Effects of the terdiurnal tide on the sporadic e (Es) layer development at low latitudes over the Brazilian sector
Bibliographic record
Abstract
Sporadic E (Es) layers are patches of high ionization observed at around 100-140gkm height in the E region. Their formation at low latitudes is primarily associated with the diurnal and semidiurnal components of the tidal winds via the ion convergence driven by the wind shear mechanism. However, recent studies have shown the influence of other tidal modes, such as the terdiurnal tide. Therefore, this work investigates the effect of terdiurnal tide-like oscillations on the occurrence and formation of the Es layers observed over Palmas (10.17g gS, 48.33g gW; dip lat.-7.31g ), a low-latitude station in Brazil. The analysis was conducted from December 2008 to November 2009 by using data collected from CADI (Canadian Advanced Digital Ionosonde). Additionally, the E Region Ionospheric Model (MIRE) was used to simulate the terdiurnal tidal component in the Es layer development. The results show modulations of 8gh periods on the occurrence rates of the Es layers during all seasonal periods. In general, we see three well-defined peaks in a superimposed summation of the Es layer types per hour in summer and autumn. We also observed that the amplitude modulation of the terdiurnal tide on the Es occurrence rates minimizes in December in comparison to the other months of the summer season. Other relevant aspects of the observations, with complementary statistical and periodogram analysis, are highlighted and discussed.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".