Bibliographic record
Abstract
We propose a new class of generalized moment functions (GMFs) that scan the object with different probing functions. Using the GMF, it is possible to extract a unique geometric point within the object, called the generalized centroid (G-centroid). We can obtain a set of discrete G-centroids from the same object by using different GMFs. The GMFs, which are similar to traditional moment functions, can also be used to describe the global shape of the object, including symmetry and fullness. However, the GMFs, along with the G-centroids, can further serve to construct a feature vector of the object, which is critical to the process of image registration and pattern recognition. Conformal transforms (C-transforms) are another tool used to probe the object by rearranging the latter's mass distribution, without distorting its shape. Using the C-transformed object, it is possible to detect a new mass centroid and G-centroid. More distinguishing feature points can be extracted from the same object by changing the combination of different GMFs and C-transforms. As GMF and centroid detection can be performed by convolution, the centroid and G-centroid can be detected optically in real time. It is also possible to optically implement some of the C-transforms. We present the results of GMF and C-transform applications, including image registration and pattern recognition.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".