Alberta Differential Magnetometer Measurement (DMM) Data from 2021
Bibliographic record
Abstract
This data product consists of a magnetometer dataset from the augmented differential magnetometer measurement (DMM) study conducted in Alberta, Canada in 2021 and GIC data from a nearby transformer substation. This data was analyzed and presented in the paper "Using a Differential Magneoter Measurement to Infer Geomagnetically Induced Currents: An Augmented Approach." Specifically, these datasets were used to create Figures 3, 4, 5, 6, 7, 9 and 10 in this paper. The data files available here are from two magnetometers (USB4 and USB5) on a magnetically quiet day, September 26th, 2021, and a magnetically active day, October, 12th, 2021. USB4 data is from the underline sensor and USB5 is from the remote sensor. Each DMM data file includes the time in UT and magnetic field measurements in the x, y, and z components, where Bx is magnetic north and By is magnetic east. The GIC data file includes the local time (Mountain Daylight Time = GMT-6) and the transformer neutral-to-ground current at the Ellerslie substation (89S) in both transformers (T1 and T2) during the magnetic storm on October 12th, 2021. The magnetic data has a resolution of 1 Hz. The GIC data has a resolution of 0.5 Hz.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".