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Record W2794101634 · doi:10.1520/stp160220160136

Visualization of Urea Treatments Using Micro-X-Ray Fluorescence Spectrometry

2018· book-chapter· en· W2794101634 on OpenAlexaff
Lora Brehm, Ellen C. Keene, Sze‐Sze Ng

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsDow Chemical (Canada)
Fundersnot available
KeywordsX-ray fluorescenceVisualizationUreaMass spectrometryFluorescenceChemistryAnalytical Chemistry (journal)Materials scienceComputer scienceChromatographyPhysicsData miningOpticsBiochemistry

Abstract

fetched live from OpenAlex

Urea has many uses in agriculture. It is a common source of nitrogen fertilizer but is also used as an inert ingredient in solid formulations. Urea is listed on the U.S. EPA’s list of minimal concern. When used as an inert ingredient, urea can be used as a dissolution aid. Its worldwide availability, low cost, and safe-handling profile are some of the drivers of its use. Therefore, a wide variety of chemicals are coated on urea for various reasons. Having a method for detecting the deposition of various chemicals on urea is useful for understanding how uniformly a chemical has deposited on urea. Dyes and pigments have been used in fertilizer and seed treatments as an indication that a treatment has been applied. However, this method assumes that the treatment has the same mobility as the dye through the urea granule. Alternatively, chromatography techniques such as high-performance liquid chromatography and gas chromatography can indicate how much treatment has been applied, but these techniques do not provide information regarding the uniformity of treatment from granule to granule or the penetration of the treatment into the granule. An analytical technique called micro-X-ray fluorescence (MXRF) was evaluated for visualizing urea treatments. MXRF is a nondestructive form of elemental analysis capable of collecting elemental maps over a region of interest. Because urea is a simple organic molecule that contains only carbon, hydrogen, oxygen, and nitrogen (elements not typically detected by X-ray fluorescence spectrometry [XRF]), treatment that contains elements that are detectable by XRF can be differentiated from urea and visualized. This technique was used to analyze urea samples treated with zinc and N-(n-butyl)thiophosphoric triamide (NBPT). The distribution of zinc and NBPT (sulfur and phosphorus) across the granule and within the granule could be observed with MXRF. Whereas a red dye of a commercial NBPT treatment fully penetrated a urea granule, MXRF indicated that NBPT remained on its surface.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.273
Teacher spread0.256 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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