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Record W4381547179 · doi:10.24043/001c.81661

Island Studies Journal 10(2), Book Reviews

2015· article· en· W4381547179 on OpenAlexvenueno aff

Bibliographic record

VenueIsland Studies Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPacific and Southeast Asian Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGuanoArchipelagoSeabirdEcosystemOceanographyEcologyGeographyEarth scienceGeologyBiology

Abstract

fetched live from OpenAlex

This book is based on the author's PhD thesis, which examines the changes in the ecoenvironment of the Xisha archipelago (also known as the Paracel islands) in the central South China Sea over the last 2000 years.All the islands of the archipelago are coral islands in a tropical ocean.So far, the Xisha Islands have been well preserved and they remain in a relatively pristine condition due to their distance from mainland China and restrictions on traveling to the area imposed by the Chinese government.Changes in seawater temperature, animal migration and human activities, therefore, may bring ecological risks and may substantially impact the vulnerable ecosystem of the islands.The main objective of this study is to explain interactions between the climate system, marine environments, seabirds and coral islands.Furthermore, it aims to provide data to predict future ecological responses to climate change.DNA barcoding and elemental, isotopic, geochemical, and biochemical analyses of the ornithogenic sediments from the Xisha Islands are used to track changes in the island ecology, along with multi-proxy analyses and regional comparisons.Soils that are ornithogenic -literally, originating from birds -consist of a welldefined layer of bird excrement, or guano, resting on mineral layers.In order to find out about environments on the Xisha archipelago, Xu's research selected various biological materials such as sediments and seabird remains (bones, guano).Seabirds inhabit these islets in large numbers and have played a central role in the development of the ecosystem.Seabird occupation leads to the accumulation of high levels of guano in the soil, which supports the development of flora: a great number of trees and shrubs flourish on guano-rich islands.The thriving vegetation, in turn, provides an ideal habitat for the seabirds.This case study shows that, besides nutrients (phosphorus and nitrogen), seabirds may also deliver significant quantities of contaminants and metal pollutants to a pristine and fragile island eco-environment.Regarding mercury, for instance, this happens, as Xu suggests that the dry and wet deposition of mercury from the atmosphere above the South China Sea "could be absorbed by the extremely productive oceanic phytoplankton" (p.106).Mercury is then further transferred to fish and then to birds as their predators.Collected soil samples consisted of a sediment mixture including plant humus, seabird guano and coral sand with remains of bird and fish bones.In order to trace elements in the soil and guano, elemental and isotopic analyses were carried out.However, the main method used in this study is a multi-proxy analysis which includes determining the age of ornithogenic sediments in the coral sand.Chemical analyses and radiometric dating techniques used include lead ( 210 Pb) and radiocarbon ( 14 C) chronology analyses.The long half-life of radiocarbon (more than 5000 years) means it is well suited to determining the age of ancient carbonbearing materials (such as bird and fish bones from hundreds to 50,000 years old).Several other radionuclides -or unstable, radioactive atom nuclei -were detected in ornithogenic coral sand sediment cores collected from five different islands of the Xisha archipelago, including radium ( 226 Ra) and caesium ( 137 Cs).Seabirds may have a significant impact on radionuclide concentrations in their immediate surroundings.However, peaks of 137 Cs were discovered in 1963 and 1986.The former were identified as a record of the 1963 fallout

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)0.006
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.100
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.224
GPT teacher head0.435
Teacher spread0.211 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2015
Admission routes1
Has abstractyes

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