Modeling Surface Mass Load Displacements Along The Cascadia Subduction Zone
Bibliographic record
Abstract
The Earth’s surface is under constant strain from different mass loads. Surface mass loads, such as the oceans, atmosphere, and continental water reservoirs, exert forces on the elastic solid Earth, inducing crustal deformation. These loads move over Earth's surface on time scales varying from less than a day to many thousand years. Since the Earth is elastic and not perfectly rigid, the pressure from these loads deforms the shape of Earth’s surface. Horizontal and vertical displacement responses due to a load can be recorded using Global Positioning System (GPS) receivers. Modeling and removing surface-mass loading signals, which are present in all GPS time series, can reduce the variance in the time series. Surface deformation is of particular interest along subduction zones. A subduction zone is an area of tectonic plate collision where the more dense plate subducts, or moves underneath, the less dense plate. The Cascadia Subduction Zone extends from Vancouver Island down to Northern California. This research project focuses on using the python-based software program LOADDEF to accurately compute displacement responses of the Earth to oceanic, atmospheric, and hydrologic loads. These modeled responses are then compared to the observed displacement responses measured by the Plate Boundary Observatory along the Cascadia Subduction Zone.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".