Characterising Percolation and Wet-Snow Facies Variability on Devon Island Ice Cap, Nunavut, Canada
Bibliographic record
Abstract
As the Earth’s coldest regions undergo marked changes due to atmospheric warming, so will the surface facies configurations of its glaciers and ice sheets. Their percolation and wet snow zones will expand upwards and occupy more area. The inherent stratigraphic complexity of these zones will then impart greater uncertainty in glacier and ice sheet mass balance estimates derived from traditional stake and pit methods. Using impulse and FM-CW Gound Penetrating Radars, borehole neutron scattering and manual snow stratigraphy measurements, our goal is to better describe the spatial and temporal variability of the percolation and wet-snow facies. Our measurements consider sub-meter to kilometre to inter-facies scale variability. Improved knowledge of such variability has practical significance. First, uncertainties in glacier and ice sheet mass balances remain largely unquantified – unsatisfactory as it concerns documenting relatively small changes over large areas. Second, the retrieval of wide-area mass balance change using elevation changes from repeat airborne and orbital altimetry (e.g., ALTM, ICESat, CryoSat) will require information on snow density, densification and the spatial scale of variability over the altimeter footprint. We suggest that there is a need for continual in situ validation studies over the lifetime of altimeter-based glacier and ice sheet change detection campaigns. * Corresponding author address: Michael N. Demuth, 601 Booth Street, Geological Survey of Canada, Glaciology Section, Ottawa, ON, Canada, K1A 0E8; tel: 613-996-0235; fax: 613-996-5448; email: mike.demuth@nrcan.gc.ca
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".