Effects of Preprocessing of Ultraviolet-Induced Fluorescence Spectra in Plant Fingerprinting Applications
Bibliographic record
Abstract
Preprocessing is an important step in data analysis. Dealing with spectral data, normalization is mandatory in order to compare items collected under various conditions. This paper addresses normalization of frontface fluorescence spectroscopy data where spectra are affected by an unknown multiplicative effect. The usual methods for reducing multiplicative problems are reviewed and a more detailed analysis of the normalization by closure is provided based on data on the fluorescence of plants as a means for plant species fingerprinting. As normalization is essentially the reduction of information, some methods of carrying it out are likely to remove either meaningful or discriminant pieces of information. As a result, it is demonstrated that normalization by closure should be performed using spectral data in a range where the spectra contain no information relevant to the problem at hand. This applies provided that in this range the signal-to-noise ratio is high enough. When the noise level is too high, a compromise should be found between preserving useful information and limiting the amount of noise introduced by the normalization procedure. Even if this study were carried out using fluorescence spectra, the overall process is likely to be applied to other spectral data.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".