Bibliographic record
Abstract
The authors present some preliminary results of their ongoing study on subjective VQ distortion measure in the time/spatial domain. They first propose a context based distortion measure between two vectors. The new measure is intuitively appealing, and they include some empirical evidence for its subjective significance. Although the measure is formulated as a matrix norm, it is computationally no more difficult than the mean-squares error. This measure quantifies the quantization distortion in the context (shape) of the signal waveform, but it is amplitude-invariant. So they combine the context distortion measure with a weighted mean distortion measure to obtain a unified subjective distortion measure D. They show that D is a distance measure and can be easily computed. Moreover, the process of computing the centroid of a set of training vectors and designing the VQ codebook under the new subjective distortion measure D is as simple as the conventional VQ. Specifically, the LBG algorithm can be applied to design the subjective VQ codebook after a simple linear transformation of the vector space in which signal samples are originally taken. They also analytically relate their subjective distortion measure to the ubiquitous mean-squares measure, and demonstrate that the latter is only a special case of the former. They also observe that the mean-removed VQ in a sense clusters training vectors under the proposed context distortion measure.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".