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Record W7144812494

64Hz induction magnetometer data for Nain in CDF

2022· dataset· en· W7144812494 on OpenAlexaboutno aff
Kazuo Shiokawa, ISEE IUGONET Metadata Management Group

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2022
Typedataset
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsMagnetometerAmplitudeSensitivity (control systems)Earth's magnetic fieldPhase (matter)Sampling (signal processing)Polarity (international relations)
DOInot available

Abstract

fetched live from OpenAlex

The induction magnetometer data observed at Nain, Canada. The data consist of variations of three-dimensional (H, D, and Z components) geomagnetic field taken with a sampling rate of 64 Hz, and some engineering parameters for the instrument, such as the sensitivity and phase difference. The digital data are distributed in the Common Data Format (CDF) through the ERG-SC repository. Importnat Notes: 1. For frequencies below ~1Hz, use the following equation to obtain amplitude values in units of nT/s: dB/dt (nT/s) = data (V) / quick_sensitivity. The value of quick_sensitivity is given in global attributes. For frequencies above ~1Hz, use the exact sensitivity curve to obtain absolute amplitude of waves. 2. Please note that the positive direction (polarity) of H, D, and Z are different depending on the station. For example, in February 2011, the polarity is NAI: positive=northward, eastward, upward; MGD: positive=northward, eastward, downward; ATH: positive=northward, eastward, downward; MSR: positive=southward, westward, upward; STA: positive=northward, eastward, downward. Please see http://stdb2.isee.nagoya-u.ac.jp/magne/magne_stations.html for the latest information.

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 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)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0000.004
Open science0.0040.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.070
GPT teacher head0.334
Teacher spread0.264 · 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 teacher head, not a consensus.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueInstitutional Repositories DataBase (IRDB)French-language works237,207