An environment for visualizing higher dimensional measured data
Bibliographic record
Abstract
Over the last few decades, the ability of instrumentation to measure large volumes of data has literally been growing exponentially. It is increasingly easy to build a measurement system with a large number of channels that come from acquisition cards and networked sensors. Analysis and statistics from raw measurements yeild derived measurements that add to the dimensionality of the space that a measurement user needs to understand to gain knowledge or solve problems from the data. It is therefore necessary to create methods that permit the exploration of such large and high dimensional sets of measured data within the limitations of the graphics rendering capabilities, screen sizes, and input devices that can reasonably be built into instrumentation and measurement systems. The problem addressed in this paper is that of vizualizing measured data of higher dimension (>; 10) and moderately large size. We present a software prototype that uses multiple complementary graphical views to display multidimensional data, with coordinated highlighting across views and flexible selection techniques. Here, we describe a tool providing a new combination of existing methods, scatterplot matrices (SPLOMs) and parallel coordinate plots (PCPs), for exploring higher dimensional data. We demonstrate the tool's application to measured orthogonal frequency-division multiplexing (OFDM) signals including identifying the nature of a disturbance in the signal.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".