Evidence for rapid weathering response to climatic warming during the Toarcian Oceanic Anoxic Event
Bibliographic record
Abstract
Abstract Chemical weathering consumes atmospheric carbon dioxide through the breakdown of silicate minerals and is thought to stabilize Earth’s long-term climate. However, the potential influence of silicate weathering on atmospheric pCO2 levels on geologically short timescales (103–105 years) remains poorly constrained. Here we focus on the record of a transient interval of severe climatic warming across the Toarcian Oceanic Anoxic Event or T-OAE from an open ocean sedimentary succession from western North America. Paired osmium isotope data and numerical modelling results suggest that weathering rates may have increased by 215% and potentially up to 530% compared to the pre-event baseline, which would have resulted in the sequestration of significant amounts of atmospheric CO2. This process would have also led to increased delivery of nutrients to the oceans and lakes stimulating bioproductivity and leading to the subsequent development of shallow-water anoxia, the hallmark of the T-OAE. This enhanced bioproductivity and anoxia would have resulted in elevated rates of organic matter burial that would have acted as an additional negative feedback on atmospheric pCO2 levels. Therefore, the enhanced weathering modulated by initially increased pCO2 levels would have operated as both a direct and indirect negative feedback to end the T-OAE.
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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 | 0.001 | 0.001 |
| 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".