Bibliographic record
Abstract
Dos noticias de la empresa privada durante el mes de mayo alimentan el optimismo: Cyrium Technologies y Sunrgi. El Dr. Simon Fafard, cientifico que habia trabajado en el NRC de Canada y fundador de Cyrium Technologies, en 2002, expone ahora su opinion en NRC Newslink, del NRC, edicion Spring 2008: �Los puntos cuanticos, las celulas de triple union y la concentracion optimizada lograran una eficiencia del 44%�. Por parte de Sunrgi, Paul Sidlo, uno de los siete fundadores de Sunrgi, afirma que han logrado concentrar la luz a 1.600 soles con una celula de una eficiencia del 37,5%, y conseguiran vender el kWh a solo 5 centimos de dolar. Ambas empresas prometen comercializar sus productos en un plazo de 12-15 meses. De momento no hay pruebas. Queda la confianza en Cyrium Technologies y en Sunrgi. Se ha avanzado mucho en energia solar, pero todavia no compite con los combustibles fosiles, para generar energia electrica a gran escala. La causa es la ineficiencia de las celulas basadas en el silicio, ademas del alto precio. Pero los recientes avances en nanotecnologia y fotonica van a cambiar la situacion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".