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Record W4402830775 · doi:10.1126/sciadv.ado5534

Exploring global oceanic persistence and ecological effects of legacy persistent organic pollutants across five decades

2024· review· en· W4402830775 on OpenAlexaff
Xue Zhang, Li Li, Zhiyong Xie, Jianmin Ma, Yi-Fan Li, Minghong Cai, Nanqi Ren, Roland Kallenborn, Zi-Feng Zhang, Xianming Zhang, Derek C. G. Muir

Bibliographic record

VenueScience Advances · 2024
Typereview
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsEnvironment and Climate Change CanadaConcordia University
FundersState Key Laboratory of Urban Water Resource and EnvironmentHarbin Institute of Technology
KeywordsEnvironmental sciencePollutantBiogeochemical cycleArcticContext (archaeology)Global changeClimate changeEcologyOceanographyGeographyBiologyGeology

Abstract

fetched live from OpenAlex

Global monitoring of persistent organic pollutants (POPs) has intensified following regulatory efforts aimed at reducing their release. In this context, we compiled over 10,000 POP measurements, reported from 1980 to 2023, to assess the effectiveness of these legislative measures in the global marine environments. While a general decreasing trend in legacy POP concentrations is evident across various maritime regions, highlighting the success of source control measures, the Arctic Ocean and its marginal seas have experienced a rise in POP levels. This increase suggests the northward migration of pollutants via ocean currents from mid-latitude regions to polar areas. Despite global efforts to reduce emissions, the continued transport and accumulation of pollutants to the Arctic regions may have substantial ecological impacts. Addressing these environmental challenges demands a thorough understanding of POP dynamics, including response times, multiphase transport, and biogeochemical cycling. Continued research into these processes is vital to accurately map their distribution and temporal variations within marine systems.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.978
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.004
Scholarly communication0.0000.003
Open science0.0020.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.335
Teacher spread0.276 · 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 designOther design
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

Citations45
Published2024
Admission routes1
Has abstractyes

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