Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract With advances in computer graphics, a number of innovative approaches to information visualization have been developed (e.g., Card et al, 1991 ). Some of these approaches create a mapping between information and corresponding structure in a virtual world. The resulting virtual worlds can be fully three dimensional (3D) or they can be implemented as a series of 2D birds‐eye “snapshots” that are traversed as if they were in 3D, using operations such as panning and zooming interactively (2.5D). This paper reports a study that contrasted 3D and 2.5D performance for people with differing levels of spatial and structure learning ability. Four data collection methods were employed: search task scoring; subjective questionnaires; navigational activity logging and analysis; and administration of tests for spatial and structure‐learning abilities. Analysis of the results revealed statistically significant effects of user abilities, and information environment designs. Overall, this research did not find a performance advantage for using a 3D rather than a 2.5D virtual world. In addition, users in the lowest quartile of spatial ability had significantly lower search performance in the 3D environment. The findings suggest that individual differences in traits such as spatial ability may be important in determining the usability and acceptability of 3D environments.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it