Empirical Prediction of Sea Ice Surface Temperature from Surface Meteorological Parameters in Pistolet Bay Northern Newfoundland
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Thermodynamic models of sea ice are important tools for the prediction of the patterns of the seasonal evolution of regional ice concentration, thickness, as well as the flexural and compressive strength of the ice. These models aid in offshore operational planning, decision-making, and structural design in the sectors of marine transport and hydrocarbon development in ice-prone environments. The initialization of vertical ice temperature profiles and the definition of boundary conditions for temperature at the ice surface and base have a significant impact on the evolution of internal ice temperatures in these models and the associated melt, growth, and strength response of the ice. While the ice basal temperature in thermodynamic models is relatively straightforward to define as the freezing temperature of seawater, the ice surface temperature is less certain and is traditionally estimated using a surface energy flux balance. During February 27-29, 2016, six Temperature Acquisition Cables (TACs) and two data loggers were installed on the snow-free land-fast sea ice in Pistolet Bay, northern Newfoundland in Atlantic Canada. The TACs and data loggers recorded vertical ice temperature profiles and surface air temperatures at five-minute intervals. Two co-located tripod-mounted anemometers recorded oneminute surface wind speeds. Dew point temperatures and cloud areal fraction were obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim Reanalysis, and downwelling longwave radiation was inferred from the surface air temperature measurements and cloud cover data. Incoming shortwave radiation was also calculated. In this paper, an alternative robust and more computationally efficient method is presented for solving the ice surface temperature as a linear function of the aforementioned surface meteorological parameters. Subsequently, the linear model is used to establish ice surface temperature boundary conditions in order to demonstrate its use in modeling the evolution of the vertical ice temperature profile as recorded by one of the TACs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it