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Record W4404436256 · doi:10.1504/ijgw.2025.10067933

Experimental Study for Recovery of Heavy Metals from Contaminated Soil using Arbuscular Mycorrhizal Fungi

2024· article· en· W4404436256 on OpenAlex
Ali Khalvati, Turgut T. Onay, A. Yağmur Gören, Bülent Budak

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Global Warming · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsOntario Tech UniversityThornhill Medical (Canada)
Fundersnot available
KeywordsArbuscular mycorrhizal fungiHeavy metalsEnvironmental scienceContaminationSoil contaminationEnvironmental chemistryAgronomyChemistryBiologySoil scienceSoil waterEcologyHorticultureInoculation

Abstract

fetched live from OpenAlex

Soil micro-organisms like arbuscular mycorrhizal fungi can provide beneficial symbiosis to their host plant and have been adopted to recover metal-polluted soils. This study investigates the removal of heavy metals from soil using phytoremediation in the presence of fungi. The results indicate that the sunflower plant illustrates the highest copper accumulation, with 18.55 mg/kg. In contrast, sunflower and sorghum controls (non-microorganisms) showed weak capability to transfer copper through plant biomass with 0.91 and 0.97 mg/kg, respectively. Both plants showed that phytoremediation can be a promising approach to providing sustainable solutions for soil heavy metal contamination in the presence of fungi.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.227
Threshold uncertainty score0.833

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 studies0.0000.000
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 head0.023
GPT teacher head0.295
Teacher spread0.272 · 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