TRICO II Core Inventory Calculation and its Radiological Consequence Analyses
Bibliographic record
Abstract
Two severe accident scenarios are investigated in this paper as they have never been considered previously in the safety analysis report (SAR) of the Congo TRIGA Mark II research reactor (TRICO II) in Kinshasa, the Democratic Republic of Congo. The source term is derived from the reactor core after two postulated accidents: (1) a large plane crash with total destruction of the reactor building and (2) full damage of one fuel element while the reactor building remains intact. Total effective dose (TED), after core inventory, and dose profiles to human organs are derived to assess the operational safety of the reactor. Results from the study will be used to upgrade the current SAR of the reactor as the reactor safety and licensing concepts are changing over the years; the knowledge and lessons learned from the past experience are being updated accordingly with the available data. TEDs to workers of the facility show that higher values are obtained at areas near the source term at the time of the plane crash accident, which dies out as quickly as the plume is carried away following predominant meteorological conditions at the site. The situation with one fuel element totally damaged poses no threat as far as radiation protection is concerned and reveals a maximum TED of 1.30×10−7 mSv at 100 m from the reactor core. This shows that the operation of this type of research reactor is reliable and safe.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".