MétaCan
Menu
Back to cohort
Record W2148750960 · doi:10.1109/vetec.1994.345411

Relationship between measured 900 MHz complex impulse responses and topographical map data

2002· article· en· W2148750960 on OpenAlexaff
R.L. Kirlin, Peter F. Driessen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAntenna Design and Optimization
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsTerrainImpulse (physics)Impulse responseGeologyMultipath propagationRemote sensingAzimuthContour lineComputer scienceReflection (computer programming)GeodesyMathematicsTelecommunicationsGeometryCartographyGeographyPhysics

Abstract

fetched live from OpenAlex

1 GHz complex impulse response data in mountainous terrain is measured at closely spaced locations, and is processed as data from a synthetic aperture array. Experimental data from linear and crossed arrays with 50 or 100 elements is considered. The direction of arrival for each delayed component is identified, and contour plots of the receiver power at bearings and distances are produced. These contour plots closely match the topography of the region, and clearly indicate that the strongest mountain reflections come from the steepest slopes. These results are used to establish a relationship between the mountain reflection coefficients and the topography, thus making it possible to invert the problem and estimate the impulse response (multipath delay profile) in mountainous terrain directly from topographical map data. Such estimates can help to select cell site locations and antenna configurations to minimize the delay spread.>

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.188
GPT teacher head0.277
Teacher spread0.089 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2002
Admission routes1
Has abstractyes

Explore more

Same topicAntenna Design and OptimizationFrench-language works237,207