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Record W4307630126 · doi:10.3847/2041-8213/ac9839

Most Planets Might Have More than 5 Myr of Time to Form

2022· article· en· W4307630126 on OpenAlexaff
Susanne Pfalzner, Shahrzad Dehghani, Arnaud Michel

Bibliographic record

VenueThe Astrophysical Journal Letters · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsQueen's University
Fundersnot available
KeywordsStarsPlanetPhysicsAstrophysicsmyrLimitingAstronomyProtoplanetary diskPlanetary systemAstrobiologyChemistry

Abstract

fetched live from OpenAlex

Abstract The lifetime of protoplanetary disks is a crucial parameter for planet formation research. Observations of disk fractions in star clusters imply median disk lifetimes of 1–3 Myr. This very short disk lifetime calls for planet formation to occur extremely rapidly. We show that young, distant clusters (≤5 Myr, >200 pc) often dominate these types of studies. Such clusters frequently suffer from limiting magnitudes leading to an over-representation of high-mass stars. As high-mass stars disperse their disks earlier, the derived disk lifetimes apply best to high-mass stars rather than low-mass stars. Including only nearby clusters (<200 pc) minimizes the effect of limiting magnitude. In this case, the median disk lifetime of low-mass stars is with 5–10 Myr, thus much longer than often claimed. The longer timescales provide planets ample time to form. How high-mass stars form planets so much faster than low-mass stars is the next grand challenge.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0130.003

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 head0.209
Teacher spread0.203 · 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 designSimulation or modeling
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

Citations55
Published2022
Admission routes1
Has abstractyes

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