Bibliographic record
Abstract
Auroral activity is a significant concern for reliable operation of GPS positioning applications at high latitudes. Auroral substorms are characterized by increased spatial and temporal decorrelations of ionospheric range delay, and loss of signal availability can occur during periods of ionospheric scintillations – a major concern during the current period of solar maximum. In recent years, GPS precise positioning applications have been disrupted consistently in Northern Canada – at significant cost to commercial surveying operations. In order to mitigate the economic impact of such effects, surveyors have developed methods of determining the nature of ionospheric activity in localized regions. For example, magnetometers have been installed in survey sites to monitor local magnetic field variations in real-time and detect the presence of auroral activity. Such methods have proven adequate, but far from ideal. Given the number of GIS, exploration, and construction applications presently relying on GPS in Canada, a significant need exists to develop ionospheric warnings and alerts for the GPS user community. In order to provide timely predictions of auroral activity, and associated degradations in receiver tracking performance and positioning accuracies, we are currently developing a real-time ionospheric warning and alert system for GPS users in Canada. This system will be based on a network of at least three GPS Ionospheric Scintillation Monitors (GISM) which provide real-time scintillation parameters extracted from 50 HZ L1 phase observations, in addition to raw GPS code and phase observations, rates of change of total electron content (TEC), and absolute TEC values. Observations will be processed at a centralized control facility at the University of Calgary. The focus of this research is the development of methods to track the magnitude and extent of ionospheric effects in Canada and Northern United States. Measures of associated impacts on GPS applications will be provided to GPS users in near real-time. It is intended that GPS users will access the real-time ionosphere warning service via the internet. This paper describes preliminary development of the system and methodologies. Initial results indicate that such a service may provide significant benefit for GPS users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.014 |
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".