Impact of land use on coastline change of island cities: A case of Zhoushan Island, China
Bibliographic record
Abstract
Anthropogenic activities have an important effect on the natural coastlines of island cities as a result of urbanization and population agglomeration in developing countries. In order to identify the relationships between land use and coastline changes in the typical island city, this study used land use data, remote sensing technology, and geographic information system (GIS) technology to analyze the land use situation and coastline changes in the coastal zone of Zhoushan Island in China. The results show that, from 2012 to 2017, the coastal land area of Zhoushan Island increased from 121.54 km2 to 126.00 km2. New agricultural land accounted for the highest proportion of total land use growth (46.86%), followed by residential land, land for street, and transportation and industrial land. The length of the coastline increased from 137.98 km to 142.7 km. The indicators of agricultural land, industrial land, land price, and production coastline had a significant positive impact on the rate of coastline changes. Moreover, the coastline was more than just a natural coastline but also had 336 multiple functions in terms of production, daily living, leisure, and transportation. The study found that the rapid growth of tideland reclamation-based, land reclamation-based aquaculture, and the harbour/port construction-based logistics industry are the main reasons for the continuous changes in coastlines.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".