Experimental investigation of wave-forced heat convection across water-permafrost boundaries
Bibliographic record
Abstract
Arctic permafrost coastlines are retreating faster as climate warming intensifies. Accurate modelling of the thermomechanical process is hindered by a lack of direct measurement of heat flux or heat transfer coefficients ( h w ) at the water-permafrost interface. This study presents the first, direct laboratory measurements of wave-induced convective heat transfer coefficients. In twelve wave-flume experiments, artificial permafrost samples were exposed to air, still water, and irregular waves (0.02–0.04 m height; 0.8–1.2 s period). Embedded resistance temperature detectors tracked temperature changes at high spatial and temporal resolution, allowing for precise heat flux and heat transfer coefficient calculations. Under wave action, thaw-front advanced rapidly into the permafrost blocks at about 160–350 mmh −1 compared to 3.24 mmh −1 in air and 50.14 mmh −1 in still water. Similarly, heat transfer coefficient ranged from 459 to 1210 Wm −2 K −1 in wave tests, significantly exceeding those for still water (~165 Wm −2 K −1 ) and air exposure (~4.4 Wm −2 K −1 ) tests. Heat flux correlated most strongly with wave height; higher ice content slowed thawing but did not evidently affect h w magnitude. A novel empirical model was developed that pioneers the linking of h w to surf similarity and dimensionless wave height and period. With strong predictive performance (R 2 = 0.89, RMSE = 81.1 Wm −2 K −1 ), the model provides a practical, experimentally validated tool for specifying h w in coastal permafrost erosion models, eliminating the reliance on parameter tuning required by previous analytical approaches. Overall, this study demonstrates the critical role of waves in heat delivery to permafrost coastlines.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".