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Record W7023913424

Radiotracer methods for understanding contaminant dynamics in aquatic environments

2017· other· en· W7023913424 on OpenAlexaboutno aff

Bibliographic record

VenueThe Australian Nuclear Science and Technology Organisation Institutional Repository (The Australian Nuclear Science and Technology Organisation) · 2017
Typeother
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsnot available
FundersInternational Atomic Energy AgencyEuropean CommissionCentre International de Recherche sur le Cancer
KeywordsContaminationTracingTRACERSorptionRadionuclideAquatic ecosystemRadioactive contaminationRadioactive wasteNatural (archaeology)
DOInot available

Abstract

fetched live from OpenAlex

Radioactive tracers have a distinct advantage in tracing contaminant migration in natural systems and characterizing contaminant mobility and uptake into living organisms in already-contaminated environments or at trace (environmental) levels. To use the contaminant itself in its non-radioactive form, concentrations significantly higher than the normal contaminated background level are commonly required which may be undesirable from a toxicological, chemical or regulatory perspective by impacting on the very processes under study. In contrast, radioactive forms of the contaminant can often be more easily measured (often in situ or non-destructively) and imaged at trace levels (using autoradiography), and usually have the advantage of a short half-life to remove residual contaminant. As such radiotracers have a valuable role to play in contaminant dynamics studies from the lab scale to the field. In the lab, radiotracers are well established in studies of contaminant kinetics and bio-distribution in living organisms, in interactions with non-living natural environments, e.g., sorption to soils and sediment, rocks and organics matter, and in tracing contaminant flow pathways and rates. Radioisotopes of heavy metal contaminants (e.g., Cd, As, Se, Zn, Pb, Hg), nutrients (P, C) and the shorter-lived isotopes of longer lived radioactive contaminants such as Cs and Sr are commonly used in environmental contaminant studies. Recently, there is increasing interest and benefit in using radiotracer versions of emerging environmental contaminants such as persistent organic compounds or nanoparticles. While most radiotracer work is conducted in laboratories, this approach can be up-scaled to field environments. There are obvious scientific benefits of conducting studies in situ, where the tracer interacts with the complex natural environment rather than an artificially simplified laboratory representation. However there are few examples where this has been done. Since the first field scale uses of radiotracer in the mid-1950s, the majority of field scale radiotracer applications have been in the nexus between industry and environment sediment transport in harbours and dams, effluent dispersion from outfalls and in mining and oil extraction. Exceptions include whole ecosystem studies in the Canadian Experimental Lakes in the 1970s, heavy metals downstream of a uranium mine in Kakadu NP in Australia, and studies demonstrating the retardation of metals and nutrients in studies in Sweden. Increasingly public and regulatory concern about the potential impact and perception of radiotracing in field environments has made these methods appear largely inaccessible to the research community. However, the introduction of new biota dose modelling tools and guidelines over the past two decades has provided improved evaluation of the environmental impact of radiotracer releases to the environment and ensure and demonstrate their safe use.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0050.004

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 head0.022
GPT teacher head0.295
Teacher spread0.273 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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