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Record W2786774684 · doi:10.1088/1361-6560/aacfb2

Output and ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mstyle> <mml:msubsup> <mml:mi>k</mml:mi> <mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>Q</mml:mi> </mml:mrow> <mml:mrow> <mml:mrow> <mml:mi>c</mml:mi> <mml:mi>l</mml:mi> <mml:mi>i</mml:mi> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>,</mml:mo> </mml:mrow> </mml:msub> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>Q</mml:mi> </mml:mrow> <mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> <mml:mi>s</mml:mi> <mml:mi>r</mml:mi> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:mrow> <mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mspace/> <mml:mi>f</mml:mi> </mml:mrow> <mml:mrow> <mml:mrow> <mml:mi>c</mml:mi> <mml:mi>l</mml:mi> <mml:mi>i</mml:mi> <mml:mi>n</mml:mi> </mml:mrow> <mml:mo>,</mml:mo> </mml:mrow> </mml:msub> </mml:mrow> <mml:mrow> <mml:msub> <mml:mrow> <mml:mi>f</mml:mi> </mml:mrow> <mml:mrow> <mml:mrow> <mml:mi>m</mml:mi> <mml:mi>s</mml:mi> <mml:mi>r</mml:mi> </mml:mrow> </mml:mrow> </mml:msub> </mml:mrow> </mml:mrow> </mml:msubsup> </mml:mstyle> </mml:math> ) correction factors measured and calculated in very small circular fields for microDiamond and EFD-3G detectors

2018· article· en· W2786774684 on OpenAlexaff

Bibliographic record

VenuePhysics in Medicine and Biology · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicParticle Detector Development and Performance
Canadian institutionsUniversity of VictoriaBC Cancer Agency
Fundersnot available
KeywordsDetectorPerpendicularCollimated lightMonte Carlo methodTruebeamBeam (structure)

Abstract

fetched live from OpenAlex

The purpose of this work was to obtain [Formula: see text] factors for microDiamond and EFD-3G detectors in very small (less than 5 mm) circular fields. We also investigated the impact of possible variations in microDiamond detector design schematics on the calculated [Formula: see text] factors. Output factors (OF's) of 6 MV beams from TrueBeam linac collimated with 1.27-40 mm diameter cones were measured with EBT3 films, microDiamond and EFD-3G detectors as well as calculated (in water) using Monte Carlo (MC) methods. Based on EBT3 measurements and MC calculations [Formula: see text] factors were derived for these detectors. MC calculations were performed for microDiamond detector in parallel and perpendicular orientations relative to the beam axis. Furthermore, [Formula: see text] factors were calculated for two microDiamond detector models, differing by the presence or absence of metallic pins. The measured OFs agreed within 2.4% for fields ⩾10 mm. For the cones of 1.27, 2.46, and 3.77 mm maximum differences were 17.9%, 1.8% and 9.0%, respectively. MC calculated output factors in water agreed with those obtained using EBT3 film within 2.2% for all fields. MC calculated [Formula: see text] factors for microDiamond detector in fields ⩾10 mm ranged within 0.975-1.020 for perpendicular and parallel orientations. MicroDiamond detector [Formula: see text] factors calculated for the 1.27, 2.46 and 3.77 mm fields were 1.974, 1.139 and 0.982 with detector in parallel orientation, and these factors were 1.150, 0.925 and 0.914 in perpendicular orientation. Including metallic pins in the microDiamond model had little effect on calculated [Formula: see text] factors. EBT3 and MC obtained [Formula: see text] factors agreed within 3.7% for fields of ⩾3.77 mm and within 5.9% for smaller cones. Including metallic pins in the detector model had no effect on calculated [Formula: see text] factors. Our results show that microDiamond and EFD-3G detectors can be used in very small (1.27-3.77 mm) fields once [Formula: see text] corrections determined in this work are applied. Expected uncertainty of such measurements will be in the range of 8%-2.5%.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.628
Threshold uncertainty score0.530

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0080.008
Open science0.0040.007
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.6280.593

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 head0.034
GPT teacher head0.257
Teacher spread0.224 · 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.

Study designSimulation or modeling
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

Citations7
Published2018
Admission routes1
Has abstractyes

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