Bibliographic record
Abstract
A novel text entry system designed based on the ubiquitous 12-button telephone keypad and its adaptation for a soft keypad are presented. This system can be used to enter full text (letters + numbers + special characters) on devices where the number of keys or the keyboard area is limited. Letter-frequency data is used for assigning letters to the positions of a 3x3 matrix on keys, enhancing the entry of the most frequent Letters performed by a double-click. Less frequent letters and characters are entered based on a 3x3 adjacency matrix using an unambiguous, two-keystroke scheme. The same technique is applied to a virtual or soft keyboard layout so letters and characters are entered with taps or slides on an 11-button keypad. Based on the application of Fitts' law, this system is determined to be 67% faster than the QWERTY soft keyboard and 31% faster than the multi-tap text entry system commonly used on cell phones today. The system presented in this paper is implemented and runs on Palm OS PDAs, replacing the built-in QWERTY keyboard and Graffiti recognition systems of these PDAs.
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 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.017 |
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".