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Record W4256290989 · doi:10.1002/essoar.10503497.2

N/A

2020· preprint· en· W4256290989 on OpenAlexaff
A. S. McEwen, E. I. Schaefer, C. M. Dundas, Sarah Sutton, L. K. Tamppari, M. Chojnacki

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsWestern University
Fundersnot available
KeywordsWorld Wide WebEmail authenticationElectronic mailComputer science

Abstract

fetched live from OpenAlex

Following the planet-encircling dust event (PEDE) of Mars Year (MY) 34, MRO/HiRISE has seen many more candidate RSL than in typical Mars years. They were imaged at more than 285 unique locations from August 2018 to August 2019, 157 where RSL had not been seen previously. Of the locations where RSL had been observed in the same season of prior Mars years, 34 sites had more extensive RSL coverage than MY29-33; none had less extensive RSL. 150 active RSL sites were identified in the southern middle latitudes (SMLs) versus the 36/year average during MY28-33. RSL are present on ~87% of the HiRISE images covering steep, rocky slopes in the SML in southern summer of MY34, rather than ~40% as in prior years. Post-PEDE RSL are also present over a wider combined range of latitude, slope aspect, and season than in prior years. These RSL sites usually show evidence for recent dust deposition. There are clear dust devil tracks in 54% of post-PEDE images with RSL, and in 73% of such images in the SMLs and L=236°-360° (late southern spring to the end of summer), where and when dust devils are most active. The tracks indicate dust lifting, by several mechanisms. We suggest that dust lifting processes on steep slopes may initiate and sustain RSL formed from flows of dust (perhaps clumped) and/or sand that is destabilized by dust movement. The otherwise puzzling recurrence and year-to-year variability of RSL activity can be at least partly explained by variable yearly dust fallout.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.710
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0020.006
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.012

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 head0.655
GPT teacher head0.529
Teacher spread0.125 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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