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Record W2766985361

Marsh Recession and Erosion study of the Fraser Delta, B.C., Canada from Historic Satellite Imagery

2017· article· en· W2766985361 on OpenAlexaboutno aff
Richard Marijnissen, Stefan Aarninkhof

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeological formations and processes
Canadian institutionsnot available
Fundersnot available
KeywordsMarshDeltaVegetation (pathology)Satellite imageryPhysical geographyGeologyGeographyErosionSedimentWetlandSatelliteOceanographyEcologyGeomorphology
DOInot available

Abstract

fetched live from OpenAlex

<p class="MsoNormal">The goal of the study is to map the changes of marsh extent and topography on both Sturgeon Bank and Westham Island between 1980 and now. The study will look for a correlation between the recession and the possible loss of sediment from the banks. If a sediment deficit is a (major) contributor of marsh recession within the Fraser Delta, the results of the study should reveal such a connection.</p> <p class="MsoNormal">Although there are plenty of studies suggesting changes have taken place in the marshes fronting the Fraser Delta, no study has utilized the extensive data record of satellites to study these changes for the entire Fraser Delta. Tools like the Aquamonitor can detect the changes in coastlines in the past 30 years from satellite imagery. More advanced tools are still in development like MI-SAFE, which detects inter-tidal elevations and vegetation on foreshores to estimate the potential risk reduction of flooding by coastal vegetation all across the world. Within the study the latest techniques from these tools are applied and adapted to the Fraser Delta. By using the full 30+ years of information on satellite imagery, the marsh and inter-tidal surface changes are examined from a new angle.</p>

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)0.000
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 score0.164
Threshold uncertainty score0.738

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.018
GPT teacher head0.214
Teacher spread0.196 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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