F0 range instead of F0 slope is the primary cue for the falling tone of Mandarin
Bibliographic record
Abstract
It has been well known that rising/falling pitch is employed to distinguish the rising (R) or falling (F) tones from the high-level (H) tone in Mandarin, but whether F0 range or F0 slope is the more critical F0 cue to perception is still inconclusive. To clarify this issue quantitatively, we took the F tone as the test case, and conducted two-alternative forced choice identification tests on two types of two-dimensional high-level-falling (H-F) tonal continua, one of which was manipulated along F0 range and duration ("F0 range continuum") while the other along F0 slope and duration ("F0 slope continuum"). Experimental results indicated that F0 range was the primary cue because it resulted in a more robust (less duration-dependent) perceptual boundary than F0 slope. Meanwhile, the perceptual boundary in F0 range was not fully independent of but mildly modulated by duration, suggesting that duration (or equivalently, F0 slope) played a supplementary role in identifying the H-F tonal contrast.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".