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Record W6917509341 · doi:10.57757/iugg23-2079

Case study of foehn events over Alborz mountains in Iran

2023· article· en· W6917509341 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

VenuePublication Database GFZ (GFZ German Research Centre for Geosciences) · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicMeteorological Phenomena and Simulations
Canadian institutionsYork University
Fundersnot available
KeywordsWeather Research and Forecasting ModelSea levelMaximum sustained windCyclone (programming language)Wind speedPressure gradientPrecipitation

Abstract

fetched live from OpenAlex

<!--!introduction!--><b></b> In mountainous regions worldwide, warm and dry Foehn winds can have a significant impact on human life. The characteristics of foehn winds include a rising temperature, decreased relative humidity, and a persistent high wind direction of origin. Many parts of Iran, a country with mountainous terrain, are impacted by foehn winds. Iran's northern region is bounded by the Caspian Sea to the north and the southern Alborz Mountains. This study focuses on the southwestern part of the Caspian Sea coast. Significant mountain waves with large amplitudes have been observed, leading to severe forest fires in the Caspian region. The study shows that foehn events can arise as a result of high pressure in interior regions and a lee cyclone over the southern Caspian Sea, accompanied by a strong south-north pressure gradient across the Alborz Mountains. To demonstrate this foehn situation, four typical wind events from 2021 will be used as examples. The sample simulation results demonstrate the efficacy of WRF in forecasting changes in meteorological quantities.

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)0.005
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.149
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.125
GPT teacher head0.380
Teacher spread0.255 · 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