Field observations of ice melt and decay to constrain lake ice model parameterizations
Bibliographic record
Abstract
Field measurements within ice covers during the melting period are scarce, often constrained by safety concerns leading to uncertainty in model parameterizations of ice melt. To address these limitations, a Floating Research Station (FRS) was constructed in a small subarctic lake near Yellowknife, Canada to monitor ice processes year-round. Using the FRS, the objective of this study was to delineate key processes influencing ice melt and decay through evaluating heat budget components over three melt seasons: April 1- May 31 of 2023, 2024, and 2025. Our results show that the decay process was both thermally and mechanically driven, with mechanical decay from ice collapse occurring at internal ice porosities of 0.31-0.35 and accounting for 24 - 48% of total ice loss. Thermal melt at the surface (0.6 - 2.1 cm d -1 ) was driven by surface heat absorption (13-160 W m -2 ) and losses via net longwave fluxes (-95.2 - -6.6 W m -2 ). Bottom melt (0.1- 0.6 cm d -1 ) was caused by modest mean daily water-to-ice heat fluxes (< 15 W m -2 ). Melt season lengths varied between 35-49 days, and break-up dates up to 16 days, and were dependent on air temperatures, albedo, and net insolation. Results from this study can be used to improve and constrain melt period parameterizations in ice models while providing needed validation and calibration data in northern, high-latitude environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".