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High-Resolution Atmospheric Lead Pollution during the Roman Period Recorded in Belgian Ombrotrophic Peat Cores

2018· preprint· en· W2803902472 on OpenAlexaff
Mohammed Allan, Daniele L. Pinti, Bassam Ghaleb, Sophie Verheyden, Nadine Mattielli, Nathalie Fagel

Bibliographic record

VenuePreprints.org · 2018
Typepreprint
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsUniversité du Québec à Montréal
FundersFonds De La Recherche Scientifique - FNRS
KeywordsOmbrotrophicPeatPeriod (music)BogPollutionEnvironmental scienceEnvironmental chemistryDeposition (geology)GeologyChemistryArchaeologyGeomorphologyGeographyStructural basin

Abstract

fetched live from OpenAlex

Two peat cores from two bogs were used to reconstruct high–resolution changes in atmospheric Pb accumulation rate (Pb AR) in Belgium during the Roman period. The two records were compared to assess the reliability of peat cores as archives of atmospheric Pb deposition and to established histories of atmospheric emissions from anthropogenic sources. To address these issues we analyze Pb concentration and isotopes, using ICP-MS, LA-ICP-MS and MC-ICP-MS in two peat sections, spanning 1000 yr each. Lead concentrations in the two cores range from 0.1 to 60 μg g−1, with the maxima between 15 and 60 μg g−1. The average natural background of Pb AR was 0.005± 0.002 mg m-2 yr-1 and the maximum ranges from 0.7 to 1.2 mg m-2 yr-1 between 50 BC and AD 215. The highest Pb AR exceed the pre-Roman period values by a factor of 25-30. Pb isotopic composition indicates that mining and metallurgical activities were the predominant sources of pollution during the Roman period. The Pb AR and chronologies in the Belgian peat cores are consistent with those reported for other continental archives as lake sediments, peat and ice cores.

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 score0.090
Threshold uncertainty score0.180

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 studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.042
GPT teacher head0.277
Teacher spread0.235 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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Same venuePreprints.orgSame topicHeavy metals in environmentFrench-language works237,207