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Record W2789788744 · doi:10.5539/apr.v10n1p19

The Distribution of the Heavy Metals in Soil Sample Along Blue Nile During overflow Season

2017· article· en· W2789788744 on OpenAlexvenueno aff
Magdi Hassan Saad

Bibliographic record

VenueApplied Physics Research · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsInductively coupled plasmaHeavy metalsDeposition (geology)Environmental chemistryInductively coupled plasma mass spectrometryMetalEnvironmental scienceAnalytical Chemistry (journal)Mass spectrometryMaterials scienceChemistryGeologyPlasmaMetallurgyPhysicsNuclear physicsGeomorphology

Abstract

fetched live from OpenAlex

The research cared out to calculate the heavy metals deposition for the soil of the Blue Nile along the edges of two sides, starting from the Sudan Ethiopia border end to the Khartoum state in Tuti Island. The samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS) technique. The results show that there is a significant difference for most of the concentration of the elements deposition on two sides of the Blue Nile outside of Nile channel for that area which covered by water after the overflow. Heavy metals Cr, Fe, Co, Cu, and Zn concentration deposition are increased in the direction of the water to Khartoum, while Ni and Pb concentrations are decreased in the same direction. K, Ca, Ti, Mn, Br, Rb, Sr, and Zr heavy metal concentrations are varied but in general remains equals.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.287
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.002
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 head0.321
Teacher spread0.279 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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