Partial least squares discrimination applied to a few samples dataset: A case for predicting the presence of pesticide in lettuce
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract To perform discriminant analysis through partial least squares (PLS‐DA), it is important to note that the smaller the set of samples and the higher the variable to sample ratio, the higher the chance of a too optimistic classification model. In this sense, it is necessary to use strategies to check for the possibility that the classification achieved is done by chance. In metabolomics studies, it is not uncommon to work with a reduced number of samples, in which discrimination approaches must be evaluated according to its reliability. Considering this issue, this study aimed to show a case study to deal with few samples using PLS‐DA to discriminate lettuce cultivated in the absence and presence of imidacloprid (IMI). The data were acquired by using ultra‐high‐performance liquid chromatography coupled to a quadrupole‐time of flight mass spectrometry, and the model prediction ability was evaluated by permuting the classes. The performance of the PLS‐DA model built using all the variables reached 100% correct classification. Nonetheless, the reliability tests (Wilcoxon, sign test, and Rand t test) indicated that the model has been build choosing variables by chance. By using the variable importance in projection, it was possible to build a model with reliable specificity and sensitivity equals 1. The study showed the need to check the classification ability in PLS‐DA models through strategies such as variable selection and the permutation test in order to allow for the evaluation of the reliability of the results, even in cases in which the classification reaches 100% in the target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it