Quantification of the Effects of Preprocessing Filters on the Performance of GNSS-R Based Sea Ice Detection
Bibliographic record
Abstract
This study examines the influence of preprocessing techniques on the performance of Global Navigation Satellite System Reflectometry (GNSS-R) based sea ice detection. Preprocessing techniques evaluated in this study include precipitation screening and SNR thresholding. SNR thresholds are tested with −3dB, 0 dB, +3 dB, and no threshold configurations. Precipitation screening is tested in three scenarios, containing all data, precipitation free data, and only precipitation contaminated data. Performance impact is determined by recording the sea ice detection accuracy, precision, and recall using the preprocessed datasets. The binary classification is conducted using two models: a histogram thresholding approach and a Bayesian approach. From these tests it was found that the inclusion of precipitation contaminated data did not result in an appreciable change to classification accuracy, precision, or recall. For SNR thresholding, classifcation accuracy peaks for the histogram thresholding approach when a SNR threshold of −3dB or 0 dB is applied. For the Bayesian approach, peak accuracy is achieved with a −3 dB threshold. These results indicate that an SNR threshold of < 0 dB results in superior classification accuracy and that precipitation may be considered negligible for sea ice detection purposes.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".