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Record W2078944562 · doi:10.1118/1.3505452

Characterization of a multi‐axis ion chamber array

2010· article· en· W2078944562 on OpenAlexfundno aff
T Simon, Jakub Kozelka, William E. Simon, Darren Kahler, Jonathan Li, Chihray Liu

Bibliographic record

VenueMedical Physics · 2010
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsnot available
FundersAtomic Energy of Canada LimitedSun Nuclear Corporation
KeywordsDosimetryLinear particle acceleratorMaterials scienceIonization chamberImaging phantomBackscatter (email)CalibrationBeam (structure)OpticsDose profileFlat panel detectorDetectorEnvironmental scienceNuclear medicinePhysicsIonComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this work was to characterize a multi-axis ion chamber array (IC PROFILER; Sun Nuclear Corporation, Melbourne, FL, USA) that has the potential to simplify the acquisition of LINAC beam data. METHODS: The IC PROFILER (or panel) measurement response was characterized with respect to radiation beam properties, including dose, dose per pulse, pulse rate frequency (PRF), and energy. Panel properties were also studied, including detector-calibration stability, power-on time, backscatter dependence, and the panel's agreement with water tank measurements [profiles, fractional depth dose (FDD), and output factors]. RESULTS: The panel's relative deviation was typically within (+/-) 1% of an independent (or nominal) response for all properties that were tested. Notable results were (a) a detectable relative field shape change of approximately 1% with linear accelerator PRF changes; (b) a large range in backscatter thickness had a minimal effect on the measured dose distribution (typically less than 1%); (c) the error spread in profile comparison between the panel and scanning water tank (Blue Phantom, CC13; IBA Schwarzenbruck, DE) was approximately (+/-) 0.75%. CONCLUSIONS: The ability of the panel to accurately reproduce water tank profiles, FDDs, and output factors is an indication of its abilities as a dosimetry system. The benefits of using the panel versus a scanning water tank are less setup time and less error susceptibility. The same measurements (including device setup and breakdown) for both systems took 180 min with the water tank versus 30 min with the panel. The time-savings increase as the measurement load is increased.

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)0.000
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 score0.086
Threshold uncertainty score0.865

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.276
Teacher spread0.264 · 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

Citations30
Published2010
Admission routes1
Has abstractyes

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