Bibliographic record
Abstract
A study has investigated certain auditorily based sound changes and assimilations, obtained by adjusting the definition of the feature [grave], and concomitant adjustments to the classification of segments. Dentals are considered [acute] in all Jakobsian taxonomy et sequentes, while their noise energy and their involvement in [flat] enhancement and assimilation suggest instead that they are [grave]. The study has argued that the Jakobsian feature [grave] does not require a predominance of low-frequency noise, but rather requires that the noise below 2.5 kHz is 'sufficiently audible' owing to a lack of predominance of high-frequency noise. This effectively extends the reach of the feature, since all the noisy sounds, which were classed as [grave] under the original definition are notably labials and velars. The study also highlighted that non-sibilant dentals too are [grave] as their noise energy is similar to that of labials.
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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) | 0.003 | 0.003 |
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; both teacher heads agree on what is shown here.
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".