Bibliographic record
Abstract
As one of the coastal areas, Bengkulu Province has very potential natural wealth.Even so, the development has not run optimally, where the level of community welfare is still low and development is still lagging behind.This study aims to identify the strategy for developing coastal areas in Bengkulu Province using SWOT analysis.The results of the study found that the strategy for developing the coastal region of Bengkulu province could be done using an aggressive strategy, which illustrates that the situation is very good because there are forces that are utilized to seize profitable opportunities, to overcome various weaknesses and threats.The development strategy of the coastal region of Bengkulu province can be done by optimally utilizing coastal and marine natural resources to be carried out to meet the broad share of the share of domestic and foreign fishery products, enforce existing legislation to increase the level of domestic fish consumption, acceleration of government policies to accelerate the development of marine and fisheries in order to provide raw materials for processed and fishery products, strengthen permanent government institutions to meet the demand for processed marine and fishery products, increase the allocation of funds managed by the government to increase product competitiveness and prices of marine products , increasing the development of infrastructure/facilities, advances in marine and fisheries technology in developing maritime tourism and tourism as well as increasing foreign and domestic investment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".