Improving the State of Coastal Areas in the Asia-Pacific Region
Bibliographic record
Abstract
Stresses impacting the coastal zone in the Asia-Pacific region are briefly reviewed under the headings of sustainable coastal activities, coastal ecosystem management, community/resource interactions, coastal resource economics and sustainability, coastal area planning, and integrated coastal policies. Recent contributions on mitigation of these stresses are introduced, with emphasis on the Coastal Zone Asia-Pacific Conference, held in Bangkok, May 2002, where various innovative approaches to research, education, information sharing, and coastal policies aiming at improving the state of the coastal areas were presented. These include the roles of community in integrated coastal management; tools and planning for management of coastal areas; education program and capacity building; and the establishments of national and regional frameworks for integrated coastal management. As appropriate information and its transfer are critical to these processes, an analysis is presented of the content of the database on coastal projects in the region, highlighting areas of research interests, funding sources, and achievements. Another database on coastal ecosystems, currently under development, is presented as an example of the type of resource that can be expected to help advance our knowledge and ability to improve the management of coastal areas. Overall, these tools should allow us, given the political will, to improve the state of coastal areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".