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Record W2924017158 · doi:10.1017/s1473550418000526

Melt-water formed dark streaks on slopes of Haughton crater as possible Mars analogues

2019· article· en· W2924017158 on OpenAlexaboutno aff
Jonathan Clarke, J. P. Knightly, Shannon Rupert

Bibliographic record

VenueInternational Journal of Astrobiology · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
Fundersnot available
KeywordsImpact craterMars Exploration ProgramGeologyAstrobiologySnowMartianArcticLiquid waterHesperianMartian surfaceNoachianExploration of MarsGeomorphologyEarth scienceOceanography

Abstract

fetched live from OpenAlex

Abstract Haughton crater in the Canadian Arctic has been extensively used as a Mars (and lunar) analogue over the past 20 years. Here we report on small scale, dark, semi-seasonal slope streaks formed by melt water flowing down the crater walls that we observed during the Mars Society-sponsored M160 expedition to the F-MARS facility on the NW rim of the crater. The streaks are formed by biofilms colonizing snow melt flowing from semi-permanent snow patches in Haughton crater on Devon Island and elsewhere in the Canadian Arctic. These features superficially resemble the dark slope streaks and recurring slope lineae (RSL) observed on Mars and may serve as analogues for wet models for their formation and a contrast with dry formation models. Their significance to astrobiology and planetary science is three-fold: (1) as examples of dark recurring streaks known to be associated with water they provide a benchmark to compare with Martian slope streaks and RSL. (2) The melt streaks may have potential as astrobiological analogues for wet models of slope streaks and RSL. (3) They are natural laboratories to study planetary protection issues associated with robotic and astronaut exploration of potential water-related slope features 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 score0.076
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.007
GPT teacher head0.243
Teacher spread0.235 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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