The new NAD83v80VG velocity model using an updated velocity field for Canada
Bibliographic record
Abstract
An improved national scale crustal velocity model has been developed for Canada that is part of an ongoing effort to update the realization of the Canadian Spatial Reference System (CSRS) to NAD83(CSRS) v8 and to support the implementation of the new North American Terrestrial Reference Frame of 2022 (NATRF2022). The new model is based on a reprocessing of all GNSS data to the end of 2023 and includes data from active GNSS stations throughout Canada, repeated high accuracy Canadian campaign data, as well as continuous GNSS data from Alaska, the northern contiguous United States, and Greenland. Like the previous version, the updated velocity model is a ‘hybrid’ combination of geophysical model predictions constrained to GNSS crustal velocity estimates, a feature that is particularly useful in regions with large deformation signals and/or sparser GNSS data coverage (i.e., much of northern Canada). The geophysical model inputs are predictions from glacial isostatic adjustment (GIA) and elastic deformation models that estimate the vertical velocity signal from present-day melting of ice sheets and glaciers. The GIA and present-day mass signals are combined with the GNSS velocities to provide crustal velocity and uncertainty estimates for the north, east and vertical components. For most of the study area, the predicted uncertainties are <1 mm/yr. The resulting hybrid velocity model is a critical part of the CSRS that enables users to propagate their coordinates to different reference epochs and can also be used to support studies of sea level change and natural hazards. • An updated velocity field for Canada is derived from GNSS measurements • The measured velocities and geophysical models generate a crustal velocity model • The updated national-scale velocity model is termed NAD83V80VG • The velocity model includes vertical and horizontal motion as well as uncertainties
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".