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Record W2758584640 · doi:10.1080/02757540.2017.1376664

High level of nickel tolerance and metal exclusion identified in silver maple ( <i>Acer saccharinum</i> )

2017· article· en· W2758584640 on OpenAlexafffundabout
K. K. Nkongolo, Ramya Narendrula-Kotha, K. N. Kalubi, Sabrina Rainville, P. Michael

Bibliographic record

VenueChemistry and Ecology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsLaurentian University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNickelBioaccumulationZincManganeseMetalCopperMetallurgyEnvironmental chemistryChemistryHeavy metalsHorticultureMaterials scienceBiology

Abstract

fetched live from OpenAlex

Nickel (Ni) and copper (Cu) are the most prevalent metals found in the Greater Sudbury Region ecosystems. The main objectives of this study are to (1) assess silver maple (Acer saccharinum) tolerance to different doses of Ni and (2) determine the translocation pattern of metals in A. sacharinum. This study revealed that A. sacharinum is highly tolerant to high doses of NI (1600 and 9200 mg/kg). Growth chamber screening trials revealed that Ni is stored in roots and does not translocate to other plant parts. Analysis of samples from A. sacharinum growing for >30 years in soil contaminated with metals also showed that the levels of iron (Fe), manganese (Mn), Ni, and zinc (Zn) were significantly higher in roots compared with soils and aerial parts. On the other hand, the amount of Cu was higher in soil compared with roots and other plant parts. In fact, the bioaccumulation factors (BFs) were 0.29, 2.00, 3.6, 1.9, and 4.0 for Cu, Fe, Mn, Ni, and Zn, respectively. The translocation from roots to aerial parts showed an insignificant level of movement of Cu, Fe, and Ni. Hence, A. saccharinum is classified as excluder for Fe, Mn, Ni, and Zn, and avoider for Cu.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.490
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.029
GPT teacher head0.253
Teacher spread0.223 · 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 designBench or experimental
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
Published2017
Admission routes3
Has abstractyes

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