Bibliographic record
Abstract
Abstract Light detection and ranging (lidar) is a technique in which a beam of light is used to make range‐resolved remote measurements. A lidar emits a beam of light, that interacts with the medium or object under study. Some of this light is scattered back toward the lidar. The backscattered light captured by the lidar's receiver is used to determine some property or properties of the medium in which the beam propagated or the object that caused the scattering. The lidar technique operates on the same principle as radar; in fact, it is sometimes called laser radar. The principal difference between lidar and radar is the wavelength of the radiation used. Radar uses wavelengths in the radio band whereas lidar uses light, that is usually generated by lasers in modern lidar systems. The wavelength or wavelengths of the light used by a lidar depend on the type of measurements being made and may be anywhere from the infrared through the visible and into the ultraviolet. The different wavelengths used by radar and lidar lead to the very different forms that the actual instruments take. The major scientific use of lidar is for measuring properties of the earth's atmosphere, and the major commercial use of lidar is in aerial surveying and bathymetry (water depth measurement). Lidar is also used extensively in ocean research and has several military applications, including chemical and biological agent detection. Atmospheric lidar relies on the interactions, scattering, and absorption, of a beam of light with the constituents of the atmosphere. Depending on the design of the lidar, a variety of atmospheric parameters may be measured, including aerosol and cloud properties, temperature, wind velocity, and species concentration. This article covers most aspects of lidar as it relates to atmospheric monitoring. Particular emphasis is placed on lidar system design and on the Rayleigh lidar technique.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.073 | 0.064 |
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".