Observations of ice surface temperature and thickness in the Baltic Sea
Bibliographic record
Abstract
This paper presents the results of an airborne thermal infrared (TIR) experiment. The data were obtained during 6–9 February 1992 period in the Bay of Bothnia, the northern section of the Baltic Sea, in connection with an ERS‐1 field campaign. Two Aegema model 880 TIR cameras were used with nominal wavelengths at 5 and 10 µm, attached to the front of the helicopter with a Tyler mount. The camera's thermal resolution is 0.2 K; after corrections for atmosphere effects, the surface temperature accuracy is 0.5 K. The spatial resolution of individual images is 70 cm at the 300‐m flight altitude. The measurement programme was successful, producing high‐quality TIR data over ice for two different days, even under difficult weather conditions. Ice temperatures ranged from open water temperatures to 261 K for fast ice conditions on the day on the first flight. The standard deviation of the surface temperature, generally, increased with ice thickness with a value of ∼0.4 K for maximum thickness, and autocorrelation length scales not exceeding a value of 5 m. Generally, all the higher values of standard deviation (>0.7) of surface temperatures were for scenes with mixed ice/open water. The results show that TIR has a substantial ability to classify ice type and thickness when the air temperature is less than 269 K, from open water at the freezing point to thin nilas and thicknesses up to 20–45 cm in the fast ice zone. In addition, a quasi‐steady sea‐ice model is used to provide a physical interpretation of the sea‐ice surface temperatures. The use of the model requires information on the atmospheric surface layer and snow thickness data, together with calibration points. The model worked well when the air temperature was around 260 K or less.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".