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Record W7019655882

Hg isotope fractionation in sediment cores

2014· other· en· W7019655882 on OpenAlexaboutno aff

Bibliographic record

VenueQST-Repository (National Institutes for Quantum and Radiological Science and Technology) · 2014
Typeother
Languageen
FieldSocial Sciences
TopicHistorical Geography and Geographical Thought
Canadian institutionsnot available
Fundersnot available
KeywordsFractionationMercury (programming language)SedimentInductively coupled plasma mass spectrometryMass-independent fractionationIsotopeStable isotope ratio
DOInot available

Abstract

fetched live from OpenAlex

Analytical advances in the past decade and the recent advent of multicollector inductively coupled plasma mass spectrometry (MC-ICP/MS) have made it possible to measure fractionation of non-traditional elements such as mercury. We measured the isotope fractionation of mercury in different sediment cores to establish historical records. Marine sediment profiles from deep Pacific locations and from remote lakes on Vancouver Island (British Columbia) and the Experimental Lakes Area (Ontario) were investigated. While the Pacific and Ontario cores (going back to approx. 1800) compare well with each other in that older sediments become progressively enriched with lighter Hg isotopes, the much older BC records (dating back to ca.8000 BC) show the opposite trend starting at around 500 AD increasing in the bottom layers. Only a small degree of mass independent fractionation was detected in this study. While most samples contained sufficient mercury for the measurement, some were as low as 5 ng/g and required preconcentration prior to the isotope ratio measurement. Nitric acid/sulfuric acid sediment digests were evaporated to near dryness and re-dissolved in max. 20 % acid. delta-202Hg before and after pre-concentration were statistically not different from each other, demonstrating that this preconcentration process does not fractionate Hg isotopes.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.016
GPT teacher head0.287
Teacher spread0.272 · 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 designTheoretical or conceptual
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

Citations0
Published2014
Admission routes1
Has abstractyes

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