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
There has been a lot of research on ellipse fitting and measuring ellipticity of a set of points. However, when the shape is primarily hyperbolic or parabolic, there are no existing methods to measure such properties. This paper describes the first known methods of measuring conicity, hyperbolicity and parabolicity of a set of points. After finding the best conic fit, we measure the corresponding ellipticity (using a known method), hyperbolicity or parabolicity value with respect to that best fit. We are interested in measures which rely exclusively on shape boundary points. They should also be calculated very quickly, be invariant to rotation, scaling and translation. The evaluation of fits transforms the point data into polar representation where the radius in this representation is equal to the difference of distances from each point to both foci (for hyperbolas), and the sum of distances from each point to the focus and a line parallel to the directrix line (for parabolas). The linearity of the polar representation will correspond to the quality of the fit for the original data. The conicity measure is tested on a set of 45 shapes. 1
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 | 0.001 | 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