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Record W3028086217 · doi:10.1029/2020rs007060

Comparison of Surface Radio Refractivity Variability in the Northern and Southern Parts of Quebec, Canada

2020· article· en· W3028086217 on OpenAlexaffabout
Yamina Bettouche, Huthaifa Obeidat, Basile L. Agba, Ammar B. Kouki, Mahmood F. Mosleh, Ali Alabdullah, Raed A. Abd‐Alhameed

Bibliographic record

VenueRadio Science · 2020
Typearticle
Languageen
FieldEngineering
TopicRadio Wave Propagation Studies
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsEnvironmental scienceStandard deviationClimatologySmoothingAtmospheric sciencesMeteorologyGeographyGeologyMathematicsStatistics

Abstract

fetched live from OpenAlex

Abstract Thirty‐nine years of archived meteorological data measured at two stations located in the northern and southern parts of Quebec, Canada are used to estimate the surface refractivity and its dry and wet components. The results of the comparison of the obtained estimates showed that for all months the values of the dry component are higher in the northern part, whereas the values of the wet component are higher in the southern part. Due to this, for several months of the year, the values of the surface refractivity are higher in the northern part and for the remaining months in the southern part. Moreover, in both parts, August is the month where the highest values of the surface refractivity were recorded. In this particular month, the slope of the surface refractivity trend in the northern part is several times higher than that in the southern part. The obtained results show that the performance of the used direct smoothing forecasting technique depends on the deviation between the values of N in the current year and the previous year.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.021
GPT teacher head0.246
Teacher spread0.225 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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