Budidaya Lele Dalam Ember dan Upaya Pemasaran Digital Menggunakan Media Sosial
Bibliographic record
Abstract
The community service program titled 'Catfish Farming in Buckets and Digital Marketing Efforts Using Social Media' aims to provide participants with both theoretical knowledge and practical skills on simple, cost-effective, and appropriate methods for catfish farming in limited spaces. This initiative responds to the needs of the Tembalang community, which seeks to engage in farming activities on limited land while also exploring ways to market their agricultural products via social media platforms. The training was conducted using a participatory approach, which incorporated theory, hands-on practice, and interactive discussions. The content of the training included, among other things, techniques for catfish farming in buckets as well as strategies for utilizing social media to market the cultivated catfish products. Participants were guided to apply proper aquaculture practices using the provided cultivation buckets and equipment, and were then asked to capture images of their farming outcomes to be used as marketing content on social media. The implementation of this activity proceeded smoothly, and the participants showed strong enthusiasm throughout the process. It is anticipated that this training will enable the Tembalang community to effectively leverage technology, particularly social media platforms such as WhatsApp, as a tool for digital marketing of their catfish farming products.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.005 |
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".