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Record W4390636410 · doi:10.1029/2023je007770

Deciphering Martian Flood Infiltration Processes at Hebrus Valles: Insights From Laboratory Experiments and Remote Sensing Observations

2024· article· en· W4390636410 on OpenAlexaff
F. Costard, J. A. P. Rodriguez, E. Godin, Antoine Séjourné, Jeffrey S. Kargel

Bibliographic record

VenueJournal of Geophysical Research Planets · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSinkholeGeologyMartianMars Exploration ProgramFluvialRegolithGeomorphologyEvaporiteOutflowEphemeral keyEarth scienceAstrobiologyPaleontologyKarstOceanography

Abstract

fetched live from OpenAlex

Abstract In evaluating the unique geomorphology of Hebrus Valles on Mars, an outflow channel southeast of Utopia Planitia, we investigated the role of fluvial dynamics in forming subsurface conduits. Unlike typical Martian channels that terminate under younger sediment rocks, Hebrus Valles retains its downstream features, thus offering a window into surface and subsurface interactions between liquid water and regolith. Employing physical lab simulations, we utilized buried polygonal tunneling configurations in a sandy matrix to replicate the incision features observed at the channel's terminus. This study provides the first lab‐based evidence demonstrating the role of conduits in conveying large floods in the Hebrus Valles. Our results lend support to the interpretation that Hebrus Valles conveyed high‐volume floods by demonstrating that conduits are necessary to form the features observed on the surface of Hebrus Valles. The experiments also highlight the role of sinkholes as floodwater capture points, which suggests the formation of a regional interconnected network of subsurface channels and caverns. These findings affirm that fluvial dynamics in Hebrus Valles could have expanded subsurface discontinuities into functioning conduit systems, potentially directing floodwaters through an interconnected subterranean network.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.347

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 communication0.0000.001
Open science0.0000.000
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 head0.065
GPT teacher head0.325
Teacher spread0.260 · 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 designBench or experimental
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

Citations4
Published2024
Admission routes1
Has abstractyes

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