Bibliographic record
Abstract
Abstract Si(Li) lineshapes are generated using a model that incorporates Monte Carlo simulation of the interactions of the incident photon, a very simple approach to energy deposition by the primary (Auger and photo‐) electrons, and an analytical approximation for diffusion of thermalized secondary electrons. The aim of the work is illustrative rather than quantitative. Interactions in the metal contact produce various spectrum artefacts, and interactions in the silicon produce a low‐energy shelf structure with well‐defined steps that have recently been observed experimentally but that are not mentioned in previous Monte Carlo simulation work, although they are to be expected on the basis of electron transport. Details of this structure are strongly influenced by pulse processing time in the electronic system. Artefacts arising from a silicon dead layer can enhance the escape peak intensity, and can assist in estimating this layer's thickness. Inclusion in the simulation of Goto's model for secondary electron diffusion, incorporating reflection at the metal–Si interface, results in a truncated low‐energy shelf, whose threshold energy is determined by the reflection coefficient; its intensity can also be predicted from an earlier, simple, analytical model that combines finite, primary, electron range effects with carrier losses in a region of incomplete charge collection whose thickness is well defined. Copyright © 2001 John Wiley & Sons, Ltd.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".