The structure of turbulence in the middle and lower atmosphere seen by and deduced from MF, HF and VHF radar, with special emphasis on small-scale features and anisotropy
Bibliographic record
Abstract
Abstract. An overview of the turbulent structures seen by MF, HF and VHF radars in the troposphere, stratosphere and mesosphere is presented, drawing on evidence from previous radar measurements, in situ studies, laboratory observations, observations at frequencies other than those under focus, and modelling studies. We are particularly interested in structures at scales less than one radar pulse length, and smaller than the beam width, and especially the degree of anisotropy of turbulence at these scales. Previous radar observations are especially important in regard to the degree of anisotropy, and we highlight the role that these studies have had in furthering our understanding in this area. The contrasts and similarities between the models of anisotropic turbulence and specular reflection are considered. The need for more intense studies of anisotropy at MF, HF and VHF is especially highlighted, since this is an area in which these radars can make important contributions to the understanding of atmospheric turbulence.Key words. Meteorology and atmospheric dynamics (turbulence) – Atmospheric composition and structure (instruments and techniques) – History of geophysics (atmospheric sciences)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".