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Record W3197983442 · doi:10.21608/fjard.2020.189882

ASSESSMENT AND SPATIAL DISTRIBUTION OF NIKLE WITHIN SOILS OF IBSHWAY DISTRICT AREA, FAYOUM GOVERNORATE, EGYPT

2020· article· en· W3197983442 on OpenAlexaboutno aff
Mahmoud Abd-Elgawad, Abdelnaser A. Abdel Hafeez, Hamdy A. Abdurrahman, Mohammed Hamed El-Sayed Saleh

Bibliographic record

VenueFayoum Journal of Agricultural Research and Development /Fayoum Journal of Agricultural Research and Development · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicSoil and Land Suitability Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterDistribution (mathematics)Spatial distributionGeographyEnvironmental scienceSoil scienceRemote sensingMathematics

Abstract

fetched live from OpenAlex

Spatial distribution of Ni has been studied in soils of Ibshway district area, Fayoum governorate, Egypt using grid system- log distance of 2 km. Levels and Spatial distribution of Ni( total and extractable) contents were identified and mapped using "ILWIS application" Geographic Information System (on basis of their Ni contents) throughout the studied area. It was found that the mean concentrations of total Ni within the top 60 cm of soils were 40.02 mg kg-1 , i.e higher than the general means in some soils of the world. The general mean concentrations of total Ni within the top 60 cm in Ibshway District soils mostly higher than the maximum allowable limits applied in some countries such as Denmark , Netherlands , Germany , Ireland and Canada the total Ni values are similar to the permissible limits applied in some developed countries such as Finland and below the allowed maximum limits applied in some developed countries such as Switzerland, Czech Republic and Eastern Europe (Russia, Ukraine, Moldavia and Belarus) The maps generated through GIS are useful for decision makers for land use planning, conservation and evaluating the degree of environmental contamination with hazardous heavy metals.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.248
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.002
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 head0.060
GPT teacher head0.309
Teacher spread0.250 · 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
Published2020
Admission routes1
Has abstractyes

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Same venueFayoum Journal of Agricultural Research and Development /Fayoum Journal of Agricultural Research and DevelopmentSame topicSoil and Land Suitability AnalysisFrench-language works237,207