Error-Resilient Routing for Supporting Multi-dimensional Range Query in HD Tree
Bibliographic record
Abstract
The Hierarchically Distributed Tree (HD Tree) is a novel distributed data structure built over a complete tree. The purpose of proposing this new data structure is to better support multi-dimensional range query in the distributed environment. HD Tree doubles the number of neighbors at the cost of doubling total links of a tree. The routing operation in HD Tree is supposed to be highly error-resilient. In HD Tree, the routing table size is determined by the system parameter k, and the performance of all basic operations are bound by O(lg(n)). Multiple routing options can be found between any two nodes in the system. This paper explores fault tolerant routing strategies in HD Tree. The experimental results produce very limited and unnoticeable increases in routing cost when conducting range queries in an error-prone routing environment. The maximum failures we have tested are about 5 percent of routing nodes. The experimental results also indicate that higher fault tolerant capability requires finer consideration in the design of the error-resilient routing strategy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".