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
Cellular and wireless communication, portable computers and satellite services promise mobile users to have access to information anywhere and anytime. However, mobile computing is characterized by many constraints: small, slow, battery-powered portable devices, variable and low-bandwidth communication links. Together, they complicate the design of mobile information systems and require rethinking traditional approaches to information access and application design. The relative resource shortage of portable devices as well as their lower trust and robustness argue for reliance on static servers. The need to cope with unreliable and low-performance networks, as well as the need to be sensitive to power consumption argues for self-reliance. Any feasible approach to mobile computing must strike a balance between these competing issues. This balance cannot be static as the environment of mobile computing changes, it must react, or in other words, the applications must be adaptive. We propose an approach for adaptive mobile applications based on mobile code. To demonstrate our ideas, we are developing a mobile code toolkit and implemented a resource-intense application, an MP3 player.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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