Feedback mechanism validation and path query messages in the label distribution protocol
Bibliographic record
Abstract
In constraint-based routing a topology database is maintained on all participating nodes to be used in calculating a path through the network. This database contains a list of the links in the network and the set of constraints the links can meet. Since these constraints change rapidly, the topology database will not be consistent with respect to the real network. A feedback mechanism was proposed by Ashwood-Smith, et al, to help correct the errors in the database. It behaves like a depth first search, and is meant to be useable only when the database sees the availability of resources to be more than they really are. In this mechanism, the source node can learn from the successes or failures of its path selections by receiving feedback from the path it is attempting. The received information is used in the subsequent path calculations. We validated the feedback algorithm to see how it behaves in all database situations, and found out that the feedback algorithm was helpful in all cases (not only when it was optimistic). We also propose adding query messages to make the feedback algorithm behave more like breadth first search. The path query messages algorithm reduces the retry attempts in setting up a path, and also utilizes the network more effectively by gathering much more information about the resources.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".