A divide and conquer partitioning method for bigraph data in industrial grade intelligent unmanned distributed systems
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Bibliographic record
Abstract
With the increasing deployment of intelligent unmanned systems in industrial fire monitoring and emergency response applications, the challenge of balancing storage loads among distributed nodes has become more critical. This paper proposes a divide-and-conquer partitioning method based on the Bigraph model, which jointly captures the spatial nesting and communication dependencies of system nodes. The method includes hypergraph transformation, CSR structure generation, METIS-based partitioning, and a refinement process guided by location constraints. By incorporating node data volume as a weight factor in the partitioning process, the method effectively reflects actual storage loads and achieves balanced subgraph allocation. This approach is especially suitable for managing uneven task distribution in modular, scalable distributed systems. Experimental results demonstrate that the proposed method significantly improves load balance and data locality after partitioning, showing strong adaptability and scalability.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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