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Record W4406087194 · doi:10.1038/s41597-025-04378-1

Reconstruction of long-term hourly sea level data for Prince Edward Island, Canada

2025· article· en· W4406087194 on OpenAlexafffundabout
Tianze Pang, Xiuquan Wang, Muhammad Qasim Mahmood, Sana Basheer

Bibliographic record

VenueScientific Data · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of Prince Edward Island
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaNatural Resources CanadaCanada Foundation for InnovationAtlantic Canada Opportunities AgencyGovernment of Canada
KeywordsShoreContext (archaeology)Climate changeStorm surgeTerm (time)Sea levelStormGeographyOceanographyHistorical recordHazardPhysical geographyEnvironmental scienceClimatologyMeteorologyGeologyArchaeologyEcologyHistory

Abstract

fetched live from OpenAlex

Regional sea level rise has been regarded a key factor in understanding of climate change impact to coastal communities. As a vulnerable island to sea level rise and storm surges, the province of Prince Edward Island (PEI) in Canada lacks sufficient long-term island-wide historic record of sea level data. This has become a major challenge for further studies on coastal environments and climate change adaptation. To overcome this limitation, here we reconstruct a long-term hourly sea level dataset using the existing long-term records of limited permanent tide stations and short-term records of widely-distributed temporary stations. With comprehensive statistical analysis and modeling, the historical sea level records furthest between 1911 and 2023 are reconstructed with an hourly time step. This new dataset significantly extends the availability of long-term sea level data along with the shoreline of PEI, which can be used for further studies on coastal change assessment and coastal hazard adaptation in the context of climate change.

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)0.001
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: none
Teacher disagreement score0.678
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.081
GPT teacher head0.270
Teacher spread0.188 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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