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Record W1671297897 · doi:10.1109/igarss.1995.520471

Modeling techniques for curved and tapered branches at different microwave frequencies

2002· article· en· W1671297897 on OpenAlexaboutno aff
Roger H. Lang, Robert J. Landry, R. Cacciola

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil Geostatistics and Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsTaperingCurvatureCylinderOpticsMicrowaveGeometryRadiation patternRadiationMathematicsPhysicsComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.602

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.034
GPT teacher head0.222
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2002
Admission routes1
Has abstractyes

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