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Record W6891692788 · doi:10.48380/67my-zb09

Late veneer on the terrestrial planets: dynamics perspective

2023· article· en· W6891692788 on OpenAlexaff

Bibliographic record

Venuedggv-e-publications · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAstro and Planetary Science
Canadian institutionsCanadian Nautical Research Society
Fundersnot available
KeywordsPlanetesimalTerrestrial planetMantle (geology)PopulationPlanetVeneerMars Exploration Program

Abstract

fetched live from OpenAlex

The origin of the late veneer of the terrestrial planets and of the lunar bombardment has been the subject of numerous studies in the field of cosmochemistry and in the field of planet formation and dynamical evolution of the early solar system. In the last years, we have studied [1,..,5] the dynamical and collisional evolution of the population of planetesimals originally in the terrestrial planet region and still “alive” at the time of the Moon-forming event. We have shown that this population of leftover planetesimals can explain the late veneer of Earth, Mars and Vesta as constrained by the amount of highly siderophile elements (HSE) in their mantles as well as the number of late impact basins on the Moon. The low concentration of HSE in the lunar mantle can be explained by a late sequestration of lunar mantle HSEs into the core at the time of the lunar mantle overturn. The origin of the late veneer carrier from the terrestrial planet region is consistent with the isotopic constraints on the source of the late veneer, indicating a non-carbonaceous source. This suggests that the carbonaceous projectiles that delivered part of the terrestrial volatile elements had already decayed by the time the late veneer started. [5]Nesvorný, D., et al. 2023, Icarus, 399, 115545. [4]Nesvorný, D., et al. 2022, ApJL, 941, L9. [3]Zhu, M.-H. et al. 2021, Nature Astronomy, 5, 1286. [2]Zhu, M.-H. et al. 2019, Nature Astronomy, 571, 226. [1]Morbidelli, A., et al. 2018, Icarus, 305, 262.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.022
GPT teacher head0.255
Teacher spread0.233 · 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.

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

Citations0
Published2023
Admission routes1
Has abstractyes

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