Modeling techniques for curved and tapered branches at different microwave frequencies
Bibliographic record
Abstract
The effects of modeling the curvature and tapering of branches for various microwave frequencies are examined. The forest models that are currently being used represent branches as perfect dielectric cylinders. However actual branches are composed of segments decreasing in diameter along the branch length. Adjacent branch segments also have slightly different angles causing branch curvature. In this study, each branch is represented as a collection of cylindrical segments whose sizes and positions are known. The data for the calculations are obtained from actual branch measurements of red pine and jack pine trees using the "tree vectorization" technique which has been developed at the Canada Centre for Remote Sensing. For a given frequency and incidence angle, a curved and tapered branch can be approximated by an equivalent cylindrical branch. The equivalent cylinder can be determined by choosing the size and orientation so that the radiation patterns match closely. For curved branches, the radiation pattern at typical incident angles is calculated by adding the contributions from each branch segment coherently. It is observed that at low frequencies, the parts of the branch with thinner segments do not contribute to scattering. Therefore, the resulting equivalent cylinder is represented by the thicker part of the branch which is closer to the trunk.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".