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Record W4307815246 · doi:10.1016/j.jnucmat.2022.154101

Reply to S. Wang's comments (Journal of Nuclear Materials 570 (2022) 153952) on short communication of G.A. McRae and C.E. Coleman published in Journal of Nuclear Materials 568 (2022) 153889

2022· article· en· W4307815246 on OpenAlexaff
G.A. McRae, C.E. Coleman

Bibliographic record

VenueJournal of Nuclear Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsCarleton University
Fundersnot available
KeywordsIrradiationTungstenFluenceAnalytical Chemistry (journal)IsotopeHeliumAtmospheric temperature rangeDesorptionRadiochemistrySpectral lineSaturation (graph theory)ChemistryThermal desorptionMaterials scienceNuclear physicsPhysical chemistryThermodynamicsPhysicsAdsorption

Abstract

fetched live from OpenAlex

Helium (He) isotope exchange in tungsten (W) during sequential irradiation by 3 keV 4He and 3He ions at room (RT) and elevated temperatures (700-1200 K) was investigated. The total He fluence was in the range of 5 × 1021–1.8 × 1022 He/m2 to provide a saturation of the surface layer. The amount of He retained in W after irradiation was measured using in-situ (up to 1500 K) and ex-situ (up to 2500 K) thermal desorption spectroscopy (TDS). Air exposure influenced TDS spectra, but clear were no noticeable differences between 3He and 4He TDS spectra after single irradiation. Subsequent irradiation with different He isotopes demonstrated a very efficient isotope exchange already at room temperature. The substitution of He atoms goes even faster with increasing irradiation temperature. Mechanisms of observed processes are discussed on the base of a simple model.

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)0.004
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.026
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0260.025
Insufficient payload (model declined to judge)0.0110.013

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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2022
Admission routes1
Has abstractyes

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