Ice streaming in the Laurentide Ice Sheet: A first comparison between data‐calibrated numerical model output and geological evidence
Bibliographic record
Abstract
Despite the importance of rapidly‐flowing ice streams to ice sheet mass balance, their incorporation into numerical ice sheet models is a major scientific challenge. This introduces large uncertainties in model output and inhibits a more complete understanding of the role of ice streams in overall ice sheet stability. Recent computational advances have enabled more realistic representations of ice streaming but few studies have attempted to compare model output against known locations of ice streams. This paper compares predictions of ice streaming derived from a large ensemble analysis of a Glacial Systems Model of the Laurentide Ice Sheet against independent geological evidence compiled from previously published studies. Although the precise dating of paleo‐ice stream locations is problematic, our analysis includes comparisons at six different time‐steps (18 to 10 cal ka BP) during deglaciation. Results indicate that the model is successful in predicting all of the major marine‐terminating ice streams but there is mixed success in simulating terrestrial ice streams in the right place and at the right time, which is vital in guiding future model development. The model also reveals that whilst some ice streams persist throughout deglaciation the focus of mass loss associated with ice streaming switches through time with dynamic changes in ice stream catchments and tributaries. This implies that major changes in ice stream activity are to be expected in a deglaciating ice sheet, with important implications for contemporary ice sheet dynamics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".