An automatic identification of clutter and anomalous propagation in polarization-diversity weather radar data using neural networks
Bibliographic record
Abstract
Radar polarization measurements have mostly been used to improve rainfall estimation and hydrometeor characterization. The authors extend the use of such measurements to the problem of ground clutter recognition, including the case when this problem is associated with anomalous propagation of the electromagnetic wave. They present a methodology used for recognizing both clutter and meteorological targets. The methodology is based on the knowledge of the scattering properties of the targets, as provided by the polarization measurements and the use of the neural network approach that performs the classification. The results show that if circular polarization is used, the circular depolarization ratio and the degree of polarization are good discriminators of clutter and nonclutter. They have used data from the Alberta polarization diversity radar to build an automatic decision process using a feedforward neural network. After they trained the neural network, they tested the classifier for two common clutter situations: when there is an electromagnetic wave anomalous propagation and when targets from rain are mixed with the clutter close to the radar.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".