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Record W2038136993 · doi:10.1029/2005je002629

Venusian channel formation as a subsurface process

2006· article· en· W2038136993 on OpenAlexfundno aff
Nicholas P. Lang, Vicki L. Hansen

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2006
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersNational Institutes of HealthMcGill University
KeywordsProcess (computing)Channel (broadcasting)GeologyRemote sensingEnvironmental scienceComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

We constructed detailed geologic maps of channels within three widely separated regions using Magellan synthetic aperture radar (SAR) imagery and altimetry radar data in order to understand the operative processes involved in Venusian channel formation. Channels in each region cut topographic highs including ridges and shield edifices with no deflection of the channel course by surface topography. We argue that these relationships are difficult to reconcile with the widely held view that Venusian channels represent surface processes, whether constructional or erosional. We conclude that the channels evolved through subsurface fluid flow, which involved local stoping and transport of overlying material; that is, these channels were carved from below, rather than from above. We postulate that at least some Venusian channels form because of subsurface fluid flow along a shallow crustal interface that forms the boundary between overlying low backscatter surface materials and underlying basal materials. Fluid movement along the interface may facilitate piecemeal stoping and erosion of the local surface materials from below, eventually resulting in the formation of channel traces exposed at the surface. The low backscatter material which hosts the channels in this study also hosts abundant coalescing shields and associated deposits, which may represent shield terrain.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.536

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 head0.028
GPT teacher head0.318
Teacher spread0.290 · 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 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

Citations29
Published2006
Admission routes1
Has abstractyes

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