Evaluating the Current State of Findability and Accessibility of Microplastics Data
Bibliographic record
Abstract
Tia Jenkins1, Bhaleka D. Persaud1, Win Cowger2, Kathy Szigeti3, Dominique G. Roche4,5, Erin Clary6, Stephanie Slowinski1, Benjamin Lei1, Amila Abeynayaka7, Ebenezer S. Nyadjro8,9, Thomas Maes10, Leah Thornton Hampton11, Melanie Bergmann12, Julian Aherne13, Sherri A. Mason14, John F. Honek15, Fereidoun Rezanezhad1, Amy L. Lusher16, Andy M. Booth17, Rodney D. L. Smith15 and Philippe Van Cappellen1. \n \nAffiliations: \n1 Department of Earth and Environmental Sciences, University of Waterloo, Waterloo, ON, Canada \n2 Moore Institute for Plastic Pollution Research, Long Beach, CA, United States \n3 Davis Centre Library, University of Waterloo, Waterloo, ON, Canada \n4 Department of Biology, Carleton University, Ottawa, ON, Canada \n5 Institute of Biology, University of Neuchâtel, Neuchâtel, Switzerland \n6 Digital Research Alliance of Canada, Ottawa, ON, Canada \n7 Institute for Global Environment Strategies (IGES), Kanagawa, Japan \n8 National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI), Stennis Space Center, Starkville, MS, United States \n9 Northern Gulf Institute, Mississippi State University, Stennis Space Center, Starkville, MS, United States \n10 GRID-Arendal, Arendal, Norway \n11 Southern California Coastal Water Research Project (SCCWRP), Costa Mesa, CA, United States \n12 Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany \n13 School of Environment, Trent University, Peterborough, ON, Canada \n14 The Behrend College, Pennsylvania State University, Erie, PA, United States \n15 Department of Chemistry, University of Waterloo, Waterloo, ON, Canada \n16 Norwegian Institute for Water Research, Oslo, Norway \n17 SINTEF Ocean, Trondheim, Norway
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.039 | 0.211 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".