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Record W2964186498 · doi:10.5515/kjkiees.2019.30.3.223

Analysis of Abnormal Path Loss in Jeju Coastal Area Using Duct Map

2019· article· en· W2964186498 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Korean Institute of Electromagnetic Engineering and Science · 2019
Typearticle
Languageen
FieldEngineering
TopicRadio Wave Propagation Studies
Canadian institutionsRoyal Canadian Military Institute
Fundersnot available
KeywordsAtmospheric ductDuct (anatomy)Environmental scienceAltitude (triangle)Remote sensingMeteorologyGeologyGeodesyGeographyMathematicsGeometry

Abstract

fetched live from OpenAlex

This study analyzes the propagation of the path losses between Jeju-do and Jin-do transceivers located in the coastal areas of Korea using the Advanced Refractive Prediction System(AREPS) simulation software based on the actual coastal weather database. The simulated data is used to construct a duct map according to the altitude and thickness of the trap. The duct map is then divided into several regions depending on the altitude parameters of Tx and Rx, which can be used to effectively estimate the abnormal wave propagation characteristics due to duct occurrence in the Jeju-do coastal area. To validate the proposed duct map, two representative atmospheric index samples of the weather database in May 2018 are selected, and the simulated path losses using these atmospheric indices are compared with the measured data. The simulated path losses for abnormal conditions at the Rx point at Jeju-do are 167.7 dB and 192.3 dB, respectively, which are in good agreement with the measured data of 164.4 dB and 194.9 dB, respectively.

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.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.276
Threshold uncertainty score0.341

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.008
GPT teacher head0.207
Teacher spread0.199 · 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