HVDC-WISE D6.1: Definition of the R&R oriented methodology for the use cases
Bibliographic record
Abstract
This document defines a common methodology aligned with the goals of the HVDC-WISE project to be applied to the use cases, thereby providing resilience and reliability-oriented HVDC-based reinforcement. It serves as the first version of Deliverable 6.1 within Work Package 6 (WP6). The associated task in this work package, Task 6.1, primarily focuses on: •The practical methodology's overall framework is described, outlining the required inputs from other work packages (WPs) and expected outputs.•An overview of the use cases, as per WP2, is provided, accompanied by a methodology that includes the grid topology and specifications, along with HVDC architectures for further study in the use cases.•The R&R-oriented planning toolsets developed in WP5 are explained in the context of applying the methodology to the use cases. This involves assessing the grid using different indices, including techno-economic/adequacy indices, as well as reliability and resilience indices.•The design of the methodology involves defining key indicators, practical assumptions, operational scenarios, and types of disturbances tailored to each use case's characteristics. The final version of the deliverable, incorporating updates and improvements to the practical methodology for the use cases, will be submitted in June 2024.
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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 | 0.026 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.014 |
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".