Bibliographic record
Abstract
Files: grids.zip contains TextGrids for all audio files containing three tiers words, phonemes and transcription words contains the aligned normalized English words phonemes contains IPA pronunciations transcribed using CMU dictionary which then were aligned with Montreal Forced Aligner. The pronunciations were then mapped from ARPAbet to IPA and duration marks were applied (without punctuation) transcription contains unaligned phonemes including punctuation and word boundary labels (SIL0) preview.png preview of the first TextGrid opened in Praat words-vocabulary.txt contains all words from tier words phonemes-vocabulary.txt contains all phonemes from tier phonemes transcription-vocabulary.txt contains all phonemes/punctuation from tier transcription phonemes-durations.pdf contains the plotted phoneme duration distribution of tier phonemes phonemes-durations-simple.pdf contains the plotted phoneme duration distribution of tier phonemes if all duration markers are ignored pronunciations.dict contains the pronunciations for each word including punctuation and weights (occurrence) script.sh contains the script to reproduce all results Phoneme duration marker: ˘ -> [0, 20) percentile ˑ -> [80, 90) percentile ː -> [90, inf) percentile Silence marker: SIL0 -> no silence SIL1 -> [0, 33.33) percentile SIL2 -> [33.33, 66.66) percentile SIL3 -> [66.66, inf) percentile
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.185 |
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".