Global chemical weathering dominated by continental arcs since the mid-Paleozoic
Bibliographic record
Abstract
Abstract Earth’s plate tectonic activity regulates the carbon cycle, and hence, climate, via volcanic outgassing and silicate-rock weathering1,2,3. Mountain building, arc-continent collisions, and clustering of continents in the tropics have all been invoked as controlling the weathering flux4,5,6, with arcs also acting as a major contributor of carbon dioxide (CO2) to the atmosphere7. However, these processes have largely been considered in isolation when in reality they are all tightly coupled. To properly account for the interactions between these processes, and the inherent multi-million-year time lags at play in the Earth system, we need to characterise their complex interdependencies. Here we analyse these interdependencies over the past 400 million years, using a Bayesian network to identify primary relationships, time lags and drivers of the global chemical weathering signal. We find that the spatial extent of continental volcanic arcs — the fastest-eroding surface features on Earth — exerts the strongest control on global chemical weathering fluxes. We find that the rapid drawdown of CO2 tied to arc weathering stabilises surface temperatures over geological time, contrary to the widely held view that this stability8 is achieved mainly by a delicate balance between weathering of the seafloor and the continental interiors.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".