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
In this paper, we consider the evacuation problem that consists of grouping and routing evacuees to safe zones. Grouping evacuees reduces congestion on roads, addresses fuel shortage and supports individuals with limited access to transportation means. We propose a mixed integer programming model where individuals with vehicles are instructed to pick up others along their route in order to evacuate the maximum number of individuals within a limited time. Since evacuation decisions and plans must be made as quickly as possible, we propose two heuristics that provide comparable solutions within a short computational time. The first heuristic is inspired from the Clarke-Wright savings heuristic for the vehicle routing problem, while the second heuristic is based on maximum bipartite matching. Computational results show that the proposed heuristics find solutions in less than a second for instances with up to 40 evacuee locations. We also present extensions of the evacuation problem that include vehicles with different capacities, that minimize the time to evacuate everyone, and that find the optimal vehicle placement.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| 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