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Record W4281667483 · doi:10.1021/acs.est.2c01204

Persistent Problem: Global Challenges to Managing PCBs

2022· article· en· W4281667483 on OpenAlexafffundabout
Lisa Melymuk, Jonathan Blumenthal, Ondřej Sáňka, Adriana Shu-Yin, Veena Singla, Kateřina Šebková, Kristi Pullen Fedinick, Miriam L. Diamond

Bibliographic record

VenueEnvironmental Science & Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicToxic Organic Pollutants Impact
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaEnvironment and Climate Change CanadaRECETOX Přírodovědecké Fakulty Masarykovy UniverzityEuropean CommissionU.S. Environmental Protection Agency
KeywordsConventionBusinessEnvironmental scienceStock (firearms)PollutantEnvironmental protectionEnvironmental planningWaste managementEngineeringPolitical scienceChemistry

Abstract

fetched live from OpenAlex

Polychlorinated biphenyls (PCBs), "famous" as persistent organic pollutants (POPs), have been managed nationally since the 1970s and globally under the Stockholm Convention on POPs since 2004, requiring environmentally sound management (ESM) of PCBs by 2028. At most, 30% of countries are on track to achieve ESM by 2028. Globally over 10 million tonnes of PCB-containing materials remain, mostly in countries lacking the ability to manage PCB waste. Canada (Ontario) and Czechia, both parties to the Stockholm Convention, are close to achieving the 2028 goal, having reduced their stocks of pure PCBs by 99% in the past 10 years. In contrast, the USA, not a party to the Stockholm Convention, continues to have a substantial but poorly inventoried stock of PCBs and only ∼3% decrease in mass of PCBs since 2006. PCB management, which depends on Stockholm Convention support and national compliance, portends major challenges for POP management. The failure to manage global PCB stocks >30 years after the end of production highlights the urgent need to prioritize reducing production and use of newer, more widely distributed POPs such as chlorinated paraffins and per- and polyfluorinated alkyl substances, as these management challenges are unlikely to be resolved in the coming decades.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.748
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.005
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.002

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations139
Published2022
Admission routes3
Has abstractyes

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