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Record W2598589720

Evaluation of Native Ohio Plants to Lead and Zinc Contaminated Soils

2008· article· en· W2598589720 on OpenAlexaboutno aff
William E. Ondrasik

Bibliographic record

VenueOhioLink ETD Center (Ohio Library and Information Network) · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterContaminationLead (geology)Environmental scienceSoil contaminationZincGeologySoil scienceBiologyEcologyMetallurgyMaterials science
DOInot available

Abstract

fetched live from OpenAlex

Phytoremediation has been acknowledged for quite some time now, as a viable alternative to traditional, more invasive, remediation practices.However, there is a large demand for research relating to the association between specific plants and metal contaminants.The objective to this research is to identify native plants capable of removing or tolerating metal contaminants in soils.Two native Ohio plants commonly found in wet habitat will be evaluated for tolerance and accumulation of zinc and lead.The soil was spiked with two metals, lead and zinc, commonly found along riverbanks in the local area around the Mahoning River.The plants were grown in single metal as well as mixed metal spiked soil for a period of 15 weeks.Once the plants have grown substantially, they were harvested, dried and processed for analysis.The concentrations of metals found in the root area of the soil samples were compared to the spiked soil samples before growth.Both Indian grass and Canada wildrye soil samples showed losses of available metals, with small amounts of metals found in the plant tissue.This indicates that, even though there was limited above ground plant growth, both species may be tolerable to soils containing various concentrations of zinc and lead.iv

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score1.000

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 communication0.0000.005
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 head0.018
GPT teacher head0.224
Teacher spread0.205 · 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
Published2008
Admission routes1
Has abstractyes

Explore more

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