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Record W2109027846 · doi:10.11648/j.sjac.20140203.11

Spectroscopic Determination of Some Trace Elements as Pollutants in Fruit Dates Palm and Agricultural Soils at Zilfi Province

2014· article· en· W2109027846 on OpenAlexaboutno aff
Nawal M. Suleman

Bibliographic record

VenueScience Journal of Analytical Chemistry · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersKing Saud University
KeywordsSoil waterPollutantEnvironmental scienceSoil testEnvironmental chemistryPalmSoil qualityTrace elementAgricultureArsenicChemistryGeographySoil scienceArchaeology

Abstract

fetched live from OpenAlex

Some trace elements (Ag, Al, As, Bi, Cd, Co and Cr,) which could be found as pollutants in fruit dates palm and agricultural soils at Zilfi province was measured using ICP-MS. The area of study, the date palm farms found at Zilfi province, was surfed during September 2013 taking 11 samples of soil from these farms randomly. Also 11 samples of date palm fruit (fully ripe, stage of Tamer) were collected from the same farms. The pH and electric conductivity was measured to the soil samples. (ICP-MS) spectrophotometers were used to determine the concentrations of the elements in both soil and dates (washed and unwashed). The concentration of Ag in all samples was higher the Saskatchewan quality standards for soils .All soil samples were found to be contaminated with As, because all these concentrations of arsenic are more than Saskatchewan quality standards for soils (11(mg/kg)), and SQGE soil quality guideline for environmental health (17(mg/kg). All elements concentrations date fruits samples are less compared to literature elements concentrations. As general the concentrations of these elements are less in washed date samples compared to unwashed ones.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.255
Teacher spread0.248 · 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

Citations8
Published2014
Admission routes1
Has abstractyes

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