MétaCan
Menu
Back to cohort
Record W2335381546 · doi:10.14358/pers.77.6.601

A “New Hybrid” Modeling for Geometric Processing of Radarsat-2 data without User’s GCP

2011· article· en· W2335381546 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenuePhotogrammetric Engineering & Remote Sensing · 2011
Typearticle
Languageen
FieldEngineering
TopicSatellite Image Processing and Photogrammetry
Canadian institutionsnot available
FundersCanadian Space Agency
KeywordsData processingRemote sensingComputer scienceGeographyComputer graphics (images)GeodesyDatabase

Abstract

fetched live from OpenAlex

The objective of the research study is to use the synergy of 3D empirical (Rational Function Model (RFM) and physical (Toutin's) geometric models to develop a new model for the geometric processing of Radarsat-2 data without collecting ground control point (GCP). After independently evaluating the processing and the accuracy of each model using accurate differential GPS points, RFM was thus used to generate few virtual RFM points at the highest elevation of the study site. In addition to information of the metadata, these virtual points were input as GCPs in Toutin's model. This new hybrid model consistently achieved pixel accuracy, with better results for steep looking angles than the two other models. Its main advantage was, in addition, to be used without collecting GCP, increasing the applicability of Radarsat-2 data in remote areas.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.965
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.254
Teacher spread0.194 · 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