Bibliographic record
Abstract
Abstract. Models of subglacial drainage and of cavity formation generally assume that the glacier bed is pervasively hydraulically connected. A growing body of field observations indicates that this assumption is frequently violated in practice. In this paper, I use an extension of existing models of steady state cavitation to study the formation of hydraulically isolated, uncavitated low-pressure regions of the bed, which would become flooded if they had access to the subglacial drainage system. I also study their natural counterpart, hydraulically isolated cavities that would drain if they had access to the subglacial drainage system. I show that connections to the drainage system are made at two different sets of critical effective pressure, a lower one at which uncavitated low-pressure regions connect to the drainage system, and a higher one at which isolated cavities do the same. I also show that the extent of cavitation, determined by the history of connections made at the bed, has a dominant effect on basal drag while remaining outside the realm of previously employed basal friction laws: Changes in basal effective pressure alone may have a minor effect on basal drag until a connection between a cavity and an uncavitated low-pressure region of the bed is made, at which point a drastic and irreversible drop in drag occurs. These results point to the need to expand basal friction and drainage models to include a description of basal connectivity.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.023 | 0.012 |
| Insufficient payload (model declined to judge) | 0.251 | 0.174 |
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".