Comments on “Stochastic TEC Structure Characterization” by Charles Rino, Yu Morton, Brian Breitsch, and Charles Carrano, <i>Journal of Geophysical Research: Space Physics</i>, <i>124</i>, https://doi.org/10.1029/2019JA026958
Bibliographic record
Abstract
Abstract Rino et al. (2019, https://doi.org/10.1029/2019JA026958 ) has recently explored the effects of refractive (total electron content [TEC]) and stochastic/diffractive (scintillation) on the GPS carrier phase observable, with the intent to show that the scintillation portion of the total ionospheric effect on the carrier phase is negligible. A major portion of the manuscript is based on “stochastic TEC,” a new term that is not well defined by the authors. If the authors meant this as variations in the phase that does not scale with carrier frequency, then it is not TEC at all. One cannot measure TEC, using phase, if the variations do not scale with frequency. In the data‐driven portion of the paper, there are claims and assumptions made that are incorrect, or not well explained. These include the claim that the TEC and scintillation portions of the carrier phase observable cannot be separated, but previous work has shown that it is possible (McCaffrey & Jayachandran, 2019, https://doi.org/10.1029/2018ja025759 ); the use of the C/N0 in calculating the S4 scintillation index, despite C/N0's sensitivity to the phase and amplitude of the carrier; and the negligence of the higher‐order effects of the TEC calculation.
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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 | 0.007 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.018 | 0.019 |
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".