Modeling neutral-atmospheric electromagnetic delays in a “big data” world
Bibliographic record
Abstract
If left unmodeled, the delay suffered by electromagnetic waves while crossing the neutral atmosphere negatively affects Global Navigation Satellite System positioning. The modeling of the delay has been carried out by means of empirical models formulated based on climatological information or using information extracted from numerical weather prediction (NWP) models. This paper explores the potential use of meteorological information of several types that will become available with the increasing number of sensors (e.g. a cell phone, or the thermometer of a nearby smart home) in cyberspace. How can we make use of these potentially huge data-sets, which may help to provide the best possible representation of the neutral atmosphere at any given time, as readily and as accurately as possible? This situation falls in the realm of Big Data. A few potential scenarios, a sequential improvement of Marini mapping function coefficients, a self-feeding NWP, and near real-time empirical model updates, are discussed in this paper. The pros and cons of each approach are discussed in comparison with what is done today. Experiments indicate that they have potential for a positive contribution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".