An empirical firn-densification model comprising ice lenses
Bibliographic record
Abstract
Abstract In the past, several empirical firn-densification models have been developed fitted to measured density–depth profiles from Greenland and Antarctica. These models do not specifically deal with refreezing of meltwater in the firn. Ice lenses are usually indirectly taken into account by choosing a suitable value of the surface snow density. In the present study, a simple densification model is developed that specifically accounts for the content of ice lenses in the snowpack. An annual layer is considered to be composed of an ice fraction and a firn fraction. It is assumed that all meltwater formed at the surface in one year will refreeze in the corresponding annual layer, and that no additional melting or refreezing occurs in deeper layers. With this assumption, further densification is solely controlled by compaction of the firn fraction of the annual layer. Comparison of modelled and observed depth–density profiles from Canadian Arctic ice-core sites with large melting–refreezing percentages shows good agreement. The model is also used to estimate the long-term surface elevation change in interior Greenland that will result from temperature-driven changes of density–depth profiles. These surface elevation changes reflect a volume change of the ice sheet with no corresponding change of mass, i.e. a volume change that does not influence global sea level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".