Coupling of Shoreline Erosion and Biodiversity Loss: Examples from the Black Sea
Bibliographic record
Abstract
The shoreline zone is an area where the sea and land contact and plays a very important role in integrating a sea and its watershed in a whole system. Among the main environmental problems of the coastal zones, two critical ones are - coastal erosion and a biodiversity loss. Problems are most pronounced in semi-enclosed seas as the Black sea. Using results of the long-term studies in different parts of the Black Sea shoreline this paper attempts to make some steps to deepen our understanding of interactions between biodiversity loss and shoreline erosion. An analysis of the results from several case studies was done. Some mechanisms of interrelations between coastal erosion and biodiversity changes are also discussed. The increased concentration of mineral particles, especially hydrophilic ones, as a result of coastal erosion, is a threat not only to benthic organisms, but also to planktonic microalgae and copepods. This negative impact sharply decreases total productivity of coastal waters. De-vegetation of the beaches and cliffs increases movement of sand and soil particles from beaches and cliffs due to high acceleration of wind and water erosion. This also leads to an increased turbidity of marine waters and an associated decrease in their productivity. Other results suggest there is a decrease in mollusk shell production leading to acceleration of a beach degradation which may also increase cliff abrasion. Coastal de-vegetation, marine community degradation and coastline erosion interrelate through a network of chains of cause-and-effect that forms the positive feed-forward and feed-back loops. This creates a self-acceleration mechanism of a development of coastal erosion and biodiversity loss.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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".