Bibliographic record
Abstract
Advancement of fire risk analysis methods has led to a widespread development of detailed fire probabilistic risk assessments (PRA) at nuclear power plants.The Fire PRA assesses the possibility of a fire at critical plant locations and evaluates the fire damage.Fire PRA also evaluates the effect of the fire on safety-related cables and equipment.The scope of the Fire PRA is limited to demonstrating that the fire safe shutdown objectives and performance criteria are met.Hence, the Fire PRA is only used for plant areas where fires may have a potential impact on systems, structures, and components (SSCs) that are required to perform the fire safe shutdown functions.Canadian Nuclear Power Plants (NPPs) use NUREG/CR-6850 or some portion of it in performing Fire PRAs.There are differences between Canadian and U.S. nuclear reactor types.The generic fire ignition frequencies provided in NUREG/CR-6850 reflect the experiences only of the U.S., and not Canada.There are also differences in systems, structures, and components when comparing Canadian nuclear reactors, which use pressurized heavy water, to U.S. reactors, which use light water.Differences are also found in the core of the reactors, which contain uranium fuel.CANDU nuclear reactors have a number of inherent safety features that differentiate them from light water reactors (LWRs), while light water reactors have other systems that do not exist in CANDU plants.Consequently, fire events that are related with these types of systems in LWR plants should not be considered in CANDU plants.A CANDU Fire Database was developed by the Canadian Nuclear Safety Commission (CNSC) to collect and maintain data of all CANDU related fires.There are tree) for CANDU reactors, which is an essential step in the CANDU Fire PRA.A CANDU Fire PRA methodology for CANDU reactors was developed, and two fire zones were selected to demonstrate the use of the CANDU Fire PRA methodology.In addition, High Energy Arc Fault (HEAF) risks in CANDU reactors were examined and analyzed, and recommendations were given to mitigate the risks and consequences of any potential HEAF fire events.vAcknowledgments I am sincerely grateful to my advisor, Dr. George Hadjisophocleous, for his essential role in my doctoral work.He provided me with the needed direction, support, and knowledge during my first few semesters.After his gracious assistance, I felt ready to undertake the research on my own and expand into new research areas.He provided me the independence to work autonomously while remaining available to contribute muchappreciated feedback, guidance
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".