Bibliographic record
Abstract
Experimental data and analytical models that should be considered in assessing the transport properties of plutonium aerosols following a hypothetical reactor accident have been examined. Behaviors of released airborne materials within the reactor containment systems, as well as in the atmosphere near the reactor site boundaries, have been semiquantitatively predicted from experimental data and analytical models. The fundamental chemistry of plutonium as it may be applied in biological systems has been used to prepare models related to the intake and metabolism of plutonium dioxide, the fuel material of interest. Attempts have been made to calculate the possible doses from plutonium aerosols for a typical analyzed release in order to evaluate the magnitude of the internal exposure hazards that might exist in the vicinity of the reactor after a hypothetical LMFBR (Liquid-Metal Fast Breeder Reactor) accident. Intake of plutonium (using data for {sup 239}Pu as an example) and its distribution in the body were treated parametrically without regard to the details of transport pathways in the environment. To the extent possible, dose-response data and models have been reviewed, and an assessment of their adequacy has been made so that recommended or preferred practices could be developed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".