Full Transcripts - On the Potential of ChatGPT to Generate Distribution Systems for Load Flow Studies using OpenDSS
Bibliographic record
Abstract
This is the full ChatGPT transcript for the IEEE Power Engineering Letter "On the Potential of ChatGPT to Generate Distribution Systems for Load Flow Studies using OpenDSS". The abstract for the letter is as follows:In recent years, the Large Language Models have developed at an unprecedented pace with the potential to revolutionize various fields of knowledge, including power systems. This letter illustrates the current status and potential use of GPT-3.5 and GPT-4 to create test distribution systems modeled as DSS files for load flow studies using OpenDSS, focusing on educational and research purposes. A performance comparison of GPT-3.5 and GPT-4 large language models (with the ChatGPT frontend) has been conducted. More specifically, the ability of ChatGPT to generate simple test circuits to run in OpenDSS is verified, including elements such as lines, loads, transformers, and photovoltaic generators. The ability of ChatGPT to identify and solve simple engineering problems applied to the generated circuits is also briefly discussed. The results demonstrate that GPT-4 has the potential to create functional circuits and propose solutions for engineering problems if adequate guidance and examples are provided.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.034 |
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".