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Record W2193392035 · doi:10.1120/jacmp.v16i6.5869

P<sub>ion</sub> effects in flattening filter‐free radiation beams

2015· article· en· W2193392035 on OpenAlexaff
Robert Corns, Vicky Huang, Steven Thomas

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsIonization chamberElectrometerPercentage depth dose curveSIGNAL (programming language)RadiationCalibrationIonDosimetryOpticsPhysicsLinear particle acceleratorAtomic physicsNuclear medicineMaterials scienceComputational physicsIonizationBeam (structure)Medicine

Abstract

fetched live from OpenAlex

Flattening filter-free radiation beams have higher dose rates that significantly increase the ion recombination rate in an ion chamber's volume and lower the signal read by the chamber-electrometer pair. The ion collection efficiency correction (P(ion)) accounts for the loss of signal and subsequently changes dosimetric quantities when applied. We seek to characterize the changes to the percent depth dose, tissue maximum ratio, relative dose factor, absolute dose calibration, off-axis ratio, and the field width. We measured P(ion) with the two-voltage technique and represented P(ion) as a linear function of the signal strength. This linear fit allows us to correct measurement sets when we have only gathered the high voltage signal and to correct derived quantities. The changes to dosimetric quantities can be up to 1.5%. Charge recombination significantly affects percent depth dose, tissue maximum ratio, and off-axis ratio, but has minimal impact on the relative dose factor, absolute dose calibration, and field width.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.020
GPT teacher head0.332
Teacher spread0.312 · 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 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

Citations16
Published2015
Admission routes1
Has abstractyes

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