The Hp geomagnetic index test dataset 2003, 2004, 2005 and 2017
Bibliographic record
Abstract
Purpose and design of the Hp indices, test dataset The geomagnetic Hp indices are developed as part of the SWAMI project (http://swami-h2020.eu) funded by the European Union’s H2020 research and innovation program. They are designed to resemble the geomagnetic Kp index, but have a higher temporal resolution of 90, 60 and 30 minutes. Whereas the Kp index is a measure of energy input from the solar wind during a 3-hour interval, the Hp indices aim at being a similar measure for the energy input, but over shorter intervals. The geomagnetic Hp indices can be provided back to 1995. Their derivation procedure is similar, but not identical, to the Kp index. Hp values range from 0 to 9 (like Kp), and have mean occurrence rates that are comparable to those of the Kp index. However, users have to appreciate that the Hp indices are not identical to the Kp index of the corresponding time interval. Therefore, it is to be expected that they represent the energy input from the solar wind slightly differently than when using the Kp index. Disclaimer to users of the Hp indices test data set Please carefully test and validate all your model output and services for which you use the Hp indices (including the ap90, ap60, ap30) as input parameter. This is especially true when these models and services were originally derived or parameterized with the Kp index. Which files to use? We provide a number of test data files with different time resolutions. By default, we recommend to use the 1-hourly Kp-like Hp60 index (e.g. data file Hp60_2003.dat) or ap-like ap60 index (e.g. ap60_2003.dat). Hp test dataset description The Hp test dataset consists of 24 files. It is accompanied by the presentation given on the index at the IUGG General Assembly 2019 in Montreal (Stolle et al., 2019). For each year 2003, 2004, 2005 and 2017, there exist annual files for 90, 60 and 30 minutes time resolution) in 2 different formats (Hp and ap). In the format 'Hp' the Hp values are given as 0, 0.7, 1, 1.3, 1.7, 2, 2.3, ... 8.7, 9. In the format 'ap', the Hp values are mapped onto ap values in the same fashion as Kp values are mapped to ap values. The index is provided with an hourly resolution (Hp60 and ap60), and also with a 30-minute (Hp30 and ap30) and 90-minute version (Hp90 and ap90). The years 2003 (Halloween storm in October and November), 2005 (frequent geomagnetic storms) and 2017 (geomagnetic storm in September) were chosen for the occurrence of strong geomagnetic activity. The files are ASCII and have 7 header lines. The data is blank separated and fixed length. The 7th header line indicates the start time (in UTC) of the index interval. For Hp90 there are 16 intervals per day, for Hp60 there are 24 intervals per day, for Hp30 there are 48 intervals per day. Every line with data contains the index values for one day and starts with the date (year-month-day) in the format YYYY-MM-DD. The index values for each interval are written below the start time of the 7th header line. Missing data is indicated by -1. For more information on the Kp and ap index, please refer to https://www.gfz-potsdam.de/en/kp-index/ and to Siebert and Meyer (1996). For more information on the Hp indices test dataset, please refer also to the presentation (Stolle et al., 2019) which can be downloaded from the FTP server.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".