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Record W2781930916 · doi:10.3847/1538-4357/aaa3e6

Revisiting the Extended Schmidt Law: The Important Role of Existing Stars in Regulating Star Formation

2018· article· en· W2781930916 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2018
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsHerzberg Institute of Astrophysics
Fundersnot available
KeywordsStar formationSpiral galaxyStarsGalaxyDwarf galaxyStar clusterIrregular galaxyStellar massMolecular cloud

Abstract

fetched live from OpenAlex

Abstract We revisit the proposed extended Schmidt law, which posits that the star formation efficiency in galaxies depends on the stellar mass surface density, by investigating spatially resolved star formation rates (SFRs), gas masses, and stellar masses of star formation regions in a vast range of galactic environments, from the outer disks of dwarf galaxies, to spiral disks and to merging galaxies, as well as individual molecular clouds in M33. We find that these regions are distributed in a tight power law as ∝ , which is also valid for the integrated measurements of disk and merging galaxies at high- z . Interestingly, we show that star formation regions in the outer disks of dwarf galaxies with down to 10 −5 yr −1 kpc −2 , which are outliers of both the Kennicutt–Schmidt and Silk–Elmegreen laws, also follow the extended Schmidt law. Other outliers in the Kennicutt–Schmidt law, such as extremely metal-poor star formation regions, also show significantly reduced deviation from the extended Schmidt law. These results suggest an important role for existing stars in helping to regulate star formation through the effect of their gravity on the midplane pressure in a wide range of galactic environments.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.018
GPT teacher head0.269
Teacher spread0.251 · 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 designTheoretical or conceptual
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

Citations70
Published2018
Admission routes1
Has abstractyes

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