Bibliographic record
Abstract
Bistatic Doppler radar networks have become in the last five years a viable and inexpensive alternative to multiple-Doppler networks. Operational experience with a bistatic network at McGill University showed many cases in which data quality seemed heavily affected. Study of those situations suggested sidelobe contamination from the transmitter antenna pattern to be the principal cause. To confirm these findings a sidelobe simulation model (SISI model) was constructed. Comparison between simulations and actual data showed a good reproduction of the observed effect. It is also shown that this effect may have damaging consequences in Doppler fields in both convective and stratiform precipitation events. An index of contamination that can be obtained either with the SISI model or directly using the reflectivity bistatic data is introduced to detect areas of low quality data. Recommendations for the effective use of bistatic data are presented. These findings are taken into account when the optimization of the layout of a bistatic network is analyzed. Sidelobe contamination was found to be a serious problem irrespective of the receiver's location. More than one passive receiver increases the extent of the dual Doppler area but unfortunately does not significantly reduce the problem of sidelobe contamination within a predetermined area. A rule-of-thumb for the deployment of a bistatic network is presented. Some suggestions for improvements of the network are given.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".