Ablation patterns of snow cover over smooth first‐year sea ice in the Canadian Arctic
Bibliographic record
Abstract
Abstract We examine the temporal evolution of snow distribution over first‐year sea ice from late winter to the period when melt ponds form. Our objectives are to model snowmelt over first‐year sea ice and investigate how melt rate affects the transmission of photosynthetically active radiation (PAR). These objectives are a subcomponent of a larger initiative to examine the coupling of physical and biological systems within a changing ocean–sea‐ice–atmosphere system (Iacozza J, Barber. 2000. C‐ICE 2000 Field Summary. CEOSTEC‐2000‐12‐01. CEOS, University of Manitoba). Results indicate that the melt rate for the snow cover is non‐uniform both spatially and temporally, with decreasing rates for increasing snow depths. At depths greater than approximately 16 cm the melt rate is fairly consistent (approximately 3·5% day−1). The melt rate was best modelled using a quadratic equation, accounting for 71·5% of the variation in the melt rate. This melt‐rate equation was used to estimate the evolution of a statistical snow surface based on Iacozza and Barber (1999. Atmosphere–Ocean 37: 21–51) and to examine the transmission of PAR through the melt period. Analysis of the PAR transmission indicated that, as the melt season progressed, a greater amount of PAR is transmitted through the snow and ice and that this transmission is controlled by the ablation rate of different thicknesses of snow. The variation in PAR over the study area also increased as the melt season progressed. Copyright © 2001 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".