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Record W1989368763 · doi:10.1086/309129

Interstellar Clump Behavior and Magnetic Effects in Small Clumps

2000· article· en· W1989368763 on OpenAlexaff
J. P. Vallée

Bibliographic record

VenueThe Astrophysical Journal · 2000
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsNational Research Council CanadaHerzberg Institute of Astrophysics
Fundersnot available
KeywordsPhysicsAstrophysicsInterstellar mediumMagnetic fieldLine (geometry)Molecular cloudResolution (logic)AstronomyStarsGalaxy

Abstract

fetched live from OpenAlex

Cold, dusty molecular clumps (0.01 pc < diameter < 0.50 pc) in the interstellar medium are beginning to reveal their secrets. Using the latest observational findings at sufficiently high angular resolution, an analysis is made of the physical dependencies and energy values inside cold molecular clumps. There are universal physical relations in clumps governing mean parameters such as gas density n , diameter D , magnetic field , and gas line width σ, with the forms ⟨ n ⟩ ~ D c , ⟨ ⟩ ~ ⟨ D ⟩ p , ⟨ ⟩ ~ ⟨ n ⟩ k , ⟨σ⟩ ~ ⟨ D ⟩ q . For clumps with diameters < 0.5 pc, one finds c = -1.5 ± 0.1, p = -1.5 ± 0.1, k = 1.0 ± 0.2. These exponent values differ from those found by Larson for molecular clouds with sizes greater than 1 pc. These differences in c and k could be indicative of ongoing accretion processes in shocked media as a prelude to star formation. The energy distribution in clumps reveals the following: the support against gravitational collapse in clumps with sizes greater than 0.1 pc comes mainly from turbulent energy, while smaller clumps with sizes less than 0.1 pc are supported by both magnetic and turbulent energies. The clump size of 0.1 pc is critical in many other respects.

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 score0.001
Threshold uncertainty score0.003

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.214
Teacher spread0.208 · 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

Citations10
Published2000
Admission routes1
Has abstractyes

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