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Record W2117418374 · doi:10.1130/2015.2518(12)

Mapping terrestrial impact craters with the<b><i>TanDEM-X</i></b>digital elevation model

2015· book-chapter· en· W2117418374 on OpenAlexaboutno aff
Manfred Gottwald, Thomas Fritz, Helko Breit, Birgit Schättler, Alan W. Harris

Bibliographic record

VenueGeological Society of America Special Papers · 2015
Typebook-chapter
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsnot available
FundersAgenzia Spaziale ItalianaEuropean Space Agency
KeywordsDigital elevation modelImpact craterTandemRemote sensingInterferometryElevation (ballistics)GeologyShuttle Radar Topography MissionRadarGeographyGeodesyComputer scienceAstrobiologyPhysicsAstronomyAerospace engineeringTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

The TanDEM-X digital elevation model (DEM), generated by the TanDEM-X mission, provides a unique opportunity for studying the topography and morphology of terrestrial impact structures. In the TanDEM-X mission, the two Earth-orbiting X-band radar satellites TerraSAR-X and TanDEM-X are operated in close formation to act as a single-pass interferometer. Interferometric processing of the acquired data yields a global DEM with unprecedented accuracy of better than 4 m and a spatial resolution of 12 m. We investigate how the TanDEM-X DEM can be used for mapping terrestrial impact structures by applying it to the confirmed impact craters in the Earth Impact Database of the Planetary and Space Science Centre at the University of New Brunswick, Canada. The majority of these structures show distinct topographic signatures. Particularly prominent are medium-sized eroded craters. We provide representative elevation and X-band amplitude maps for a sample of the Earth Impact Database entries.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0280.015

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 head0.029
GPT teacher head0.225
Teacher spread0.196 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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