SUPER PV Progress Report – Developing and Testing Innovative High-Quality PV Systems to Regain European Leadership in the PV Market
Bibliographic record
Abstract
The PV sector is one of the fastest growing industries globally, between 2010 and 2019 global solar installations grew from around 16 gigawatts to over 105 gigawatts in 2019. This significan rise in photovoltaics has been in large part due to a substantial decrease in the price of PV cells and modules. At the beginning of the decade the average Multi Crystalline Silicon module cost roughly 2USD per watt, now (early 2020)the average Multi-Si costs around 0.2USD per watt. This expansion of the PV sector has coincided with a geographical shift in manufacturing capacities. Currently East and Central Asia produce over 80% of the global supply. In order to remain competitive and recapture a larger market share of PV manufacturing, European PV companies will need innovate along the PV supply/value chain. SUPER PV project is targeting competitiveness of European produced PV systems integrating 26 partners from all over the Europe delivering innovations from research centers to manufacturing lines. Expected cost reduction will be realized through targeted technological and business innovations which will significantly improve the levelized cost of electricity (LCOE) of European systems. A year and a half into the project these price reductions and innovations have begun to take shape. This poster will outline the status of this ground-breaking project.
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.008 |
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".