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Record W2303013424 · doi:10.14288/1.0085986

Thermal susceptibility study of the Canadian Hydrogen Intensity Mapping Experiment Pathfinder Instruments

2015· article· en· W2303013424 on OpenAlexaboutno aff
Stephanie A. N. Gerbrandt

Bibliographic record

VenuecIRcle (University of British Columbia) · 2015
Typearticle
Languageen
FieldEngineering
TopicSpacecraft and Cryogenic Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsPathfinderIntensity (physics)Environmental sciencePhysicsComputer scienceOpticsLibrary science

Abstract

fetched live from OpenAlex

The Canadian Hydrogen Intensity Mapping Experiment (CHIME) will study Baryon Acoustic Oscillations (BAO) in the redshift range when the expansion of the Universe began to accelerate due Dark Energy’s dominating influence. CHIME will measure the Hubble parameter, H(z), and constrain the equation of state parameter, w, of Dark Energy. These measurements are critical in furthering our understanding of the expansion history of the Universe and Dark Energy. CHIME will observe the faint cosmological signal and map expansion from its location at the Dominion Radio Astrophysical Observatory (DRAO) near Penticton, BC. In order to make the required measurements, the CHIME telescope requires an accurate calibration plan. Of the many components of the overall calibration plan, this paper specifically addresses the system gain calibration, with respect to exploring the relationship between system gain and temperature in the development of a thermal model. The model presented here describes the relationship to first order and lays the foundation for more detailed study. Findings provide insight into system gain configuration and facilitate subsequent development of the thermal model. The results presented here are critical steps in the system gain calibration, contributing to a successful overall calibration plan that will ultimately lead to reliable data from which new science results will emerge. The analysis of these data has the potential to lead to an increased understanding of the expansion of the Universe and the nature of Dark Energy.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.223
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.017
GPT teacher head0.169
Teacher spread0.152 · 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 teacher head, 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
Published2015
Admission routes1
Has abstractyes

Explore more

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