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Record W2901577191 · doi:10.3847/1538-4357/aaee67

Erratum: “The Sloan Digital Sky Survey Reverberation Mapping Project: Hα and Hβ Reverberation Measurements from First-year Spectroscopy and Photometry” (2017, ApJ, 851, 21)

2018· erratum· en· W2901577191 on OpenAlexaff
C. J. Grier, Jonathan R. Trump, Yue Shen, K. Horne, Karen Kinemuchi, Ian D. McGreer, D. Starkey, W. N. Brandt, Patrick B. Hall, C. S. Kochanek, Yuguang Chen, K. D. Denney, Jenny E. Greene, Luis C. Ho, Y. Homayouni, Jennifer I-Hsiu Li, Liuyi Pei, B. M. Peterson, P. Petitjean, D. P. Schneider, Mouyuan Sun, Yusura AlSayyad, Dmitry Bizyaev, J. Brinkmann, Joel R. Brownstein, Kevin Bundy, Kyle Dawson, Sarah Eftekharzadeh, José G. Fernández-Trincado, Yang Gao, Timothy A. Hutchinson, Siyao Jia, Linhua Jiang, Daniel Oravetz, Kaike Pan, Isabelle Pâris, K. A. Ponder, Christina Peters, Jesse Rogerson, Audrey Simmons, R. Smith, Ran Wang

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typeerratum
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsCanadian Institute for Theoretical AstrophysicsUniversity of TorontoYork University
Fundersnot available
KeywordsPhysicsSkyPhotometry (optics)ReverberationAstronomyReverberation mappingAstrophysicsSpectroscopyGalaxyStarsAcousticsActive galactic nucleus

Abstract

fetched live from OpenAlex

We found a bug in the formula used to calculate the uncertainties in the virial products.This error produced incorrect uncertainties for the virial products and consequently the black hole mass (M BH ) measurements reported in Tables 4 and 5.These uncertainties were used to produce Figures 12, 13, and 14.This error is minor and does not affect any of our results or their interpretation, and thus no edits to the text are necessary.We here provide updated/corrected tables and figures produced with the correct M BH uncertainties.

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)0.002
metaresearch head score (Gemma)0.024
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: Other · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0050.002
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0550.048

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 head0.032
GPT teacher head0.252
Teacher spread0.220 · 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
GenreOther

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

Citations3
Published2018
Admission routes1
Has abstractyes

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