A state of the art on coastal environmental protection using radioisotope tracer technology
Bibliographic record
Abstract
Construction of artificial structures has caused a sediment process change due to the variation of hydraulic condition in Korea. Subsequently we have a serious problem of shoaling for shoreline deformation, siltation of the harbor and shipping channel. To protect those abnormal environmental changes, a large estimate has been spent for additional construction such as outer wall facilities, littoral nourishment and dredging. Systematic long-term studies should be carried out to understand the causes of environmental change. In addition, comprehensive plan is required for its monitoring and prevention. The radioisotope application studies for coastal environmental protection have not been actively performed only in the developed countries like France, Canada, and Australia etc., but also in many developing countries like Poland, India. Since KAERI has performed two experiments in costal area of Korea in 1960s, no more study has been reported. Recently the studies of radiotracer application technology is getting more interested in terms of on-line data acquisition and analysis for the validation of the numerical simulation models. The experiment using radiotracer becomes an important part of the method to solve the problems happening in coastal environment, as it supplies data with high confidence in the field. On the basis of the experience obtained from the researches for industrial application of radiotracer technology, KAERI is going to make its first step to the development of the radiotracer technology for costal environmental studies.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".