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Record W2625232142 · doi:10.1017/s1431927600036357

'Secondary’ Electron Detector Design and Positioning in the Variable Pressure Scanning Electron Microscope: The Colour Option

2000· article· en· W2625232142 on OpenAlexaff
Brendan Griffin, James R. Browne, Louise M. Egerton‐Warburton, Dominique Drouin

Bibliographic record

VenueMicroscopy and Microanalysis · 2000
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsElectronDetectorScanning electron microscopeElectron microscopeSecondary electronsMaterials scienceVariable (mathematics)Environmental scanning electron microscopeOpticsOptoelectronicsPhysicsMathematicsNuclear physics

Abstract

fetched live from OpenAlex

Abstract The simple models for low energy secondary electron (SE) detection in the variable pressure or environmental scanning electron microscope (ESEM) describe a gas-amplified cascade from sample to detector when a conventional biased detector is used. Recent images obtained using a modified specimen current imaging approach however have suggested that at least a portion of the image is an induced field effect, in agreement with some of the early work. Our recent aim has been to investigate a range of detector designs and positions within the chamber in both the old ElectroScan E-3 model ESEM and the current generation FEI XL30 ESEM TMP. The results support the earlier observations of induced signal components being present, with even ‘negative’ or inverted images being obtained under some detector configurations due to the noise cancellation techniques used (figure 1). These results are being quantified using the DQE measurement approach to allow an objective comparison of different designs and positions. This data will be presented for the commercially available detectors and for the ‘Griffin’ grid detector under a range of operating conditions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.255
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2000
Admission routes1
Has abstractyes

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