Bibliographic record
Abstract
The goal of the present study is to model the ‘‘iceberg’’ portions of the demisyllables, previously extracted from the microbeam articulatory data (Bonaventura, 2003), by curve fitting. The polynomial analysis was designed to include an appropriate weighting window centering around the threshold crossing point, and aimed to provide an estimate of how, in the vicinity of the crossing point, the curve deviates from a straight line: this deviation would be represented by the higher order coefficients of the polynomial. The model was obtained preliminarily on the basis of 100 curves for the lower lip movement for /f/ and /v/ (in initial and final demisyllable for ‘‘five’’), and from 100 curves for the tongue tip displacement (for /n/ in ‘‘nine’’). In order to fit the data to the model, a robust least square method (Least Absolute Residuals) has been used, in order to minimize the influence of the outliers, that are present in the read speech data, and cannot be accounted for by ‘‘phrase final lengthening effects.’’ The fit results for the cubic polynomials satisfactorily approximated the ‘‘iceberg’’ curves. The 95% confidence bounds on the fitted coefficients indicated that they were acceptably accurate.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".