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Record W4320163794 · doi:10.1017/s1743921322001727

Modeling protoplanetary disk evolution in young star forming regions

2020· article· en· W4320163794 on OpenAlexaff
Maite J. C. Wilhelm, Simon Portegies Zwart, Claude Cournoyer-Cloutier, Sean C. Lewis, Brooke Polak, Aaron Tran, Mordecai‐Mark Mac Low, Stephen L. W. McMillan

Bibliographic record

VenueProceedings of the International Astronomical Union · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstrophysics and Star Formation Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPhotoevaporationPhysicsStarsAstrophysicsCircumstellar diskPlanetExtinction (optical mineralogy)Protoplanetary diskCircumstellar dustPlanetesimalPlanetary migrationMolecular cloudStar formationDebris diskCluster (spacecraft)Star clusterAstronomyPlanetary system

Abstract

fetched live from OpenAlex

Abstract Stars form in clusters, while planets form in gaseous disks around young stars. Cluster dissolution occurs on longer time scales than disk dispersal. Planet formation thus typically takes place while the host star is still inside the cluster. We explore how the presence of other stars affects the evolution of circumstellar disks. Our numerical approach requires multi-scale and multi-physics simulations where the relevant components and their interactions are resolved. The simulations start with the collapse of a turbulent cloud, from which stars with disks form, which are able to influence each other. We focus on the effect of extinction due to residual cloud gas on the early evolution of circumstellar disks. We find that this extinction protects circumstellar disks against external photoevaporation, but these disks then become vulnerable to dynamic truncation by passing stars. We conclude that circumstellar disk evolution is heavily affected by the early evolution of the cluster.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueProceedings of the International Astronomical UnionSame topicAstrophysics and Star Formation StudiesFrench-language works237,207