MétaCan
Menu
Back to cohort
Record W2023007169 · doi:10.5539/ep.v2n1p88

Heavy Metals in Leaf, Stem Bark of Neem Tree (Azadirachta indica) and Roadside Dust in Maiduguri Metropolis, Borno State, Nigeria

2012· article· en· W2023007169 on OpenAlexvenueno aff
Joseph Clement Akan, Lawan Bukar Inuwa, Zaynab Muhammad Chellube, Babagana Lawan

Bibliographic record

VenueEnvironment and Pollution · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsAzadirachtaCadmiumBark (sound)ArsenicHeavy metalsHorticultureToxicologyEnvironmental scienceBiologyChemistryEnvironmental chemistryEcology

Abstract

fetched live from OpenAlex

Street dust and neem tree samples (Azadirachta indica) from Maiduguri Metropolis, Borno State, Nigeria were collected for the determination of trace elements. The highest concentrations of metals were found to be higher at the seven sampling points, while the lowest levels were observed in the street dust samples from the control sites. The concentrations of all the metals in plant samples were significantly highest in the leaves of Azadirachta indica, while the stem bark shows the least values. Levels of chromium (Cr), lead (Pb), nickel (Ni), cobalt (Co), cadmium (Cd) and arsenic (As) in plant samples exceeded the world health organization standard limits for medicinal plants. At the same time, the traffic situation in the area of study might be regarded as a source of heavy metal content in the roadside dust and plant samples.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.009

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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 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

Citations16
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueEnvironment and PollutionSame topicHeavy metals in environmentFrench-language works237,207