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Long-term evolution of the neutron rate at the Canfranc Underground Laboratory

2022· article· en· W4295815925 on OpenAlexaff
S. E. A. Orrigo, J. L. Taı́n, N. Mont-Geli, A. Tarifeño-Saldivia, L. M. Fraile, Marcel Grieger, J. Agramunt, A. Algora, D. Bemmerer, F. Calviño, G. Cortés, Alfredo de Blas, I. Dillmann, Araceli Domínguez-Bugarín, R. F. Naranjo Garcia, E. Nácher, A. Tolosa-Delgado

Bibliographic record

VenueThe European Physical Journal C · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsTRIUMF
FundersConsejo Superior de Investigaciones CientíficasMinisterio de Ciencia e InnovaciónAgencia Estatal de InvestigaciónGeneralitat ValencianaMinisterio de Ciencia, Innovación y Universidades
KeywordsTerm (time)Environmental scienceNuclear engineeringNuclear physicsPhysicsEngineeringAstronomy

Abstract

fetched live from OpenAlex

Abstract We report results on the long-term variation of the neutron counting rate at the Canfranc Underground Laboratory, of importance for several low-background experiments installed there, including rare-event searches. The measurement campaign was performed employing the High Efficiency Neutron Spectrometry Array (HENSA) mounted in Hall A and lasted 412 live days. The present study is the first long-term measurement of the neutron rate with sensitivity over a wide range of neutron energies (from thermal up to 0.1 GeV and beyond) performed in any underground laboratory so far. Data on the environmental variables inside the experimental hall (radon concentration, air temperature, air pressure and humidity) were also acquired during all the measurement campaign. We have investigated for the first time the evolution of the neutron rate for different energies of the neutrons and its correlation with the ambient variables.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.008
GPT teacher head0.214
Teacher spread0.207 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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Same venueThe European Physical Journal CSame topicDark Matter and Cosmic PhenomenaFrench-language works237,207