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
The finite Gaussian mixture model with kernel correlation is a flexible tool that has recently received attention for point set registration. While there are many algorithms for point set registration presented in the literature, an important issue arising from these studies concerns the mapping of data with nonlinear relationships and the ability to select a suitable kernel. Kernel selection is crucial for effective point set registration. We focus here on multiple kernel point set registration. We make several contributions in this paper. First, each observation is modeled using the Student's t-distribution, which is heavily tailed and more robust than the Gaussian distribution. Second, by automatically adjusting the kernel weights, the proposed method allows us to prune the ineffective kernels. This makes the choice of kernels less crucial. After parameter learning, the kernel saliencies of the irrelevant kernels go to zero. Thus, the choice of kernels is less crucial and it is easy to include other kinds of kernels. Finally, we show empirically that our model outperforms state-of-the-art methods recently proposed in the literature.
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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) | 0.000 | 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