Snow and ice facies variability and ice layer formation on Canadian Arctic ice caps, 1999–2005
Bibliographic record
Abstract
Time series of enhanced resolution data from the SeaWinds scatterometer aboard QuikScat were used to map the distribution of snow and ice surface facies and ice layer formation in the percolation zone on ice caps in the Queen Elizabeth Islands during the period 1999–2005. Iterative Self‐Organizing Data Analysis classification of the mean postfreeze‐up biweekly average σ0 signal for the 7‐year period resulted in the delineation of four snow and ice surface facies (interpreted as the percolation, saturation, superimposed ice, and glacier ice zones). Analysis with National Centers for Environmental Prediction/National Center for Atmospheric Research Reanalysis reveals that changes in geopotential height in the troposphere (700, 500, and 300 hPa) and air temperature (700 hPa level) are positively (negatively) correlated with area changes in the glacier ice (percolation and saturation) zones and changes in facies boundary elevation. The change in biweekly‐averaged backscatter following the freeze‐up periods between successive autumns was used to map changes in the distribution of ice layers formed by meltwater percolation and refreezing in the snowpack within the percolation zone. Strongly positive air temperature anomalies at the 700 hPa level in 2001 and 2005 are consistent with extensive ice layer formation in the percolation zones of all ice caps. Such large interannual changes in ice layer formation are likely associated with large changes to the density profile of the snowpack and may be associated with surface elevation changes that are unrelated to changes in surface mass balance.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".