A carbon cycle coupled climate model of Neoproterozoic glaciation: Explicit carbon cycle with stochastic perturbations
Bibliographic record
Abstract
[1] It has been suggested that a negative climate feedback may have operated during the Neoproterozoic Era as a consequence of the existence of a massive oceanic pool of dissolved organic carbon (DOC). As climate cooled so as to induce intense glaciation, the drawdown of oxygen into the Neoproterozoic ocean would have been enhanced because of the temperature dependence of the solubility of oxygen in seawater. Such increasing ventilation would have enhanced DOC remineralization, thus increasing the content of dissolved inorganic carbon (DIC) in the ocean. CO2 concentration in the atmosphere increases rapidly with DIC, thereby inhibiting further climate cooling. The model employed to illustrate the resulting climate dynamical behavior was an idealized one in which stochastic influence was assumed to be absent. However, such influence is expected to exist due to the action of processes that are not explicitly included in the model. Furthermore, the paleogeography assumed for the purpose of the published analyses was more appropriate to the Marinoan glaciation than to the earlier Sturtian event. In this paper, we fully investigate the stability of the system represented by the carbon cycle coupled climate model for both the Marinoan and Sturtian continental configurations and in the presence of stochastic perturbations. It is found that the hysteresis predicted by the ice sheet coupled model is sensitive to both continental configuration and to the strength of the negative feedback which arises due to carbon cycle coupling. Nevertheless, the very low frequency cyclic glaciation process predicted by the initial version of the model is found to persist in the presence of noise of significant amplitude. However, this cyclic behavior may be arrested entirely if the glaciation process were to result in insufficient alkalinity being delivered to the ocean basins. In this case the system would be expected to execute only a single excursion into and escape from the glacial state.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".