Bibliographic record
Abstract
In this chapter, we explore the captivating domain of terahertz (THz) technology, commencing with an in-depth examination of the physics governing electromagnetic wave generation, particularly in the THz range. The discourse incorporates recent technological strides, notably the pivotal role of femtosecond lasers in advancing THz radiation generation and detection. Terahertz spectroscopy and imaging are introduced, illuminating the multifaceted applications across material analysis. Special focus is directed towards THz spectroscopy techniques such as terahertz time-domain spectroscopy (THz-TDS) and time-resolved terahertz spectroscopy (TRTS), which unravel dynamic material properties. The narrative extends to intricate setups and assets associated with these techniques. Furthermore, the chapter delves into the ongoing landscape of THz imaging, encompassing various methodologies, including terahertz near-field imaging. Noteworthy developments in THz technology are emphasized, with a spotlight on biomedical breakthroughs, particularly in early-stage cancer detection. Concluding the exploration, we delve into prospective avenues, providing foresight into the future of THz technology. Emphasis is placed on potential breakthroughs, opportunities, and the transformative impact across scientific domains through interdisciplinary collaboration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 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".