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Record W2657265192 · doi:10.1038/ncomms14792

Vulnerability of Louisiana’s coastal wetlands to present-day rates of relative sea-level rise

2017· article· en· W2657265192 on OpenAlexfundno aff
K. L. Jankowski, Torbjörn E. Törnqvist, Anjali M. Fernandes

Bibliographic record

VenueNature Communications · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal wetland ecosystem dynamics
Canadian institutionsnot available
FundersUniversity of OttawaWater Institute of the GulfU.S. Department of EnergyU.S. Geological SurveyLouisiana State UniversityTulane UniversityNational Science Foundation
KeywordsWetlandEnvironmental scienceAccretion (finance)Sea level riseSea levelCoastal plainSubsidenceHydrology (agriculture)OceanographyMississippi deltaWater tableElevation (ballistics)Submersion (mathematics)Physical geographyGeologyGeographyClimate changeEcologyGeomorphologyGroundwaterWater resource management

Abstract

fetched live from OpenAlex

of wetlands over the past century and concern exists whether remaining wetlands will persist while facing some of the world's highest rates of relative sea-level rise (RSLR). Here we analyse an unprecedented data set derived from 274 rod surface-elevation table-marker horizon stations, to determine present-day surface-elevation change, vertical accretion and shallow subsidence rates. Comparison of vertical accretion rates with RSLR rates at the land surface (present-day RSLR rates are 12±8 mm per year) shows that 65% of wetlands in the Mississippi Delta (SE Louisiana) may keep pace with RSLR, whereas 58% of the sites in the Chenier Plain (SW Louisiana) do not, rendering much of this area highly vulnerable to RLSR. At least 60% of the total subsidence rate occurs within the uppermost 5-10 m, which may account for the higher vulnerability of coastal Louisiana wetlands compared to their counterparts elsewhere.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.100
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.324
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations320
Published2017
Admission routes1
Has abstractyes

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Same venueNature CommunicationsSame topicCoastal wetland ecosystem dynamicsFrench-language works237,207