System lag tests for augmented and virtual environments
Bibliographic record
Abstract
We describe a simple technique for accurately calibrating the temporal lag in augmented and virtual environments within the Enhanced Virtual Hand Lab (EVHL), a collection of hardware and software to support research on goal-directed human hand motion. Lag is the sum of various delays in the data pipeline associated with sensing, processing, and displaying information from the physical world to produce an augmented or virtual world. Our main calibration technique uses a modified phonograph turntable to provide easily tracked periodic motion, reminiscent of the pendulum-based calibration technique of Liang, Shaw and Green. Measurements show a three-frame (50 ms) lag for the EVHL. A second technique, which uses a specialized analog sensor that is part of the EVHL, provides a "closed loop" calibration capable of sub-frame accuracy. Knowing the lag to sub-frame accuracy enables a predictive tracking scheme to compensate for the end-toend lag in the data pipeline. We describe both techniques and the EVHL environment in which they are used.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".