Bibliographic record
Abstract
Recent investigations of iodine behavior under radiolytic conditions have demonstrated that kinetics, not thermodynamics, will govern iodine speciation and partitioning under conditions typical of those expected in a reactor containment during an accident. In the presence of radiation, iodine volatility is orders of magnitude higher than that expected based on thermodynamic calculations. Kinetic studies have contributed extensively to the existing database of iodine chemistry and have several implications for modeling iodine behavior for safety analyses. For example, as a result of these investigations, many uncertainties in the iodine database, such as those regarding thermal oxidation of iodine, which were formerly regarded as reactor safety issues, are now considered to be relatively unimportant. In contrast, previously unconsidered factors, such as the effect on aqueous chemistry of impurities originating from surfaces, are now recognized as playing major roles in determining iodine volatility. An updated review of the existing literature regarding iodine behavior is provided, with a focus on recent developments. A critical evaluation of the data in the context of developing a model for iodine behavior under reactor accident conditions is also 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".