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Record W4309756837 · doi:10.1139/er-2022-0081

Sources of rare earth elements in the environment and their impact on living organisms

2022· article· en· W4309756837 on OpenAlexvenueno aff
M. A. Sysolyatina, A. S. Olkova

Bibliographic record

VenueEnvironmental Reviews · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsNatural (archaeology)BioaccumulationBiotaEnvironmental chemistryEnvironmental sciencePopulationRare earthEarth scienceEcologyChemistryGeologyBiologyGeographyArchaeology

Abstract

fetched live from OpenAlex

This review paper aims at understanding natural and anthropogenic sources of rare earth elements (REEs) in the environment, their mechanisms of action, individual toxicity of REEs, and individual toxicity of their mixtures, including with other toxicants. We also summarized the scientific knowledge on REE bioaccumulation by plants, animals, and fungi. The purpose of the work was to analyze published scientific information and identify unsolved scientific issues in the selected areas. At present, both natural positive geochemical anomalies of REEs and “spots” of anthropogenic origin are discovered. There is a relationship between natural and anthropogenic sources of REEs in the environment. Increasing REE mining leads to the exposure of rock layers rich in target elements, which are then leached from natural and waste rocks. As a result, the total content of REEs in soils of their extraction areas reaches 2.3 µg/kg, and in the snow of cities it ranges from 0.411 to 1.463 µg/kg, which is comparable with natural anomalies of elements. Most often such levels of REE accumulation are not dangerous for biota. REEs, like many trace elements, have an essential effect in low concentrations and have harmful effects only when the dose is increased. However, stimulation effects proven for agricultural plants and animals in the natural environment can influence the change in population and community structures. The effects of REE compounds have many uncertainties under the conditions of their joint action with each other and with other substances containing biogenic or potentially toxic elements. The article provides examples of additive, synergistic, and antagonistic actions of a mixture of elements. Further, it is shown that the bioaccumulation of REEs is comparable to the accumulation of heavy metals in living organisms. Data on the transfer of REEs along trophic chains are single and contradictory. Thus, REEs contribute to the toxic load on living organisms. The main “blank spots” in knowledge about the action of REEs that require further research are identification of the most sensitive and vulnerable species, establishment of long-term effects and clarification of the joint action of REEs with other substances.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.987

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)0.0140.000

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 head0.191
Teacher spread0.181 · 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

Citations18
Published2022
Admission routes1
Has abstractyes

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