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
An important problem for many location-based applications is the continuous evaluation of proximity relations among moving objects. These relations express whether a given set of objects is in a spatial constellation or in a spatial constellation relative to a given point of demarcation in the environment. We represent proximity relations as location constraints, which resemble standing queries over continuously changing location position information. The challenge lies in the continuous processing of large numbers of location constraints as the location of objects and the constraint load change. In this paper, we propose an adaptive location constraint indexing approach which adapts as the constraint load and movement pattern of the objects change. The approach takes correlations between constraints into account to further reduce processing time. We also introduce a new location update policy that detects constraint matches with fewer location update requests. Our approach stabilizes system performance, avoids oscillation, reduces constraint matching time by 70% for in-memory processing, and reduces secondary storage accesses by 80% for I/O-incurring 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 | 0.000 | 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 | 0.001 |
| 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