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Record W2133465914 · doi:10.1038/srep04222

Sulfur and iron accumulation in three marine-archaeological shipwrecks in the Baltic Sea: The Ghost, the Crown and the Sword

2014· article· en· W2133465914 on OpenAlexafffund
Yvonne Fors, Håkan Grudd, A. Rindby, Farideh Jalilehvand, Magnus Sandström, Ingemar Cato, Lennart Bornmalm

Bibliographic record

VenueScientific Reports · 2014
Typearticle
Languageen
FieldArts and Humanities
TopicCultural Heritage Materials Analysis
Canadian institutionsUniversity of Calgary
FundersSLAC National Accelerator LaboratoryBasic Energy SciencesBiological and Environmental ResearchNatural Sciences and Engineering Research Council of CanadaOffice of ScienceNational Institutes of HealthNational Center for Research ResourcesNational Institute of General Medical SciencesRiksantikvarieämbetetU.S. Department of Energy
KeywordsSulfurEnvironmental chemistryBaltic seaSulfur cycleSulfateCrown (dentistry)SulfideXANESChemistryEnvironmental scienceArchaeologyOceanographyGeologySpectroscopyMaterials scienceGeographyOrganic chemistry

Abstract

fetched live from OpenAlex

Sulfur and iron concentrations in wood from three 17(th) century shipwrecks in the Baltic Sea, the Ghost wreck, the Crown and the Sword, were obtained by X-ray fluorescence (XRF) scanning. In near anaerobic environments symbiotic microorganisms degrade waterlogged wood, reduce sulfate and promote accumulation of low-valent sulfur compounds, as previously found for the famous wrecks of the Vasa and Mary Rose. Sulfur K-edge X-ray absorption near-edge structure (XANES) analyses of Ghost wreck wood show that organic thiols and disulfides dominate, together with elemental sulfur probably generated by sulfur-oxidizing Beggiatoa bacteria. Iron sulfides were not detected, consistent with the relatively low iron concentration in the wood. In a museum climate with high atmospheric humidity oxidation processes, especially of iron sulfides formed in the presence of corroding iron, may induce post-conservation wood degradation. Subject to more general confirmation by further analyses no severe conservation concerns are expected for the Ghost wreck wood.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.047
GPT teacher head0.253
Teacher spread0.207 · 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 designBench or experimental
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

Citations48
Published2014
Admission routes2
Has abstractyes

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