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Record W2085254266 · doi:10.1088/0031-9155/54/19/n01

<i>SpekCalc</i>: a program to calculate photon spectra from tungsten anode x-ray tubes

2009· article· en· W2085254266 on OpenAlexaff
Gavin Poludniowski, Guillaume Landry, F DeBlois, Philip Evans, Frank Verhaegen

Bibliographic record

VenuePhysics in Medicine and Biology · 2009
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsMcGill UniversityJewish General HospitalMontreal General Hospital
FundersCancer Research UK
KeywordsMonte Carlo methodAnodeContext (archaeology)PhotonSpectral linePhysicsX-rayX-ray tubeTungstenRange (aeronautics)Computational physicsComputer scienceMedical physicsOpticsMaterials scienceMathematicsElectrodeStatistics

Abstract

fetched live from OpenAlex

A software program, SpekCalc, is presented for the calculation of x-ray spectra from tungsten anode x-ray tubes. SpekCalc was designed primarily for use in a medical physics context, for both research and education purposes, but may also be of interest to those working with x-ray tubes in industry. Noteworthy is the particularly wide range of tube potentials (40-300 kVp) and anode angles (recommended: 6-30 degrees) that can be modelled: the program is therefore potentially of use to those working in superficial/orthovoltage radiotherapy, as well as diagnostic radiology. The utility is free to download and is based on a deterministic model of x-ray spectrum generation (Poludniowski 2007 Med. Phys. 34 2175). Filtration can be applied for seven materials (air, water, Be, Al, Cu, Sn and W). In this note SpekCalc is described and illustrative examples are shown. Predictions are compared to those of a state-of-the-art Monte Carlo code (BEAMnrc) and, where possible, to an alternative, widely-used, spectrum calculation program (IPEM78).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Software · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.240

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0720.019

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 head0.070
GPT teacher head0.349
Teacher spread0.279 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreSoftware

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

Citations704
Published2009
Admission routes1
Has abstractyes

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