Bibliographic record
Abstract
Abstract. The column thermodynamics package (Icepack v1.1.0) of the Community Ice Code (CICE) version 6 is used to reproduce observations from two Ice Mass Balance (IMB) buoys co-deployed in the landfast ice close to Nain (Labrador) in February 2017. A new automated surface retrieval algorithm is used to determine the ice thickness and snow depths from the measured vertical temperature profiles. The buoys recorded heavy snow precipitation over relatively thin ice, negative ice freeboards and delayed snow flooding. Icepack simulations are produced to evaluate the performance of the Bitz and Lipscomb (1999) thermodynamics used in the Environment and Climate Change Canada (ECCC) ice-ocean systems and to investigate the improvements associated with the use of mushy layer physics. Results show that the Bitz and Lipscomb (1999) scheme produces a smooth thermodynamics growth that fails to capture the observed variability in bottom sea ice congelation rates. The mushy layer physics produces similar temperature profiles but better captures the variability in congelation rates at the ice bottom interface, with periods of rapid ice growth that coincide with IMB observations. Large differences are also found associated with the snow-ice parameterization: the volume of snow-ice formed during flooding is largely underestimated when using a mass conserving snow-formation scheme, but largely improved when using the mushy layer parameterization in which sea-water is filling the porosity of the snow layer. Both schemes are however unable to reproduce the delayed snow-ice formation, as they rely on the hydrostatic balance and do not allow for negative freeboards. This calls for added brine fraction or ice porosity dependencies in the snow-ice parameterizations.
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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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.014 | 0.012 |
| Insufficient payload (model declined to judge) | 0.313 | 0.239 |
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".