Bibliographic record
Abstract
This paper looks at the acoustics of uvulars in Tlingit, an Athabaskan language spoken in Alaska and Canada. Data from five native speakers was used for acoustic analysis for tokens from five phoneme groups (alveolars, plain velars, labialized velars, plain uvulars, and labialized uvulars). The tokens were analyzed by computing spectral moments of plosive bursts and fricatives, and F2 and F3 values for post-consonantal vowels, which were used to calculate locus equations, a descriptive measure of the relationship between F2 at vowel onset and midpoint. Several trends were observed, including a greater difference between F2 and F3 after uvulars than after velars, as well as a higher center of gravity (COG) and lower skew and kurtosis for uvulars than for velars. The comparison of plain versus labialized consonants supports the finding of Suh (2008) that labialization lowers mean burst energy, or COG, and additionally found labialization to raise skew and kurtosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".