Bibliographic record
Abstract
Abstract SMRs are a popular topic with a significant number of designs with a wide range of sizes. There has been some assessment done with respect to non-electrical applications including district heating and desalination. The drive of this type of work is to find alternative uses for the SMRs so that the thermal energy is more effective and hence the SMR is more economical. This work is similar in that it studies the synergy that might exist between a remote community and the SMR. Most work for SMRs related to remote communities with the impact as one-way, that is the benefit of the SMR to the community yet the SMR is a separate plant. The consideration here is that the SMR could be used to burn the non-nuclear waste products of the community and return useful products. An example is the plastics generated by the community can be converted into a usable fossil fuel, such as kerosene, by using the heat energy of the SMR. The SMR then has a dependency on the community waste stream. In this manner, the environmental load of the community is reduced yet the community also obtains a locally produced fuel that could be used for heating or transport outside of the community. Considering that diesel fuel costs can be extremely high in remote communities, methods to reduce the fuel costs, including manufacture of their own fuel, can result in a synergistic or symbiotic relationship between the community and the SMR and the community can then have a centralized energy area for supporting neighbouring communities.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".