Bibliographic record
Abstract
Propagation analysis and modeling is critical for radio systems design, but remains a challenge for most through-vegetation situations, including forests. Transmission through such inhomogeneous mixed media is complicated by the many different propagation mechanisms and the complexity of the randomness. This means that accurate, purely physics-based analysis is unlikely to be practical (conveniently computed), and similarly, that practical, purely random modeling is unlikely to be accurate. Through-vegetation propagation models, including the standard radiative energy transfer (RET), are not very accurate in the sense that the uncertainty can be tens of dB, and this seems to be an accepted limitation for vegetation. A simpler propagation model, which maintains or improves accuracy, but keeps a reasonable association with the physics, would be insightful. This paper discusses such a model. It comprises two parallel transmission mechanisms: direct transmission through a succession of trees, which is modeled by a simple linear transmission line; and transmission across the forest top, which is modeled by simplified multiple-edge diffraction. The model is examined using recently-published experiments over a long path-length. It is demonstrated that this two-mechanism model can provide an accurate fit to the dual-slope profile of through-forest propagation over a long distance which is not possible with the RET model.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".