The threat of alluviation of lakes resulting from torrents (case study: lake volvi, north greece)
Bibliographic record
Abstract
Lakes or natural reservoirs are usually formed by the water supplied by torrents runoff.Furthermore, runoff, according to the torrential environment that forms the watershed (climate, geology, vegetation and relief) is usually associated with debris fl ow.As a result, many lakes and reservoirs are undergoing an alluviation process which leads to an important reduction of their total capacity.In fact, long-term process of torrents may threaten their existence.Lakes and reservoirs have an important role since the impounding water is used for multi purposes such as municipal supply, irrigation and recreation.Often, lakes form wetlands that are really important and are protected by national or international treaties.In this paper, the methodology we use involve the analysis of torrential environment (potential) of the lake Volvi watershed.Moreover, the mean annual sediment yield calculated according to Gavrilovic method so as to assess the risk of alluviation.Also, Kronfellner-Kraus method used in order to take under consideration the sediment that can fl ow into the lake after an extraordinary fl ood event.Finally, fl ood control works proposed so as minimize the threat of alluviation.The present study shows that the torrential environment (potential) is rather moderate, although during intense rainfalls great volume of water and debris discharge are produced so the only solution against alluviation is the construction of fl ood control works.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".