Analysis of Longitudinal Advancement of the Peak Total Electron Content in the African Equatorial Anomaly Region Using Data From GPS Receivers and GIS Stations in Kenya
Bibliographic record
Abstract
We investigated longitudinal advancement of the peak Total Electron Content (TEC) within the equatorial anomaly region of Kenya, based on the data from the two Scintillation Network Decision Aid (SCINDA) Global Positioning System (GPS) receivers at Jomo Kenyatta University of Agriculture & Technology (JKUAT): latitude ?1.095, longitude 37.015, and at the University of Nairobi: latitude ?1.274, longitude 36.808; and the two Geographical Information System (GIS) stations at Malindi: latitude ?2.996, longitude 40.194, and at the Regional Center for Mapping of Resources for Development (RCMRD): latitude ?1.221, longitude 36.893. The retrieved archived data from these stations were for 17th December 2009; and for 26th, 27th and 28th August 2010 —just after the solar minimum period of 2008. They were subjected to analysis by the GPS-TEC analysis application software provided by Boston College Research Institute (USA). Results reveal that the peak TEC within the region of study occurs between 8 a.m. and 10 a.m. The Coastal region experiences peak TEC earlier than Nairobi region. Slight variations were also observed on the TEC plots from various stations. It can be concluded that the peak TEC advances westwards within the equatorial anomaly region; these variations are due to plasma bubbles that take place from the east to west within the equatorial region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".