Multicriteria PCF Design: An Accurate Photonic Crystal Fiber Design Tool
Bibliographic record
Abstract
Summary form only given. In recent years there has been a major development in optical communications and a new generation of fibers was introduced. These fibers, called Photonic Crystal Fibers (PCF) have unusual propagation properties. This paper presents multicriteria PCF design tool, which is an accurate PCF design based on multicriteria classification. This method combines the deductive and the inductive learning and it is introduced for the first time in the field of optical fibers. The multicriteria decision analysis (MCDA) makes it possible to evaluate the optical proprieties of PCFs by determining the resemblance of a PCF fiber to specified PCF category. The MCDA avoids recourse to classical distances and makes it possible to use quantitative and/or qualitative criteria. Moreover, it defeats some difficulties encountered when data are expressed in different units. These advantages allow the new multicriteria classification method to be employed easily to the diagnosis and to the design of photonic-crystals fibers.
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How this classification was reachedexpand
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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".