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Record W2104528831 · doi:10.1017/s1743921313008466

The Debiased Kuiper Belt: Our Solar System as a Debris Disk

2013· article· en· W2104528831 on OpenAlexaffabout
Samantha Lawler

Bibliographic record

VenueProceedings of the International Astronomical Union · 2013
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDebris diskDebrisSolar SystemPhysicsEnvironmental scienceAstronomyMeteorologyPlanetary systemPlanet

Abstract

fetched live from OpenAlex

Abstract The dust measured in debris disks traces the position of planetesimal belts. In our Solar System, we are also able to measure the largest planetesimals directly and can extrapolate down to make an estimate of the dust. The zodiacal dust from the asteroid belt is better constrained than the only rudimentary measurements of Kuiper belt dust. Dust models will thus be based on the current orbital distribution of the larger bodies which provide the collisional source. The orbital distribution of many Kuiper belt objects is strongly affected by dynamical interactions with Neptune, and the structure cannot be understood without taking this into account. We present the debiased Kuiper belt as measured by the Canada-France Ecliptic Plane Survey (CFEPS). This model includes the absolute populations for objects with diameters >100 km, measured orbital distributions, and size distributions of the components of the Kuiper belt: the classical belt (hot, stirred, and kernel components), the scattering disk, the detached objects, and the resonant objects (1:1, 5:4, 4:3, 3:2 including Kozai subcomponent, 5:3, 7:4, 2:1, 7:3, 5:2, 3:1, and 5:1). Because a large fraction of known debris disks are consistent with dust at Kuiper belt distances from the host stars, the CFEPS Kuiper belt model provides an excellent starting point for a debris disk model, as the dynamical interactions with planets interior to the disk are well-understood and can be precisely modelled using orbital integrations.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.004
GPT teacher head0.188
Teacher spread0.184 · 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 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

Citations2
Published2013
Admission routes2
Has abstractyes

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