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Record W3156804144 · doi:10.1029/2020gl092113

Fossil Reefs Reveal Temporally Distinct Late Holocene Lagoonal Reef Shutdown Episodes at Kiritimati Island, Central Pacific

2021· article· en· W3156804144 on OpenAlexaff
Emma Ryan, Kyle M. Morgan, Paul S. Kench, Susan Owen, Carlos P. Carvajal, T. Turner

Bibliographic record

VenueGeophysical Research Letters · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicCoral and Marine Ecosystems Studies
Canadian institutionsSimon Fraser University
FundersMarsden FundUniversity of AucklandRoyal Society Te ApārangiRoyal Society
KeywordsReefAtollHoloceneAcroporaGeologyCoral reefOceanographyEnvironmental issues with coral reefsFringing reefPoritesCoralResilience of coral reefsPaleontology

Abstract

fetched live from OpenAlex

Abstract An extremely rare example of well‐preserved emergent Holocene fossil reefs exists at Kiritimati Island, central Pacific. Fossil reefs are rich geological archives of paleoenvironmental change. The first paleoecological surveys of two fossil reefs are presented, revealing high coral cover (40–50%) and low diversity (6 genera). Fossil coral ages suggest reefs exhibited disparity in the timing of reef development (4,113 and 1,915 cal yBP) and ecological surveys show different coral compositions (Acropora or Porites dominant), between reefs. Results constrain two discrete episodes of reef shutdown (at 2,905 and 1,705 cal yBP) as lagoonal reefs thrived, and subsequently died off, through the late Holocene. Shifts in physio‐chemical conditions associated with reduced lagoon flushing following storm‐driven changes in atoll rim morphology are argued as the driver for the staged reef die‐off. The findings have implications for interpreting past and future eco‐morphological change on atolls, given projected increases in storminess with 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 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)0.000
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 score0.120
Threshold uncertainty score0.238

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.026
GPT teacher head0.273
Teacher spread0.247 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

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