The Effect of Human Activities Towards Coastal Dynamics and Sustainable Coastal Management
Bibliographic record
Abstract
One of the disasters that often occur in coastal areas is abrasion. Abrasion causes coastal dynamics, including the East Coast of Rembang, Kragan Village, Kragan District, Rembang Regency. From 1975 to 1990, at least 50 meters of land from this area has been lost due to abrasion. This dynamic may become one of the causes of unsustainable management of the coastal environment and its natural resources. Various efforts have been made to overcome abrasion, but abrasion continues to hit this area, even until 2020. Qualitative and quantitative approaches were carried out in this study to discover the coast dynamics and various human activities that may trigger abrasion. Image interpretation, observation, interviews, and questionnaires were used as data collection techniques at three observation points in the Kragan Village area. This study concludes that the beach in Kragan Village has experienced dynamics with a total land loss of 46 meters from 2003 to 2020. Harmful activities carried out by humans resulted in abrasion so that the coast experienced dynamics. Human activities also affect coastal management, namely the basic principles of integrated coastal management and processes in the management of coastal areas.
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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 | 0.001 |
| 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".