MATLAB code for firn thermodynamic and hydrological modelling
Bibliographic record
Abstract
This repository contains MATLAB source code for a model of glacier surface energy balance, coupled with a subsurface (snow and firn) thermodynamic and hydrological evolution. The default configuration of the subsurface model is for a 35-m firn column, with 0.1-m thick layers from 0-0.6 m, 0.2-m thick layers from 0.6-2 m, 0.4-m thick layers from 2-10 m, and 1-m thick layers below that. The model requires forcing from mean daily automatic weather station, climate model, or climate reanalysis data, with the default inputs being: minimum, mean, and maximum daily air temperature, mean daily relative or specific humidity, wind speed, air pressure, and longwave radiation, and mean and maximum daily incoming incoming shortwave. The model internally calculates snow albedo and snow surface temperature (hence, outgoing longwave radiation), as well as conductive heat flux to the snow surface, based on temperature gradients in the top three layers of the subsurface snow/firn model. See the referenced papers for further details on the model physics and parameterizations. The readme file with the code repository includes additional details and references.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.181 | 0.164 |
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".