A functional model for quantifying deformation in reference frame transformations
Bibliographic record
Abstract
<!--!introduction!--> IAG Commission 1 Working Group 1.3.1 in association with the Open Geospatial Consortium (OGC) have developed a deformation model functional model (DMFM) and an associated Geodetic Grid Exchange Format (GGXF) for quantifying and disseminating deformation information for use in time-dependent reference frame transformations. The purpose of the DMFM is to provide a framework through which producers and users of deformation models can describe crustal displacement and velocity data using robust grid formats such as GGXF. Using the DMFM and GGXF combined, positional displacements can be readily applied in point motion coordinate operations. This approach is essential in deforming zones where conformal time-dependent transformation approaches do not adequately handle crustal deformation. The presentation describes how the DMFM can be applied in typical use cases. These include: transforming GNSS PPP positions (e.g. in an IGS20 frame) to a national geodetic datum in a deforming zone and transformations between reference frames across earthquake events resulting in significant coseismic and postseismic crustal displacement. The DMFM and associated GGXF provide a framework for developers of geodetic software such as those used in GIS, GNSS processing and positioning to better handle complex deformation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".