Bibliographic record
Abstract
Abstract. It has long been assumed that Rayleigh lidar can be used to measure atmospheric temperature profiles up to about 90 or 100 km and that above this region the technique becomes invalid due to changes in atmospheric composition which affect basic assumptions on which Rayleigh lidar is based. Modern powerful Rayleigh lidars are able to measure backscatter from well above 100 km requiring a closer examination of the effects of the changing atmospheric composition on derived Rayleigh lidar temperature profiles. The NRLMSISE-00 model has been used to simulate lidar signal (photon-count) profiles, taking into account the effects of changing atmospheric composition, enabling a quantitative analysis of the biases and errors associated with extending Rayleigh lidar temperature measurements above 100 km. The biases associated with applying a nominal correction for the change in atmospheric composition with altitude has also been investigated. The simulations reported here show that in practice the upper altitude limit for Rayleigh lidar is imposed more by the accuracy of the temperature or pressure used to seed the temperature retrieval algorithm than by accurate knowledge of the atmospheric composition as has long been assumed.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".