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Record W3000658297 · doi:10.1021/acs.est.9b05994

Increased Temperature and Turbulence Alter the Effects of Leachates from Tire Particles on Fathead Minnow (<i>Pimephales promelas</i>)

2020· article· en· W3000658297 on OpenAlexafffund
Anna Kolomijeca, Joanne L. Parrott, Hufsa Khan, Kallie Shires, Stacey Clarence, Cheryl Sullivan, Leah Chibwe, David Sinton, Chelsea M. Rochman

Bibliographic record

VenueEnvironmental Science & Technology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of GuelphEnvironment and Climate Change CanadaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFluorantheneMinnowPimephales promelasChryseneEnvironmental chemistryPyrenePhenanthreneChemistryLeachateMicroplasticsEcotoxicologyEnvironmental scienceFish <Actinopterygii>Organic chemistryBiologyFishery

Abstract

fetched live from OpenAlex

Tire particles are of concern as a stressor due to the combination of their chemical constituents, high emission rates, and global distribution. Once in the environment, they will interact with physical parameters (e.g., UV, temperature). The interaction of chemical pollution with changing physical environmental parameters is often underestimated in ecotoxicology. Here, we investigate the role of temperature, mechanical stress (i.e., turbulence), UV, and CO2 on the effects of tire leachates on fish. Two samples of tire particles were exposed to four different levels of each physical stressor. A toxicological assessment was performed with fathead minnow embryos assessing five end points (hatching success, time to hatch, length, deformities, and heart rate). Results showed that variations of temperature and mechanical stress affect the toxicological impact of tire leachates. Zn and/or polycyclic aromatic hydrocarbons (pyrene, phenanthrene, chrysene, benzo[a]pyrene, anthracene, naphthalene, fluoranthene, and benzo[ghi]perylene) were identified in the leachate and tire samples by Raman/surface-enhanced Raman spectroscopy and gas chromatography with mass spectroscopy, respectively.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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 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

Citations96
Published2020
Admission routes2
Has abstractyes

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