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Record W2599395912 · doi:10.1017/s1743921316011595

The Neutral Hydrogen Cosmological Mass Density at z = 5

2016· article· en· W2599395912 on OpenAlexaff
Neil H. M. Crighton, M. T. Murphy, J. X. Prochaska, G. Worseck, Marc Rafelski, George D. Becker, Sara L. Ellison, Michele Fumagalli, Sebastián López, Avery Meiksin, John M. O’Meara

Bibliographic record

VenueProceedings of the International Astronomical Union · 2016
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsUniversity of Victoria
FundersScience and Technology Facilities Council
KeywordsPhysicsAstrophysicsRedshiftStar formationQuasarGalaxySpectral lineIntergalactic mediumHomogeneousHydrogenIonizationCOSMIC cancer databaseRedshift surveyAstronomyIonQuantum mechanics

Abstract

fetched live from OpenAlex

Abstract We present the largest homogeneous survey of redshift > 4.4 damped Lyα systems (DLAs) using the spectra of 163 quasars that comprise the Giant Gemini GMOS (GGG) survey. With this survey we make the most precise high-redshift measurement of the cosmological mass density of neutral hydrogen, ΩHI. After correcting for systematic effects using a combination of mock and higher-resolution spectra, we find ΩHI= 0.98+0.20-0.18 × 10−3 at 〈z〉 = 4.9, assuming a 20% contribution from lower column density systems below the DLA threshold. By comparing to literature measurements at lower redshifts, we show that ΩHI can be described by the functional form ΩHI(z) ∝ (1 + z)0.4. This gradual decrease from z = 5 to 0 suggests that in the galaxies which dominate the cosmic star formation rate, Hi is a transitory gas phase fuelling star formation which must be continually replenished by more highly-ionized gas from the intergalactic medium, and from recycled galactic winds.

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)0.001
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.007
GPT teacher head0.192
Teacher spread0.185 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the International Astronomical Union→Same topicGalaxies: Formation, Evolution, Phenomena→French-language works237,207→