Observed and Modelled Tritium in the Wetland Ecosystem in Duke Swamp near a Nuclear Waste Management Area
Bibliographic record
Abstract
The high mobility of tritium as HTO implies that, under steady-state conditions, the T/H ratio (or equivalently the HTO concentration) is the same in all water compartments of the environment. This is the basis of the specific activity (SA) model, which underlies almost all environmental tritium models. SA concepts apply to organically bound tritium (OBT) as well, since the OBT formed by a given plant process at a given time has a T/H ratio that reflects the ratio in the water that enters into that process. There is no empirical evidence that the bioaccumulation of tritium in aquatic and wetland plants will occur. OBT/HTO ratios less than one is consistently found in the laboratory where the HTO concentrations to which the plants are exposed can be held constant. These data suggest a value of 0.7 for the OBT/HTO ratio under equilibrium conditions in the laboratory. Theoretical considerations suggest that the value of the OBT/HTO ratio in plants is significantly different from one and, in most cases, greater than one. This is primarily due to the much longer residence time of OBT in plants as compared to HTO. The observed HTO concentrations are much higher than OBT concentrations, which makes OBT/HTO ratio smaller than unit in contrast with SA based expectations. In addition to this, the IMPACT model overpredicted HTO and OBT concentrations in plants and animals by a factor of 3 or 4, on average. This work is summary of the AECL funded research project (1).
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".