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
Many widely used approaches to pattern recognition/machine learning, including neural nets, k-nearest-neighbours classifiers, and support vector machines, have hitherto made little headway in speech processing, largely due to their inability to represent and compute over the variable-length sequential data of speech signals. A new technique, the string (subsequence) kernel, first applied in bioinformatics [1] and text classification [2], and extended to speech recognition by Goddard et al. [3], maps a variable-length input signal to a fixed-length feature array, by taking the inner product of n-gram subsequences. Similarity of signals can then be evaluated, by any of the above approaches, in the kernel space. In this presentation, two variations on Goddard's approach are considered and evaluated: a string kernel using fuzzy rather than absolute k-means clustering; and a kernel in which the feature counts are preserved as waveforms rather than scalars, to address the reverse mapping problem.
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 | 0.001 |
| 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