Impact of Data Grouping on the Multivariate Analysis of Several Concrete Plants
Bibliographic record
Abstract
Multivariate analysis can be used to study industrial process data exhibiting collinearity between variables. Such data can often be collected into conceptually meaningful groups or blocks. While data blocks may appear intuitive (e.g., raw material properties vs. process parameters), such blocking is sometimes much more subjective. The novelty of this work lies in the investigation of the impact of data blocking on the subsequent analysis. To our knowledge, no such investigation can be found in the literature. To fill this gap, we analyze the impact of grouping data from 10 Canadian concrete plants in which multiple blocking alternatives are considered. The analysis is performed via principal component analysis (PCA) to reduce the dimensionality of the matrix and also via consensus principal component analysis (CPCA). The data grouping options are as follows: (1) all data combined into a single block, (2) grouped according to the factory, (3) grouped according to parameter type, and (4) grouped according to parameter type within each factory. The results show that the grouping strategy alters the conclusion by emphasizing specific aspects of the data. While some grouping options emphasized seasonal variations, others emphasized other characteristics in the data, such as step changes in processing regimes or the significant impact of the raw materials’ moisture on the process. As such, it appears relevant to consider multiple blocking options when analyzing complex datasets. Doing so will give the analyst a better understanding of overarching trends and more subtle characteristics of the dataset.
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 | 0.019 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".