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Record W3133284299 · doi:10.1038/s41598-021-84251-4

Accumulation of airborne microplastics in lichens from a landfill dumping site (Italy)

2021· article· en· W3133284299 on OpenAlex
Stefano Loppi, Brett Roblin, Luca Paoli, Julian Aherne

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueScientific Reports · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsTrent University
Fundersnot available
KeywordsMicroplasticsLichenEnvironmental chemistryEnvironmental scienceTransectChemistryEcologyBiology

Abstract

fetched live from OpenAlex

The aim of this study was to assess if lichens (Flavoparmelia caperata) surrounding a landfill dumping site in Italy accumulated higher amounts of microplastics compared with lichens at more distant sites. Lichen samples were collected at three sites along a transect from the landfill: close (directly facing the landfill), intermediate (200 m), and remote (1500 m). Anthropogenic microparticles (fibres and fragments) were determined visually after wet peroxide digestion of the samples, and microplastics were identified based on a hot needle test; the type of plastic was identified by micro-Raman analysis. The results showed that lichens collected in the vicinity of the landfill accumulated the highest number of anthropogenic microfibres and fragments (147 mp/g dw), and consequently microplastics (79 mp/g dw), suggesting that the impact of landfill emissions is spatially limited. The proportion of fibres and fragments identified as microplastics was 40% across all sites and the most abundant polymer type was polyester or polyethylene terephthalate (68%). These results clearly indicated that lichens can effectively be used to monitor the deposition of microplastics.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
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
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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