Dynamic Spectra viewer data of 64Hz induction magnetometer data at All Stations (PNG format, Logarithmic Scale)
Bibliographic record
Abstract
The stack plot data about ISEE induction magnetometer observed at all stations (Athabasca Canada, Moshiri Hokkaido-Japan, Paratunka Russia, Sata Kagoshima-Japan, Zhigansk Russia, Kapuskasing Canada, Gakona Alaska, and so on). This plot data shows the H-component frequency data (in logarithmic scale) taken with a sampling rate of 64 Hz. Some engineering parameters for the instrument, such as the sensitivity and phase difference. This plot data and station list has been opened by ISEE Magentometer's web site (http://stdb2.stelab.nagoya-u.ac.jp/magne/). Important 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 ATH: positive=northward, eastward, upward; MGD: positive=northward, eastward, downward; PTK: 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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.301 | 0.138 |
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".