Checking the LuSci profile restoration
Bibliographic record
Abstract
Lunar scintillometer, LuSci, serves for measurement of surface-layer turbulence at a number of established and new sites. An original 4-channel prototype worked at CTIO and at LCO, while 6-channel instruments are being used by ESO. Methods of extracting turbulence profile (TP) C 2 n(h) from scintillation covariances are still a subject of research. Of interest are the accuracy and robustness of restored TPs. The linear method of “layers ” [2] was replaced in 2008 by a more elaborate model-fitting, representing the TP by linear (in log-log coordinates) segments between selected pivot points [4]. This technique is inspired by data analysis of SHABAR [1]. Meanwhile, the scintillometer array developed by the University of Vancouver fits data with double-exponential model [5]. It was demonstrated that the pivot-point method (PPM) produces results not very different from the previous layers method when applied to the 4-channel prototype [4]. However, the TPs derived from the 6-element LuScis systematically show low C 2 n values at the 16-m point, which is un-realistic. Limited comparison of LuSci with SL-SLODAR at Paranal in October 2008 also demonstrated this effect. The reliability of the PPM is thus put in question, warranting further study. 2 Input data Data from the ESO LuSci-1 instrument at Paranal on the nights of January 8,9,11 2009 was used to test the restoration. For the first 2 nights, the data were filtered by A.Berdja to remove a small fraction of faulty measurements, for Jan. 11 the data are not yet filtered. The covariances are written in the.dat file in the following order: variances for 6 channels, covariances of ch.0 with channels 1-5, covariances of ch.2 with chs. 2-5, etc. Figure 1 plots the covariances averaged for the whole night in the same order as redorded in.dat. The covariances
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".