[Study on the thermal radiation polarization characteristics of ice].
Bibliographic record
Abstract
As an important parameter of the global energy balance, climate, hydrological and ecological model, ice directly affects the energy balance of the earth-atmosphere system, weather and climate. It is of great significance to use the thermal infrared polarization technology to study ice thermal radiation. For the ice monitoring and the impact of global climate change on the ice, studies on ice thermal radiation polarization characteristics were conducted based on the wavelength, detection angle and azimuth angle. The results show that the wavelength has an obvious impact on the ice thermal radiation polarization properties. The polarized radiance of four bands shows that L(CH1) > L(CH3) > L(CH4) > L(CH2) while the polarization brightness temperature shows that T(CH4) > T(CH1) > TCH2 > TCH3. It's better to use the brightness temperature of the third channel than the radiance to study the thermal radiation polarization. The detection angle affects the ice thermal radiation polarization characteristics greatly and there are some differences between the polarization angles. The brightness temperature of ice is the lowest in the detection angle of 10 degrees and the polarization angle of 30 degrees, which are non-accidental factors. These was closely related to ice physical and chemical properties. The degree of ice polarization performance shows that P0 < P40 <P30 < P50 < P10 < P20 and is affected by the detection angle, which is directly related to the differences in the brightness temperature at different polarization angles. The degree of polarization changes with the azimuth angles and plays an important role in ice physical and chemical characteristics monitoring. The impact of azimuth angle on the ice thermal radiation polarization characteristics was not significant. And it is affected by the roughness of the surface, organizational structure and other factors which are direct results of ice emitted radiation at different azimuth angles.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".