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Record W4285214989 · doi:10.1071/en22030

Metal contaminants of emerging concern in aquatic systems

2022· article· en· W4285214989 on OpenAlexafffund
Graeme E. Batley, Peter G. C. Campbell

Bibliographic record

VenueEnvironmental Chemistry · 2022
Typearticle
Languageen
FieldChemistry
TopicRadioactive element chemistry and processing
Canadian institutionsInstitut National de la Recherche Scientifique
FundersCommonwealth Scientific and Industrial Research OrganisationU.S. Geological SurveyInstitut national de la recherche scientifique
KeywordsContext (archaeology)Aquatic ecosystemEnvironmental chemistryEnvironmental scienceChemistryBiology

Abstract

fetched live from OpenAlex

Environmental context There is potential for a range of metals being used in emerging industries to pose a risk if they reach aquatic environments. This is assessed by evaluating known environmental concentrations against available toxicity data. In most instances risks are low with current usage. Areas are identified where additional data are needed. Abstract The environmental concentrations and aquatic toxicity of a range of technology-critical metals comprising platinum group and rare earth group elements, together with gallium, germanium, indium, lithium, niobium, rhenium, tantalum, tellurium and thallium, have been reviewed to determine whether they pose a risk to aquatic ecosystem health. There is a reasonable body of toxicity data for most, but the quality is quite variable, and more data are required. Chronic toxicity EC10 or NOEC values are generally in the low mg L–1 range, far higher than the current environmental concentrations in the ng L–1 range, meaning that the existing risks to ecosystem health are extremely low. Missing are reliable toxicity data for niobium and tantalum, while confounding results for lanthanum toxicity need to be resolved. There is a likelihood that the currently low concentrations of most of these elements will increase in future years. Whether these concentrations are in bioavailable forms remains to be reliably determined. For most of the elements, measured speciation information is scarce, and unfortunately the thermodynamic data required to calculate their speciation are incomplete. In addition to this problem of uncertain speciation for some of these metals, notably those present in oxidation states of III or higher, there is also a need to explore the links between speciation and bioavailability for these higher valence metals. For circumneutral solutions, the calculated concentrations of the free metal ion tend to be very low for these metals and under such conditions the link between metal speciation and bioavailability is unclear.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.011
GPT teacher head0.222
Teacher spread0.210 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreReview

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

Citations47
Published2022
Admission routes2
Has abstractyes

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