Continental constriction and oceanic ice‐cover thickness in a Snowball‐Earth scenario
Bibliographic record
Abstract
Ice flow over a Snowball ocean was shown in recent years to be capable of very effectively homogenizing ice thickness globally. Previous studies all used local or one‐dimensional global (latitude‐only) models, formulated in a way that is difficult to extend to two‐dimensional global configuration. This paper uses a two‐dimensional global ice flow model to study the effects of continental constriction on ice flow and ice thickness in a Snowball‐Earth scenario using reconstructed Neoproterozoic landmass configuration. Numerical simulations and scaling arguments are used to show that various configurations of continents and marginal seas which are not represented by one dimensional models lead to large ice thickness variations, including narrow areas between sub‐continents and marginal seas whose entrance is constricted. This study ignores many known important factors such as thermodynamic, optical effects, dust and dust transport, and is therefore meant as a process study focusing on one specific effect, rather than a realistic simulation of Neoproterozoic ice thickness. The model formulation developed here generalizes and extends previous results in several ways, including the introduction of corrections due to spherical coordinates and lateral geometry. This study is therefore a step toward coupling Snowball ice flow models to general circulation ocean and atmospheric models and allowing a more quantitative simulation of Neoproterozoic Snowball ice thickness.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".