CLASSIFICATION OF SIMILAR 2-D OBJECTS BY WAVELET-SPARSE-MATRIX (WSM) METHOD
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 proposes a novel method called Wavelet-Sparse-Matrix (WSM) to extract the spatial features of 2-D objects for classifying objects that have subtle differences. The differences between these objects are present in the spatial orientations of the objects, or in the local positions of points on the contours of the objects. The separable wavelets are able to distinguish these differences and to separate them into three sparse subpatterns. Sparse matrix technique has the ability to rearrange nonzero elements in a sparse matrix by moving them as close together as possible. WSM method is a combination of these two techniques which can considerably improve the distinction of slightly dissimilar objects. Experiments are conducted, which include a series of discriminative simulations and comparisons with Fourier descriptor and Zernike moment invariant. These experiments verify the feasibility and effectiveness of the WSM method.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 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