Resolving the Influence of Ice Stream Instability on Postglacial Relative Sea-Level Histories: the case of the St Lawrence River Channel Ice Stream
Bibliographic record
Abstract
A challenge to understanding Late Quaternary glaciation history is the mechanism(s) responsible for the asymmetry in an individual glaciation cycle between the slow pace of glaciation and the more rapid pace of deglaciation (e.g., Broecker and Van Donk, 1970). It is increasingly clear that a major contributor to the rate of global deglaciation is the instability of marine terminating ice streams. Recent analyses by Velay-Vitow et al. (2020) suggest that these instabilities were often triggered by ocean tides of anomalously high amplitude. Examples include the Hudson Strait Ice Stream responsible for Heinrich Event 1 (H1) and the Amundsen Gulf Ice Stream. Here, we analyse the instability of the Laurentian Channel and St Lawrence River Channel ice stream system. Our analysis begins with the recognition of highly significant misfits of up to 60 m at ~9,000 calendar years ago between deglacial relative sea-level histories inferred by Vacchi et al. (2018) at sites along the St Lawrence River Channel and those predicted by the ICE-6G_C (VM5a) and ICE-7G_NA (VM7) models of the Glacial Isostatic Adjustment process. We suggest that these disagreements between models and data may be due to the St Lawrence River Channel ice stream becoming unstable during the deglaciation of the Laurentide Ice Sheet (LIS) due to the hypothesized tidal mechanism for ice stream destabilization. We investigate a sequence of scenarios designed to provide a best estimate of the timing of this event. Since this ice stream penetrated deeply into the interior of the LIS and was connected to the Laurentian Channel ice stream, the instability of the latter was required in order for destabilization of the St Lawrence River channel ice stream to be possible. We explore the consistency of the implied sequence of events with the observational constraints.
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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 | 0.001 | 0.004 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".