Spread-F characteristics over Tucumán near the southern anomaly crest in South America during the descending phase of solar cycle 24
Bibliographic record
Abstract
Abstract Ionospheric F-region irregularities can acutely affect navigation and communication systems. To develop predictive capabilities on their occurrence, it is key to understand their variabilities in a wide range of time scales. Previous studies at low latitudes in South America have been performed mostly in the eastern region. However, there are still few reports on the spread-F over Argentina owing to a lack of ionosonde data. This work presents the analysis of the spread-F (range spread-F and frequency spread-F) and plasma bubble occurrence characteristics near the southern crest of the Equatorial Ionization Anomaly in Argentina (Tucumán, 26.8°S, 65.2°W; magnetic latitude 15.5°S). We used ionosonde and Global Positioning System (GPS) data from November 2014 to December 2019 for different solar and geomagnetic conditions. The data show that spread-F and plasma bubble occurrence rates peak in local summer and are minimum in equinox and winter, respectively. There is a negative correlation between each type of spread-F and solar activity, whereas the opposite happens for plasma bubbles. Geomagnetic activity suppresses the generation of spread-F in equinox and summer and enhances it in winter. Plasma bubble occurrence is higher during disturbed days than during quiet days, but under medium solar activity, summer months register more plasma bubbles in quiet conditions. Range spread-F observed in winter under low solar activity is not associated with plasma bubbles originated at the magnetic equator. These results contribute to the knowledge necessary to improve the prediction of the spatial and temporal distribution of the night-time ionospheric irregularities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".