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Record W4246605077 · doi:10.31230/osf.io/gbr3m

Linking plastic ingestion research with marine wildlife conservation

2018· preprint· en· W4246605077 on OpenAlexaff
Stephanie Avery‐Gomm, Jennifer F. Provencher, Stephanie B. Borrelle

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsAcadia University
Fundersnot available
KeywordsPlastic pollutionWildlifeWildlife conservationMicroplasticsIngestionEnvironmental planningEnvironmental resource managementPopulationEnvironmental protectionEnvironmental scienceEcologyBiologyEnvironmental health

Abstract

fetched live from OpenAlex

Plastic is an increasingly pervasive marine pollutant. Concomitantly, the number of studies documenting plastic ingestion in wildlife is accelerating. Many of these studies aim to provide a baseline against which future levels of plastic ingestion can be compared, and are motivated by an underlying interest in the conservation of their study species and ecosystems. Although this research has helped to raise the profile of plastic as a pollutant of emerging concern, there is a disconnect between research examining plastic pollution and wildlife conservation. We present ideas to further discussion about how plastic ingestion research could benefit wildlife conservation by prioritising studies that elucidates the significance of plastic pollution as a population-level threat, identifies vulnerable populations, and evaluates strategies for mitigating impacts. The benefit of plastic ingestion research to marine wildlife can be improved by establishing a clearer understanding of how discoveries will be integrated into conservation and policy actions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.006
Scholarly communication0.0050.007
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.048
GPT teacher head0.280
Teacher spread0.231 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations4
Published2018
Admission routes1
Has abstractyes

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