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ANALYSIS OF RADIATION LOADS IN AN ISS CREW-QUARTER PROTECTED WITH A COMPOSITE MATERIAL

2024· article· en· W4404122815 on OpenAlexaboutno aff
D. A. Kartashov, В. И. Павленко, Н. И. Черкашина, Olga А. Ivanova, Raisa Tolochek, Vyacheslav Shurshakov

Bibliographic record

VenueAerospace and Environmental Medicine · 2024
Typearticle
Languageen
FieldEngineering
TopicSpace Exploration and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsCrewQuarter (Canadian coin)AeronauticsComposite numberEnvironmental scienceForensic engineeringNuclear engineeringEngineeringMaterials scienceComposite materialHistoryArchaeology

Abstract

fetched live from OpenAlex

The paper reports the results of calculating doses in the location of a cylindrical container of a protective composite (teflon of 1 cm in thickness and 4.05 g/cm3 density) in the left crew quarter of the ISS Service module (space experiment "Protective composite"). Calculations were compared with the data of passive PILLE-MKS detectors installed inside the container and on the nearby wall. Calculation was performed using ray tracing for the detectors' locations with consideration for the input of galactic cosmic rays and particles in Earth's radiation belts in the conditions modeling the space station compartment. The calculation was made for the ISS orbit in the period between February 21 and September 19, 2022 on the background of increasing solar activity. According to the PILLE-ISS measurements, the protective effect of the composite in terms of absorbed dose made up 1.39 ± 0.03, largely due to a reduced input of Earth's radiation belts which agrees with the calculations.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.004
GPT teacher head0.193
Teacher spread0.189 · 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 designObservational
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

Citations0
Published2024
Admission routes1
Has abstractyes

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