MétaCan
Menu
Back to cohort
Record W1827448788 · doi:10.1029/2002rs002643

RADARSAT 1 synthetic aperture radar observations of Antarctica: Modified Antarctic Mapping Mission, 2000

2003· article· en· W1827448788 on OpenAlexaboutno aff
K. Jezek, K. Farness, R. Carande, Xiaoqing Wu, N. Labelle‐Hamer

Bibliographic record

VenueRadio Science · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsRemote sensingGeologySynthetic aperture radarGlacierInterferometric synthetic aperture radarGeodesyRadarMeteorologyGeographyComputer scienceGeomorphology

Abstract

fetched live from OpenAlex

The RADARSAT 1 Antarctic Mapping Project (RAMP) is a collaboration between NASA and the Canadian Space Agency to map Antarctica using synthetic aperture radar (SAR). RAMP comprises two distinct mapping missions. The first Antarctic Mapping Mission was successfully completed in October 1997. Data from the acquisition phase of the 1997 campaign have been used to achieve the primary goal of producing the first high‐resolution SAR image map of the entire Antarctic continent. The Modified Antarctic Mapping Mission (MAMM) occurred during the fall of 2000. The acquisition strategy concentrated on collecting highest‐resolution RADARSAT 1 data of Antarctica's fast glaciers for change detection, feature tracking estimates of surface velocity, and interferometric analysis of velocity and coherence over the entire viewable region, which extends north of 80.1°S latitude. This paper reviews the MAMM project and describes the techniques to be used in processing the data. An example of data acquired over the Drygalski ice tongue, Antarctica illustrates how MAMM data will further benefit investigations of the icy continent.

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: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.039
GPT teacher head0.227
Teacher spread0.188 · 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

Citations46
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueRadio ScienceSame topicCryospheric studies and observationsFrench-language works237,207