ONDE: A Generic XML-Based Development Environment for Optimization of WDM Optical Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Developing optical network optimization algorithms involves representing many different instances related to the network itself: topology, physical layer equipment found in nodes, links, etc. It also requires description of traffic demand. We propose onML; an XML-based structure to fulfill such requirements. The onML structure is used by ONDE, a development environment targeted at optimization algorithms for optical networks. A sought feature of ONDE is a modular design allowing a clear separation of onML files parsing functions, graphical user interfaces and network optimization programs. With such a modular approach, multiple research teams can cooperate to develop and easily share new tools and algorithms. As a result, an XML parser is tailored to parse data in onML-compliant files and is easily interfaced with the main C++ optimization algorithm under development. For graphical user interface aspects, the ONDE environment uses a Java program which is interfaced with the XML parser through the Java native interface (JNI)
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it