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Record W7101600402 · doi:10.7910/dvn/kzao3g

HF Data for: Ionospheric Effects on Maximum Usable Frequency for1 a Cross-Auroral and a Polar Cap HF Radio Link

2024· dataset· W7101600402 on OpenAlexaffabout

Bibliographic record

VenueHarvard Dataverse · 2024
Typedataset
Language
FieldBusiness, Management and Accounting
TopicCorporate Insolvency and Governance
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsHigh frequencyIonosphereUSableRadio signalSIGNAL (programming language)PolarPolar capMATLAB

Abstract

fetched live from OpenAlex

HF radio receiver data used in the paper: Climatology of Maximum Usable Frequency at High Latitudes. OTT-ALE_HF_Data.zip contains signal parameters for transmissions sent from Ottawa, ON, Canada to Alert, NU, Canada from January 2014 to June 2016. QAN-ALE_HF_Data.zip contains signal parameters for transmissions sent from Qaanaaq, Greenland to Alert, NU, Canada from July 2012 to September 2016. Individual Matlab save files are in the attached zip files, and are named according to date and frequency in the following scheme: ddAle_Ott_yyyymmdd_ffffffff.mat.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.087
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0870.099

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 head0.025
GPT teacher head0.254
Teacher spread0.229 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2024
Admission routes2
Has abstractyes

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