How well is firn densification represented by a physically based multilayer model? Model evaluation for Devon Ice Cap, Nunavut, Canada
Bibliographic record
Abstract
Abstract Evaluation of how accurately snowpack models can capture hydrological processes in firn is critical to determining how well they will simulate future glacier mass-balance changes. Here we compare simulations using the Crocus snowpack model with the evolving firn stratigraphy recorded in 14 cores drilled at four elevations in the accumulation zone of Devon Ice Cap, Canada, during the 2004–12 period of rapid summer warming. Simulations were forced with a combination of surface observations and reanalysis data. Simulations resulted in positive model bias in near-surface density, and negative bias in density at depth compared to observations. Results point to the importance of incorporating heterogeneous percolation in firn in order to improve the representation of meltwater flow, better reproduce observed firn density and temperature profile evolution, and improve simulations of glacier mass balance during periods of climate warming.
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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".