Application of parabolic equation methods to HF propagation in an Arctic environment
Bibliographic record
Abstract
This paper demonstrates the usefulness and flexibility of parabolic equation methods when applied to high frequency (HF) propagation calculations over cliffs and in realistic arctic environments, which are required for estimating the performance of High Frequency Surface Wave Radar (HFSWR). All calculations are performed using a modified version of the TERPEM (TERrain Parabolic Equation Model) software package. This software has been tested extensively through comparisons with other models. As an example, a comparison between data generated with TERPEM and data published in the literature is shown. Calculations performed for four cliff heights (0, 102, 300, 498 m) show that the cliff does not significantly affect the propagation of HFSWR signals at the ranges of interest. Site-specific calculations performed for realistic arctic conditions (cold, low-salinity water covered with broken sea ice and snow) show that HFSWR propagation can be equal to or better than that for ice-free conditions over a significant range for a significant portion of the year (/spl sim/3 or 4 months). Based on these results, and other considerations related to sea clutter, ionospheric clutter, and man-made noise, it is concluded that useful operation of HFSWR at specific sites in the arctic should be feasible for a significant portion of the year.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".