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
This paper introduces collection space theory and proposes five collection space distances: scaled city block, angle, minimum, nearest first, and farthest first distances. A collection of set S is a discrete histogram of a finite subset of S. A collection space is set C of collections of a set S with a distance on C which satisfies certain axioms. The scaled city block distance is the proportion of c-points that two scaled collections do not share. The angle distance between two collections is proportional to the angle between them. The other three distances are applicable to collections of points of a metric space. A correspondence between two n-collections defines a one-to-one mapping between the n c-points of the two collections. The deformation of a correspondence is the average displacement in the metric space for transforming one collection into the other according to the correspondence between them. The minimum distance is the minimum deformation of any correspondence between two collections. The nearest first distance and the farthest first distance are heuristic approximation of the minimum distance which are less expensive to compute. In applications where patterns can be represented by differentiated collections, pattern dissymmetry is quantified by their collection space distance. An example is given where fabric samples are classified based on their image color collections using different collection space minimum distance classifiers. A collection space method for measuring texture difference and bilateral symmetry in images is also presented.
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) | 0.005 | 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