Multilevel label placement for execution trace events
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
Automatic label assignment to graphical objects is an important problem in many applications such as cartography, online maps and graph drawings. In this paper, we present efficient algorithms for automatic label assignment to execution trace items (points or lines) in a trace visualization tool. The proposed label assignment algorithms aim to maximize the number of labeled items as well as increase the quality of assignments. The algorithms take into account both the topological and semantic relationships (e.g. level of details, repetitiveness, etc.) between the trace items in order to achieve assignments that are both quantitative and qualitative. The proposed method also supports assigning multiple labels to each trace item. The algorithms have been implemented and applied to different input traces. The experimental results show that considering the relationships between data items increases the labeling success rate and the quality.
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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 | 0.000 |
| 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