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Freshwater systems in the Anthropocene: why we need to evaluate microplastics in the context of multiple stressors

2024· preprint· en· W4392584293 on OpenAlexafffund
Rachel K. Giles, Bonnie M. Hamilton

Bibliographic record

VenueF1000Research · 2024
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsMicroplasticsAnthropoceneStressorContext (archaeology)Open peer reviewPlant biologyBiologyPhysiologyEnvironmental ethicsNeuroscienceEcology

Abstract

fetched live from OpenAlex

<ns3:p>Microplastics are a diverse contaminant with complex physical and chemical properties. While microplastics have varying effects, most studies to date have focused on evaluating microplastics as a single stressor under stable environmental conditions. In reality, organisms are exposed to more than microplastics, and thus, it will be increasingly important to evaluate the effects of microplastics in the context of multiple anthropogenic stressors. Here, we highlight the need to assess the physical and chemical effects of microplastics, as well as their interactions with other anthropogenic stressors, at multiple levels of biological organization (i.e., sub-organismal, individual, population, community, ecosystem). We also outline research priorities and recommendations that will facilitate ecotoxicological assessments to better encompass the multidimensionality of microplastics as environmental conditions continue to change. By taking a multi-stressor ecotoxicological approach, we can work toward a better understanding of microplastic and other stressor effects at multiple levels of biological organization to help inform robust, evidenced-based policy and management decisions.</ns3:p>

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.042
GPT teacher head0.310
Teacher spread0.268 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations2
Published2024
Admission routes2
Has abstractyes

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