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Record W4302763257 · doi:10.1093/mnras/stac2855

Chemical and physical processes caused by electrons impacting on H2O–CO mixed ices

2022· article· en· W4302763257 on OpenAlexfundno aff
C.-H. Huang, C. Cecchi‐Pestellini, A. Ciaravella, A. Jiménez-Escobar, L-C Hsiao, Ni-En Sie, Yu‐Jung Chen

Bibliographic record

VenueMonthly Notices of the Royal Astronomical Society · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsnot available
FundersInstitut sur la Nutrition et les Aliments FonctionnelsNational Science and Technology CouncilAgenzia Spaziale Italiana
KeywordsDesorptionCarbon monoxideElectronPhysicsRadiolysisAtomic physicsPenetration depthAstrochemistryChemical physicsIrradiationInterstellar mediumChemistryAstrophysicsNuclear physicsPhysical chemistryGalaxy

Abstract

fetched live from OpenAlex

ABSTRACT Electron-induced chemistry is relevant to many processes that occur when an ionizing source interacts with matter, as in the formation of complex molecules within frozen condensates in space. We explore in this paper the radiolysis and the desorption processes affecting iced mixtures of water and carbon monoxide subjected to electron irradiation in the sub-keV regime. The experiments have been performed with the Interstellar Energetic Process System (IEPS), an ultra-high vacuum chamber equipped with an electron gun. The irradiated ices have been monitored with infrared and mass spectroscopies. We derive the chemistry and determine cross-sections for relevant processes as functions of the energy of the impacting electrons. We quantify the electron-stimulated desorption of some significant species in terms of their desorption yields, and relate these quantities to the electron penetration depth and the desorption-relevant length. The results of this study have been compared with the outcomes of similar experiments performed using pure carbon monoxide ices.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.006
GPT teacher head0.219
Teacher spread0.213 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

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