Evaluation of the Budget of Local Sea Level Trends Along the Coast of Canada and Northern USA During 1958–2015
Bibliographic record
Abstract
Abstract Estimates of vertical land motion (VLM) from an updated Canadian crustal velocity model (NAD83v70VG) are used to evaluate the budget of contributions to trends of relative sea level (RSL) at tide gauge sites along the east and west coasts of Canada and northern USA during 1958–2015. The RSL trends result from a combination of the processes of sterodynamics (SD), glacial isostatic adjustment (GIA), and changes in earth Gravity, earth Rotation, and viscoelastic solid earth Deformation (GRD). For these contributions, we used data from a recent study on global RSL trends (Wang et al., 2021, https://doi.org/10.1029/2021gl094502 ). Incorporating the NAD83v70VG VLM in the derivation of the trends results in an improvement of the RSL budget. Estimates of SD contributions to the observed tide gauge water levels are obtained using this updated VLM, and are compared with the SD‐component from the global ocean models used in Wang et al. (2021, https://doi.org/10.5281/zenodo.5554494 ). The comparison demonstrates the need to include interannual variations of river runoff for modeling the sea level changes in the St. Lawrence River and its estuary, and to improve the model's spatial resolution in the Gulf of St. Lawrence and on the open shelf of the Northwest Atlantic. Along the coast of the Northeast Pacific, the SD sea level changes are well simulated by the global models despite their coarse spatial resolutions and the use of river runoff climatology.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".