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.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".