Atmospheric temperature retrievals from lidar measurements using techniques of non-linear mathematical inversion
Bibliographic record
Abstract
The conventional method of lidar data processing to retrieve atmospheric temperature profiles has some limitations which necessitate the abandonment of the temperatures retrieved at the uppermost limits of the observational range. The application of mathematical inversion, as a tool to remedy this problem, was investigated in this project. A simple grid search technique was used to develop an alternative way of retrieving atmospheric temperature profiles from lidar data. Data obtained from the Purple Crow lidar (PCL) (42.87° N, 81.38° W, 225 m) facility at the University of Western Ontario was used to perform the preliminary tests on this technique. PCL data for 12 nights of observation were processed by the new technique. Initial results show that data at the uppermost altitude limits can be reliably retrieved with this method. A numerical scheme to analyze errors in the retrieved temperatures was developed. The uncertainties in retrieved temperatures computed using this method are comparable to the corresponding uncertainties in the conventional technique.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".