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Record W1547799649

EXPLORING THE TOXICITY LEVELS OF CHROMIUM AMONG WELDERS

2014· article· en· W1547799649 on OpenAlexaff
Ahmad M Boran, Zeid Al-Hourani, Ahed J Alkhatib, Abdulghani Zard, Murtala Muhammad, Fatima Laiche, Sani Ado Haruna, A'esha M Qasem, Mosleh A Alkhatatbeh

Bibliographic record

VenueEuropean Scientific Journal ESJ · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicChromium effects and bioremediation
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsChromiumMedicineOccupational exposureAtomic absorption spectroscopyToxicologyMetallurgyEnvironmental healthMaterials scienceBiology
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Welders are at the risk of exposure to Chromium which is associated with adverse health effects. Study objectives: To determine the prevalence of chromium toxicity among workers in welding industry, and to correlate the occupational exposure for chromium with respiratory morbidity symptoms. Methodology: Study design and setting: Cross-sectional experimental study design. The present study was conducted at Irbid Industrial City. A convenient sample of 51 participants was included in the study in addition to 61 references as a control group. Urine Chromium level was analyzed at Princess Haya Center for Biotechnology using Atomic Absorption Spectrometry. Results: The mean concentration of Chromium 15.87 ug/dl with SD + 14.52 ug/dl and 25.05 ug/dl with SD + 14.60 ug/dl for control group and welders respectively. There was a significant relationship between chromium exposure among welders compared with control (p value 0.000). Conclusion: 1- The present study showed that there is a significant exposure to Chromium among welders. 2- There is a significant correlation between Chromium and respiratory diseases as sensitivity. 3- Chromium was correlated significantly with possible high potential of certain environmental issues to have Chromium such as living close to factory, hazard wastes, organic solvents and gases.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.879
Threshold uncertainty score0.818

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0000.000
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 head0.051
GPT teacher head0.216
Teacher spread0.165 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2014
Admission routes1
Has abstractyes

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