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Record W1997452875 · doi:10.1016/j.yqres.2009.11.009

Reconstruction of recent sea-level change using testate amoebae

2010· article· en· W1997452875 on OpenAlexaboutno aff
Dan J. Charman, W. Roland Gehrels, Clare Manning, Charu Sharma

Bibliographic record

VenueQuaternary Research · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
Fundersnot available
KeywordsTestate amoebaeForaminiferaGeologyOceanographyTide gaugeSea levelSalt marshPaleontologyPhysical geographyGeographyArchaeologyPeat

Abstract

fetched live from OpenAlex

Proxy-based sea-level reconstructions place the instrumentally observed rates of recent sea-level rise in a longer term context by providing data that extend the instrumental sea-level record into past centuries. This paper presents the first sea-level reconstructions based on analyses of testate amoebae, to test their ability to produce high-precision reconstructions of past sea level. We present two reconstructions for the past 100"yr from sites in Maine (USA) and Nova Scotia (Canada) based on short cores from salt marshes, and modern training data from North America and the United Kingdom. These are compared with tide-gauge records and reconstructions based on foraminifera from the same cores. The reconstructions show good agreement with both the tide-gauge data and the foraminifera-based reconstructions. The UK data perform well in predicting known elevations of North American surface samples and produce sea-level reconstructions very similar to those based on the North American data, suggesting the methodology is robust across large geographical areas. We conclude that testate amoebae have the potential to provide robust, higher precision sea-level reconstructions for the past few centuries if modern transfer functions are improved and core sites are located within the main zone of testate amoebae occurrence on the salt marsh.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.333
GPT teacher head0.390
Teacher spread0.057 · 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.

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

Citations42
Published2010
Admission routes1
Has abstractyes

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