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Record W2168316657 · doi:10.5539/jgg.v5n4p63

An Assessment of Toxic in Urban Soils Using Garden Cress (Lepidium sativum) in Vasileostrovsky Ostrov and Elagin Ostrov, Saint Petersburg, Russia

2013· article· en· W2168316657 on OpenAlexvenueno aff
Kwabena Awere Gyekye

Bibliographic record

VenueJournal of Geography and Geology · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicLichen and fungal ecology
Canadian institutionsnot available
Fundersnot available
KeywordsLepidium sativumSativumSoil waterSaint petersburgPhytotoxicityHorticultureSoil testSeedlingBotanyBiologyEnvironmental scienceAgronomyChemistryGerminationEcologyGeographyRussian federation

Abstract

fetched live from OpenAlex

The study was conducted to detect toxicity in urban soils of Vasileostrovsky Ostrov and Elagin Ostrov in St. Petersburg, Russia, an area characterised by wide variation in land use. A total of 37 soil samples were collected from the two study areas. Garden cress, Lepidium sativum, (L. sativum) was used as the test organism to detect the presence of toxic soils. The results indicated that soils from Vasileostrovsky Ostrov were toxic to L. sativum; the level of toxicity ranged between 40–60% (mild to moderately toxic). Tests of soils from Elagin Ostrov revealed that soils, generally, were nontoxic (90–100%). The results of most examined samples showed that soil extract had a stimulating effect on the growth of fronds; however there were few instances whereby soil extract inhibited the growth of L. sativum. The results from the study indicated that L. sativum is capable of detecting toxic soils. The different reactions from L. sativum to soil extracts could be attributed to site-specific conditions. The study recommends the use of L. sativum as a test organism to conduct biomonitoring of urban soil on a wide scale because of its simplicity, sensitivity, and cost-effectiveness.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.296

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.010
GPT teacher head0.255
Teacher spread0.245 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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