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
Elasticurves present a novel approach to neaten sketches in real-time, resulting in curves that combine smoothness with user-intended detail. Inspired by natural variations in stroke speed when drawing quickly or with precision, we exploit stroke dynamics to distinguish intentional fine detail from stroke noise. Combining inertia and stroke dynamics, elasticurves can be imagined as the trace of a pen attached to the user by an oscillation-free elastic band. Sketched quickly, the elasticurve spatially lags behind the stroke, smoothing over stroke detail, but catches up and matches the input stroke at slower speeds. Connectors, such as lines or circular-arcs link the evolving elasticurve to the next input point, growing the curve by a responsiveness fraction along the connector. Responsiveness is calibrated, to reflect drawing skill or device noise. Elasticurves are theoretically sound and robust to variations in stroke sampling. Practically, they neaten digital strokes in real-time while retaining the modeless and visceral feel of pen on paper.
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.
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 | 0.001 |
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 it