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Record W4297678540 · doi:10.5281/zenodo.5710745

Workshop Report: Maximizing the Value of Environmental Microplastics Data

2021· report· en· W4297678540 on OpenAlexaff
Tia Jenkins, Rodney D. L. Smith, Nancy Goucher, Bhaleka Persaud, Stephanie Slowinski, Kathy Szigeti, Erin Clary, Mary K. Kruk, Win Cowger, Kelly Stathis, Mickey Nielsen, Lilian Tran, Krysha Dukacz, Philippe Van Cappellen

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typereport
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsWalter and Duncan Gordon FoundationMcMaster UniversityUniversity of Waterloo
Fundersnot available
KeywordsMicroplasticsValue (mathematics)Environmental scienceFisheryBiologyEcologyStatisticsMathematics

Abstract

fetched live from OpenAlex

This report summarizes the group discussions and priorities on microplastics data management that emerged from a virtual workshop hosted by the University of Waterloo’s Water Institute, the Federated Research Data Repository (FRDR), and the Gordon Foundation.

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)0.015
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.985
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.013
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.001
Scholarly communication0.0070.004
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0220.015

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 head0.057
GPT teacher head0.257
Teacher spread0.199 · 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.

Study designNot applicable
DomainReproducibility
GenreOther

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

Citations1
Published2021
Admission routes1
Has abstractyes

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