"Basal conditions of Denman Glacier from glacier hydrology and ice dynamics modeling" datasets
Bibliographic record
Abstract
Data for hydrology and inversions in "Basal conditions of Denman Glacier from glacier hydrology and ice dynamics modeling" Denman_hydrology.mat - hydrology outputs from GlaDS. SchoofNoiseX.mat - stressbalance models with perturbed meshes, Schoof friction law, and GlaDS effective pressure extrapolated to the ISSM domain SchoofCapX.mat - stressbalance models with Schoof friction law, and GlaDS effective pressure extrapolated to the ISSM domain capped at X% of overburden SchoofPrescribed.mat - stressbalance model with Schoof friction law and prescribed effective pressure BuddPrescribed.mat - stressbalance model with Budd friction law and prescribed effective pressure SchoofPrescribedExtrapolation.mat - stressbalance model with Schoof friction law, GlaDS effective pressure for the GlaDS domain and prescribed effective pressure for the rest of the grounded domain BuddPrescribedRigidity.mat - stressbalance model with Budd friction law and prescribed effective pressure, ice rigidity is inverted for after the Budd friction coefficient SchoofPrescribedRigidity.mat - stressbalance model with Schoof friction law and prescribed effective pressure, ice rigidity is inverted for after the Schoof friction coefficient BuddMain - The main stressbalance model with Budd friction law and inverted rigidity used in the paper SchoofMain - The main stressbalance model with Schoof friction law and inverted rigidity used in the paper
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".