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Record W2949095571 · doi:10.1088/0067-0049/201/2/13

SUBMILLIMETER POLARIZATION OF GALACTIC CLOUDS: A COMPARISON OF 350 μm AND 850 μm DATA

2012· article· en· W2949095571 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2012
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsPolarization (electrochemistry)Brewster's angleAmplitudePolarization in astronomyPolarimetryDegree of polarization

Abstract

fetched live from OpenAlex

The Hertz and SCUBA polarimeters, working at 350 μm and 850 μm, respectively, have measured the polarized emission in scores of Galactic clouds. Of the clouds in each data set, 17 were mapped by both instruments with good polarization signal-to-noise ratios. We present maps of each of these 17 clouds comparing the dual-wavelength polarization amplitudes and position angles at the same spatial locations. In total number of clouds compared, this is a four-fold increase over previous work. Across the entire data set real position angle differences are seen between wavelengths. While the distribution of φ(850)-φ(350) is centered near zero (near-equal angles), 64% of data points with high polarization signal-to-noise (P ≥ 3σ p) have |φ(850)-φ(350)| > 10°. Of those data with small changes in position angle (≤10°) the median ratio of the polarization amplitudes is P(850)/P(350) = 1.7 ± 0.6. This value is consistent with previous work performed on smaller samples and models that require mixtures of different grain properties and polarization efficiencies. Along with the polarization data we have also compiled the intensity data at both wavelengths; we find a trend of decreasing polarization with increasing 850-to-350 μm intensity ratio. All the polarization and intensity data presented here (1699 points in total) are available in electronic format. © 2012. The American Astronomical Society. All rights reserved.

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 score0.237
Threshold uncertainty score0.424

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 communication0.0000.001
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 head0.031
GPT teacher head0.298
Teacher spread0.267 · 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

Citations43
Published2012
Admission routes1
Has abstractyes

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