PENGUATAN SUMBER DAYA MANUSIA NELAYAN DESA LOPO KECAMATAN BATUDAA PANTAI MELALUI PELATIHAN TUNE-UP MESIN KETINTING
Bibliographic record
Abstract
This community service activity is the second of three years of village service assisted by the Department of Industrial Engineering, Faculty of Engineering UNG which has started in 2020 and will end in 2022. The Department of Industrial Engineering carries out village development service activities in four different locations, one of which is Lopo Village. Batudaa Pantai District. The purpose of this service activity is to increase the potential of Lopo Village human resources through tune-up training for fishing boat propulsion engines (ketinting). The implementation of activities is divided into 3 stages, namely (1). Preparation stage, where surveys and problem identification are carried out. (2). The stages of program implementation are the provision of training materials and practices (3). Reporting and publication of service activities. The results obtained from this service activity are, after the tune-up training, the fishing communities participating in the training are able to tune up the ketinting machine that they use to earn a living independently without having to use professional staff which of course costs money.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.003 |
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".