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Record W621674625

Characterization of Weathering Effects in Holocene Loess and Paleosol, Kluane Lake, Yukon, Canada.

2014· article· en· W621674625 on OpenAlexaboutno aff
Emma Lagerbäck Adolphi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsWeatheringLoessPaleosolGeologyParent materialGeochemistryMineralogySoil waterStrontiumGeomorphologySoil scienceChemistry
DOInot available

Abstract

fetched live from OpenAlex

When measuring weathering intensity of paleosols there arevarious methods and ratios that can be used. This studyexamines several weathering indicators too see which are bestapplied and most effective on loess and paleosols from Yukon,Canada. This region today is considered sub-arctic, but duringpast time this area and its soils has been characterized byglaciations and interglaciations. These changes are the origin ofthe unweathered Kluane loess and the weathered Slims Soilthat are analyzed in the study. Methods used to determine themost sensitive weathering indicator was; CIA (the ChemicalIndex of Alteration), CPA (the Chemical Proxy of Alteration),oxide ratios ((CaO + Na2O + MgO)/TiO2 and (CaO + Na2O +K2O)/TiO2), and elemental ratios (Rb/Sr, Ba/Sr, Ti/Sr). Theresults from the CIA and oxide ratios show that calcium is thevarying factor, and thereby a good weathering indicator forthese samples. The elemental ratios showed a great variationbetween weathered and unweathered samples, and a cleardecrease strontium is apparent. These ratios would also beuseful indicators, but since Sr is associated with Ca, it isprobably calcium that is the main feature. The methods withoutcalcium as a factor did not give any clear separation betweenKluane loess and Slims Soil. Thereby it is concluded that proxyscontaining Ca are the most useful indicators of weathering inthis area.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.702
Threshold uncertainty score0.846

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.003
GPT teacher head0.147
Teacher spread0.144 · 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.

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

Citations1
Published2014
Admission routes1
Has abstractyes

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