Cooperative cover location problems: The planar case
Why this work is in the frame
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Bibliographic record
Abstract
A cooperative-covering family of location problems is proposed in this paper. Each facility emits a (possibly non-physical) “signal” which decays over the distance and each demand point observes the aggregate signal emitted by all facilities. It is assumed that a demand point is covered if its aggregate signal exceeds a given threshold; thus facilities cooperate to provide coverage, as opposed to the classical coverage location model where coverage is only provided by the closest facility. It is shown that this cooperative assumption is appropriate in a variety of applications. Moreover, ignoring the cooperative behavior (i.e., assuming the traditional individual coverage framework) leads to solutions that are significantly worse than the optimal cooperative cover solutions; this is illustrated with a case study of locating warning sirens in North Orange County, California. The problems are formulated, analyzed and solved in the plane for the Euclidean distance case. Optimal and heuristic algorithms are proposed and extensive computational experiments are reported.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 0.001 |
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