Locating a cycle in a transportation or a telecommunications network
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 Several problems arising in transportation and telecommunications can be cast in terms of optimally locating a cycle in a graph. This paper proposes a classification of cycle location problems under two main headings. In Hamiltonian problems, all vertices of the graph must belong to the cycle. The most important cases are the traveling salesman problem (TSP), the TSP with precedence constraints, the clustered TSP, the TSP with backhauls, the TSP with time windows, several classes of pickup and delivery problems, and stochastic TSPs. In non‐Hamiltonian problems, only a subset of vertices must be visited. These problems include the generalized TSP, the covering tour problem, the median cycle and ring star problems, and several cycle location problems with revenues. These problems are modeled within a unified framework and algorithmic strategies are provided, together with computational results. Several applications are also described. © 2007 Wiley Periodicals, Inc. NETWORKS, Vol. 50(1), 92–108 2007
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 | 0.000 |
| 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