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Record W2774114631 · doi:10.1144/sp467.8

Thermal inertia variations from gully and mass-wasting activity in Gasa crater, Mars

2017· article· en· W2774114631 on OpenAlexaff
T. N. Harrison, L. L. Tornabene, G. R. Osinski, Susan J. Conway

Bibliographic record

VenueGeological Society London Special Publications · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsWestern University
Fundersnot available
KeywordsMars Exploration ProgramImpact craterMass wastingThermal inertiaGeologyAstrobiologyInertiaGeomorphologyThermalPhysicsMeteorologyClassical mechanicsSediment

Abstract

fetched live from OpenAlex

Abstract Gasa crater has been the most active site observed on Mars to date, making it of particular interest for studying the process(es) behind gully formation and activity. In this study, we investigate whether differences in thermal inertia across different segments of gully systems, combined with morphological and colour observations with High-Resolution Imaging Science Experiment (HiRISE), can provide some constraints on the physical characteristics associated with recent activity within gullies in Gasa. We also investigate thermophysical differences between slopes in Gasa dominated by gully activity compared to those predominantly modified by dry mass-wasting processes. We find that Gasa crater exhibits clear variations in thermal inertia across its walls, controlled by the material properties and the types of dominant mass movement processes occurring on each wall. The youthful gully-fan lobes display thermal inertia values c. 20–40 J m −2 K −1 s −1/2 higher than adjacent older eroded and dust-covered lobes. The talus aprons from mass wasting in Gasa have thermal inertia values c. 60–80 J m −2 K −1 s −1/2 higher than gully aprons. The results of this study thus suggest that thermal imaging can inform us on surface change detection on Mars.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.028
GPT teacher head0.247
Teacher spread0.219 · 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 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

Citations4
Published2017
Admission routes1
Has abstractyes

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