H2020 Platone Italian Demonstrator Use Case 1-2 Market 1st quarter 2022
Bibliographic record
Abstract
areti_market_flexibility_TSO_requestes areti_market_flexibility_DSO_requestes areti_market_flexibility_Aggregator_bids areti_market_flexibility_settlement areti_market_flexibility_outcomes - TSO flexibility requests: Starting Time Duration Market Type Market Session Flexibility Service Type Volumes Grid Area - DSO flexibility requests: Starting Time Duration Market Type Market Session Flexibility Service Type Volumes, Grid Area - Aggregator bids: Starting Time Duration Market Type Market Session Flexibility Service Type Volumes PoDs List - Settlement data: Pod Requested Active Power Measured Active Power Requested Reactive Power Measured Reactive Power - Market Outcomes: Market Outcome Id Market Type Market Session Flexibility Service Type Other than TSO flexibility requests, to test the demo, other data could be simulated. In this case, it will be indicated in the metadata documentation. (Useful link to consult Italian UC: https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-1-voltage-management/; https://smart-grid-use-cases.github.io/docs/usecases/platone/uc-it-2-congestion-management/, https://platone-h2020.eu/data/deliverables/864300_M12_D1.1.pdf)
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.373 | 0.289 |
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".