Passive use or active involvement? : the possibilities of D. Swarovski & Co in photovoltaics
Bibliographic record
Abstract
Two facts are in the focal point of this thesis: First that the PV industry is one of the most prospering ones worldwide with a growing rate of around 40% every year, second that for reaching the Kyoto as well as the EU 2020 targets also the industry will be strongly involved. PV-power plants could be one possibility in lowering the CO2 output by producing the needed electricity; supplying optical parts could be the gate to the PV industry for Swarovski as the company is the world leader in producing cut crystal. These optical parts are needed for the concentrating Photovoltaic (CPV), which have a huge market potential as the development of these systems is just at the beginning. At the moment no kind of CPV can be favoured, new developments for whole CPV-systems or of optical parts of them are possibilities for Swarovski. Because of the low feed-in tariffs in Austria, the non-supporting of PV in Tirol and the high long time internal interests of the company itself, it is not possible to run a PV-power plant economically. There are huge differences between fixed or moving systems with advantages to optimal inclined fixed and 1-axis systems, but all of them would have negative financial results. Other reasons like a positive "green" image or being an outrider of new technologies have to be found if a PV-power plant should be erected at the company area.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.085 | 0.021 |
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".