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Record W4408327282 · doi:10.1029/2024jc021000

Evaluation of the Budget of Local Sea Level Trends Along the Coast of Canada and Northern USA During 1958–2015

2025· article· en· W4408327282 on OpenAlexafffundabout
Li Zhai, Youyu Lu, B. J. W. Greenan

Bibliographic record

VenueJournal of Geophysical Research Oceans · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsBedford Institute of OceanographyFisheries and Oceans Canada
FundersFisheries and Oceans Canada
KeywordsOceanographyGeographyPhysical geographyEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.066
GPT teacher head0.316
Teacher spread0.250 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes3
Has abstractyes

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