Automated Operation With Redundant Ultrasonic Reactor Water Level Monitoring System
Bibliographic record
Abstract
The proposed ultrasonic Reactor water Level Monitoring system is intended for monitoring water level in the hot leg of a nuclear reactor during Operation at Reduced Inventory (ORI) with the core in place as may be needed for certain maintenance activities. Since the existing fuel bundles continue the production of heat thorough the decay process, it is critical to maintain a certain water flow rate through the reactor. The Residual Heat Removal System (RHRS) handles this task during the reactor shutdown. The free water surface during ORI subjects the RHRS to the risk of air intake. Such an event would trigger a set of emergency procedures for priming the RHRS and bringing it back online. Some of the possible outcomes of such an event are: extension of outage, handling of the extra regulatory reporting tasks, potential requirements for extra testing after system recovery, possible damage to RHR pump, boiling of reactor water, generation of airborne contamination, and fuel damage. Another risk during ORI is reactor overflow. The major immediate concern is the safety of personnel in case of accidental overflow. This event could result in contamination and the related outage delays. The previously existing systems have always had problems with transducer reliability. This has lead to the need for removal and re-installation of their transducers from outage to outage. However, the proposed system uses a redundant design with permanently mounted transducers. The system uses advanced digital signal processing techniques to determine the water level.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".