Where Did the Nuclear Industry Go Wrong? (Part 2 Q&A)
Bibliographic record
Abstract
When it comes to energy, there seems to be a very large gap between scientific, economic and environmental facts, on the one hand and what the general public believes, on the other. While the public can be excused, because all the energy technologies involved are much more complex than they appear on the surface, the people behind each of these technologies have contributed in a significant way to the existence of many of these gaps in communication.This presentation will focus on one of the largest gaps - that which exists about nuclear power. This gap will be identified; the causes and especially the consequences of this gap will be analyzed.Speaker: Cosmos VoutsinosCosmos Voutsinos is a mechanical engineer graduated from the University of Waterloo and McMaster University. He specializes in energy conversion systems, in particular conversion to electrical energy. He has worked in various capacities in the design, construction and manufacturing of equipment and operation of energy conversion facilities for most energy technologies. This list includes nuclear and fossil fuel fired power plants as well as a variety of renewable energy projects. In nuclear power he has participated as a design engineer, as a construction manager, as a manufacturer of nuclear equipment and as a techno-economic consultant. His work has brought him from Canada, to USA, Taiwan, China, S. Korea, Japan and Belgium. He has been a member of the Canadian Nuclear Association (CNA), Organization of Candu Industries (OCI) and the Canadian Nuclear Society (CNS). At present he retains his Alberta P. Eng membership.Over his 40 year working career Cosmos has got to know not only the energy technologies involved but also the people and the social structures behind them.
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 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.792 | 0.030 |
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; both teacher heads agree on what is shown here.
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".