Penentuan Kelayakan Pemberian Bantuan BBM pada Nelayan menggunakan Metode Vikor
Bibliographic record
Abstract
Fishermen is a term for people whose daily work is catching fish or other biota that live on the bottom or surface of the water. Fishermen can also discuss and share experiences in overcoming problems in the fisheries and marine sector to meet the needs of fishermen. The government of the Fisheries and Marine Service is required to support the need for subsidized fuel for fishermen. The increase in fuel oil (BBM) has an impact on the operational costs of fishermen, because BBM is one of the main components in the daily activities of fishermen, with the high price of BBM, fishermen will face an increase in operational costs that they must spend to carry out fishing activities. This can reduce the income earned by fishermen, because higher costs will affect the profits that fishermen get from the sale of fish. So the Fisheries and Marine Service of Langkat Regency Provides Assistance for Fishermen in Langkat Regency, from the many assistance that have been distributed in this thesis I took a sample of the Fishermen of Secanggang Village. Based on the research conducted, the results obtained were that the first alternative that was a priority to receive subsidized fuel assistance for fishermen in Langkat Regency with a Q value = 0 and was entitled to be recommended. And the next priority was the second alternative with a Q value = 0.492.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".