Optoelectronics - Advanced Materials and Devices
Bibliographic record
Abstract
Optoelectronics - Advanced Materials and Devices is a second edition following the initial Optoelectronics - Materials and Techniques book published in 2011 as part of the InTech collection of international works on optoelectronics. Optoelectronics, as the discipline devoted to the study and application of electronic devices that emit, detect, and otherwise control light, has widely proliferated globally and enabled many of today's modern conveniences. Because of this ubiquity, new applications and novel optical phenomena continue to drive innovation. Accordingly, as with the first book of the collection, this book covers recent achievements by specialists around the world. The growing number of countries participating in this endeavor including now Brazil, Canada, China, Egypt, France, Germany, India, Italy, Japan, Malaysia, Mexico, Moldova, Morocco, Netherlands, Portugal, Romania, Saudi Arabia, South Korea, Taiwan, Ukraine, USA, and Vietnam as well as joint participation of the US and Moldova scientists in edition of this book and writing one of its Chapters testify to the unifying effect of science. An interested reader will find in the book the description of properties and applications employing organic and inorganic materials, such as different polymers, oxides and semiconductors, as well as the methods of fabrication and analysis of operation and regions of application of modern optoelectronic devices.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.070 | 0.083 |
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".