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Record W4240355820 · doi:10.32920/ryerson.14645250

Towards Absolute Dose Measurement in MRI-LINAC and GAMMA KNIFE®: Design and Construction of an MR-Compatible Water Calorimeter

2021· preprint· en· W4240355820 on OpenAlexaff
Niloufar Entezari

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsCalorimeter (particle physics)Linear particle acceleratorNuclear engineeringMaterials sciencePhysicsOpticsEngineeringBeam (structure)

Abstract

fetched live from OpenAlex

The purpose of this work was to design and build a portable 4⁰C stagnant Water Calorimeter (WC) for dual use in MRI-linac and Gamma Knife® in addition to conventional radiotherapy linacs. WC determines radiation energy absorbed in a sensitive volume absolutely and directly through measuring radiation-induced temperature rise (related to the medium’s specific heat capacity). To assist with the design process, several parameters involved in calorimeter tank design including tank dimensions, a variety of insulation material and thicknesses, unique cooling design structures to sustain WC at 4⁰C, as well as the calorimeter vessel design were simulated, and the results on heat gain/loss at the point of measurement was evaluated. Based on the optimizations, a calorimeter tank was built, and one single set of initial measurements in a conventional clinical linear accelerator was performed.

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)0.002
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.053
GPT teacher head0.286
Teacher spread0.232 · 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
GenreMethods

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

Citations0
Published2021
Admission routes1
Has abstractyes

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