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Record W4233977668 · doi:10.4095/321095

An analytical protocol for determining the elemental chemistry of Quaternary sediments using a portable X-ray fluorescence spectrometer

2020· report· en· W4233977668 on OpenAlexaff
R D Knight, B A Kjarsgaard, D A J Stepner, H A J Russell

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMaterials Science
TopicClay minerals and soil interactions
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsQuaternarySpectrometerX-ray fluorescenceProtocol (science)FluorescenceChemistryAnalytical Chemistry (journal)Environmental chemistryPhysicsGeologyOpticsPaleontology

Abstract

fetched live from OpenAlex

Advances in portable X-ray fluorescence (pXRF) technology have resulted in the ability to collect high-quality geochemical data for sediments at a fraction of the cost of traditional laboratory methods. The analytical quality of pXRF derived geochemical data is dependent on numerous factors including sample heterogeneity, grain size, moisture content, sample thickness, and instrument specifications such as power parameters, X-ray tube type, and dwell time. In order to ensure precise and accurate results using a pXRF spectrometer an analytical protocol has been developed using reference materials and prepared Quaternary sediments. This protocol considers 1) Sample preparation, 2) Analysis, and 3) Data compilation/presentation. Although the pXRF spectrometer provides concentrations for 41 elements it has been determined that only a subset of these elements meet the criteria for near definitive, quantitative, and qualitative data. Although the analytical protocol is robust, sample collection and preparation is still the key to a successful geochemical study. A significant advantage of pXRF technology is the opportunity to refine sampling strategies in near real time and the ability to add additional samples to a project with little budget increase.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.121
GPT teacher head0.421
Teacher spread0.300 · 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 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

Citations0
Published2020
Admission routes1
Has abstractyes

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