Why Implement Distributed Systems in Municipal Music Schools in Colombia?
Bibliographic record
Abstract
In Colombia, since 2003, the public policy "National Plan of Music for citizen Coexistence" has been implemented as a government measure, which provides music courses in each one of the country's municipalities. This plan does not take into account the use of technologies to share the experiences of each one of the schools.Taking into account the above- mentioned points, this article focuses its attention on the search of technologies that can be used to share multimedia content such as Content Delivery Network (CDN), Learning Management System (LCMS) and Distributed Systems in order to indicate which technology is the most appropriate to fulfill this purpose.In that sense, through the Wireshark tool, network traffic is captured for each one of the tests performed: Upload, display and deletion of videos for each configured technology (CDN, LCMS and Distributed Systems), having as comparison parameters the following aspects: Real-time Traffic, Total Traffic Vs Packet Loss, Communication Exchange and Protocol Hierarchies. After doing that, we proceed to take statistics to be analyzed and obtain the comparative results that are needed for this research.Finally, one can conclude the comparison of the results of each technology: that it is appropriate that the municipal schools of music use distributed systems because the size of the packets sent is smaller than that one that is sent by CDN and CML technologies. There are no multiple communication jumps. No prior approval is required to publish the content and there is no limitation on the size of the content to be published.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".