MétaCan
Menu
Back to cohort
Record W2521614017 · doi:10.1120/jacmp.v17i5.6308

Technical Note: Out‐of‐field dose measurement at near surface with plastic scintillator detector

2016· article· en· W2521614017 on OpenAlexafffund
Alexandra Bourgouin, Nicolas Varfalvy, Louis Archambault

Bibliographic record

VenueJournal of Applied Clinical Medical Physics · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Radiotherapy Techniques
Canadian institutionsCentre hospitalier universitaire de QuébecUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDosimeterIonization chamberDosimetryScintillatorDose profileMaterials scienceOpticsDetectorPercentage depth dose curveBeam (structure)RadiationNuclear medicinePhysicsIonIonization

Abstract

fetched live from OpenAlex

Out-of-field dose depends on multiple factors, making peripheral dosimetry com-plex. Only a few dosimeters have the required features for measuring peripheral dose. Plastic scintillator dosimeters (PSDs) offer numerous dosimetric advantages as required for out-of-field dosimetry. The purpose of this study is to determine the potential of using PSD as a surface peripheral dosimeter. Measurements were performed with a parallel-plate ion chamber, a small volume ion chamber, and with a PSD. Lateral-dose measurements (LDM) at 0.5 cm depth and depth-dose curve (PDD) were made and compared to the dose calculation provided by a treatment planning system (TPS). This study shows that a PSD can measure a dose as low as 0.51 ± 0.17 cGy for photon beam and 0.58 ± 0.20 cGy for electron beam with a difference of 0.2 and 0.1 cGy compared to a parallel-plate ion chamber. This study demonstrates the potential of using PSD as an out-of-field dosimeter since measure-ments with PSD avoid averaging over a too-large depth, at 1 mm diameter, and can make precise measurement at very low dose. Also, electronic equilibrium is easier to reach with PSD due to its small sensitive volume and its water equivalence.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.815
Threshold uncertainty score0.614

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.025
GPT teacher head0.329
Teacher spread0.303 · 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

Citations5
Published2016
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Applied Clinical Medical PhysicsSame topicAdvanced Radiotherapy TechniquesFrench-language works237,207