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Record W1982429108 · doi:10.1118/1.598992

Saturation current and collection efficiency for ionization chambers in pulsed beams

2000· article· en· W1982429108 on OpenAlexaff
F DeBlois, Corey Zankowski, Ervin B. Podgoršak

Bibliographic record

VenueMedical Physics · 2000
Typearticle
Languageen
FieldMedicine
TopicRadiation Therapy and Dosimetry
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsSaturation (graph theory)Saturation currentIonization chamberIonizationRadiationAtomic physicsMaterials scienceVoltageCurrent (fluid)OpticsPhysicsIon

Abstract

fetched live from OpenAlex

Saturation currents and collection efficiencies in ionization chambers exposed to pulsed megavoltage photon and electron beams are determined assuming a linear relationship between 1/I and 1/V in the extreme near-saturation region, with I and V the chamber current and polarizing voltage, respectively. Careful measurements of chamber current against polarizing voltage in the extreme near-saturation region reveal a current rising faster than that predicted by the linear relationship. This excess current combined with conventional "two-voltage" technique for determination of collection efficiency may result in an up to 0.7% overestimate of the saturation current for standard radiation field sizes of 10X10 cm2. The measured excess current is attributed to charge multiplication in the chamber air volume and to radiation-induced conductivity in the stem of the chamber (stem effect). These effects may be accounted for by an exponential term used in conjunction with Boag's equation for collection efficiency in pulsed beams. The semiempirical model follows the experimental data well and accounts for both the charge recombination as well as for the charge multiplication effects and the chamber stem effect.

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)0.003
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.285
Teacher spread0.274 · 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

Citations47
Published2000
Admission routes1
Has abstractyes

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