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
In recent years, tensor completion problem, as a higher order generalization of matrix completion, has received much attention from scholars engaged in computer vision, data mining, and neuroscience. The problem is often solved by convex relaxation, which minimizes the tensor nuclear norm instead of the n-rank of the tensor. However, tensor nuclear minimizes all the singular value at the same level, which is unfair to large singular values. To solve the problem, this paper defines a log function of tensor, and uses it as an approximation of tensor rank function. Then, a simple yet efficient log-based algorithm for tensor completion (Log-TC) was proposed to recover an underlying low n-rank tensor. The Log-TC was verified through experiments on randomly generated tensors and color image inpainting, in comparison with two tensor completion algorithms: fixed point iterative method for low-rank tensor completion (FP-LRTC) and fast low rank tensor completion algorithm (FaLRTC). The results show that our algorithm greatly outperformed the two contrastive methods.
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 | 0.001 |
| 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