Bibliographic record
Abstract
Abstract Process data suffer from many different types of imperfections. For example, bad data due to sensor problems, multi‐rate sampling, outliers, compressed data etc. Since most modelling and data analysis methods are developed to analyze regularly sampled and well conditioned data sets there is a need for pre‐treatment of data. Traditionally data conditioning or pre‐treatment has been done without taking into account the end use of the data, for example, univariate methods have been used to interpolate bad data even when the intended end use of data is for multivariate analysis. In this paper we consider the pre‐treatment and data analysis as a collective problem and propose data conditioning methods in a multivariate framework. We first review classical process data analysis methods and acclaimed missing data handling techniques used in statistical surveys and biostatistics. The applications of these acclaimed missing data techniques are demonstrated in three different instances: (i) principal components analysis (PCA) is extended in data augmentation (DA) framework for dealing with missing values, (ii) iterative missing data technique is used to synchronize uneven length batch process data, and (iii) PCA based iterative missing data technique is used to restore the correlation structure of compressed data.
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.
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 | 0.028 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".