Soil contamination in nuclear reactor surrounding areas in Savar, Bangladesh using instrumental neutron activation analysis method.
Bibliographic record
Abstract
In this paper, the contamination level of fourteen elements (Al, Ca, Mg, V, Ti, Na, Mn, As, Br, La, Sm, Sc, U, Yb) from different soil samples of the nuclear reactor surrounding areas in Savar, Bangladesh was investigated with instrumental neutron activation analysis (INAA) method. Generally, neutron activation is most reliable tool for determining the concentration of the trace elements and heavy metals by measuring the gamma rays released while decaying of the radioisotopes created from the interaction of neutrons with atoms. In this study, the soil contamination level of all heavy metals and trace elements was compared to the range, mean, and median values of the World soil as well as with other studies. The concentration of K (1.65%), U (3.17 mg kg -1 ), Sm (6.10 mgkg -1 ) and Sc (10.53 mg kg -1 ) is relatively higher than that of the World mean value. Furthermore, compared to world range value for soil revealed that the high concentration range is also observed for Al (3.87-9.39%), Na (0.37-0.76%) and Yb (0.11-5.36 mg kg -1 ). However, contamination level of all elements was not evenly distributed in the studied areas. For instance, the pollution levels of U, Yb, Mn, and V were unevenly higher in the areas near to the overhead tank, decay tank, in the vicinity of the underground tank, cooling tower and reactor building area. Interestingly, the contamination level of potassium is high everywhere near the nuclear reactor installations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".