Study on autonomous search for multiple radioactive leakage sources based on updated infotaxis in nuclear emergency rescue
Bibliographic record
Abstract
Nuclear facilities face leakage risks from natural hazards, human errors, or external attacks, often generating multi-point radioactive leakage sources that produce large-scale dynamic radiation plumes through atmospheric dispersion and multi-source superposition. Unlike orphan source recovery operations (e.g., retrieving displaced or poorly shielded sealed radioactive sources in localized fields), nuclear emergencies require urgent identification of leakage points to enable real-time leakage sources suppression. Based on the Daya Bay nuclear power plant scenario, this study proposes a multi-source radiation leakage inversion model based on an updated infotaxis algorithm, which incorporates the information entropy of superimposed radiation fields from multiple sources. The search path of the mobile detector is optimized by integrating a movement strategy activation function to adjust subsequent positions. Simulation results demonstrate that the hexagonal path unit enhances search efficiency by 21.78% compared to traditional quadrilateral path units. In a scenario involving three radioactive leakage sources, the mobile detector successfully identifies all sources locations through exhaustive grid sampling, achieving an average positioning error of 5.73 m. This approach provides a novel perspective for identifying multiple radioactive leakage sources in nuclear accidents.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".