Automating equipment identification in nuclear engineering drawings
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Engineering drawings are critical assets in the nuclear industry, essential for the design, construction, and maintenance of facilities like the Darlington Nuclear Generating Station (DNGS). Manual processes for identifying equipment within these drawings are time-consuming and error-prone, affecting operational efficiency and safety compliance. This paper presents design methodologies to build an Intelligent Drawing Query (IDQ) system, leveraging Cloud Base Artificial Intelligence (AI) including Optical Character Recognition (OCR) technologies to automate equipment identification of tags within nuclear engineering drawings. The paper evaluates and compares the extraction efficiency of cloud-based OCR services including Microsoft’s Azure OCR and Azure Document Intelligence (DI). Additionally, the paper explores best practices to maximize the extraction efficiency. Moreover, the paper explores the potential of OpenAI’s multimodal GPT-4 model for additional detection tasks. Such automation reduces human error, enhances workflows, and ensures compliance with safety and regulatory standards.
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.
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 it