Bibliographic record
Abstract
Current soft QWERTY keyboards often consume a large portion of the screen space on portable touchscreens. This space consumption can diminish the overall user experi-ence on these devices. In this paper, we present the 1Line keyboard, a soft QWERTY keyboard that is 140 pixels tall (in landscape mode) and 40% of the height of the native iPad QWERTY keyboard. Our keyboard condenses the three rows of keys in the normal QWERTY layout into a single line with eight keys. The sizing of the eight keys is based on users' mental layout of a QWERTY keyboard on an iPad. The system disambiguates the word the user types based on the sequence of keys pressed. The user can use flick gestures to perform backspace and enter, and tap on the bezel below the keyboard to input a space. Through an evaluation, we show that participants are able to quickly learn how to use the 1Line keyboard and type at a rate of over 30 WPM after just five 20-minute typing sessions. Using a keystroke level model, we predict the peak expert text entry rate with the 1Line keyboard to be 66--68 WPM.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.052 | 0.018 |
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".