mechanism during the last deglacial period (∼16 000–4000 BCE) using an Earth system model of intermediate complexity (the University of Victoria Earth System Climate Model (UVic ESCM)) with four box-model parameterizations of terrestrial weathering. The deglacial climate change is driven by changes in orbital parameters, ice core reconstructions of atmospheric CO2 variability, and prescribed removal of continental ice sheets. Over the course of the 12 000 year simulation period, increases in weathering provide a mechanism that slowly removes CO2 from the atmosphere, in opposition to the observed atmospheric CO2 increase during this period. These processes transfer both carbon and alkalinity to the ocean, the combination of which results in as much as a 1000 Pg C increase in total ocean carbon, relative to a control simulation with constant weathering. However, the rapid expansion of northern hemisphere vegetation introduces a significant uncertainty among the weathering parameterizations. Further experiments to test the individual impacts of weathering dissolved inorganic carbon and alkalinity fluxes on ocean biogeochemistry suggest that the worldwide distribution of rock types and the ratio of carbonate to silicate weathering may be crucially important in obtaining an accurate estimate of changes in global weathering rates.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".