Detecting Clusters and Nonlinearity in Three-Dimensional Dynamic Graphs
Why this work is in the frame
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Bibliographic record
Abstract
Three-dimensional dynamic scatterplots can reveal certain features of data that cannot be apprehended in marginal two-dimensional displays. Using graduate students as subjects, we sought to establish whether the detection of clusters and nonlinearity in 3-D plots varies by easily characterized properties of the data and the design of the display. We found that the probability of detection of clusters increased smoothly with cluster separation, and that, at a fixed level of separation, “diagonally” displaced clusters were easier to detect than “horizontally” displaced clusters. Cluster detection appeared to be affected to a smaller extent by the design of the display. Three further experiments addressed the detection of nonlinearity in 3-D dynamic scatterplots. Most subjects were able to respond in a reasonable manner to properties of the data, so that the probability of detection of nonlinearity increased with its level, particularly when the signal was strong. As in the experiment on cluster detection, subjects' performance was also affected, though to a lesser extent, by characteristics of the displays; for example, spinning the display horizontally in the regression plane was particularly effective. We discuss the implications of these results for the design of statistical software incorporating dynamic 3-D scatterplots.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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