Varying Display Size and Resolution for Digitizing Vector and Raster Targets: A Study of Digitizing Performance on Multiple-Monitor High-Resolution Displays
Bibliographic record
Abstract
Despite technological advances, digitizing is still used for digital data creation. Although users work with large, high-resolution data sets, their workspace is often limited to a small, one-monitor viewing window. A low-cost upgrade is to build a multiple-monitor display. Multiple-monitor displays provide an increase in size and resolution, allowing concurrent access to greater context and detail, which may be particularly helpful for digitizing. To investigate the possible benefits of digitizing on multiple-monitor displays, the authors asked 57 participants to perform a map-reading test that included vector and raster target digitizing tasks. Participants took the test on one of three displays: one, four, or nine monitors. The testing program stored participants’ digitized shape files and the viewing area used for digitizing. Although participants were more efficient on the larger displays for other tasks, no statistically significant differences were found for the vector or raster digitizing tasks among display configurations, using goodness-of-fit and shape metrics to compare results. However, larger displays still potentially offer benefits for digitizing. Guideline provision and variability in image interpretation for vector and raster digitizing, respectively, may have been prevailing factors. Additionally, lack of motivation, along with the physical demands and unfamiliarity of large displays, may have hindered the realization of potential benefits.
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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 | 0.005 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".