Modelling microplastic bioaccumulation and biomagnification potential in the Galápagos penguin ecosystem using Ecopath and Ecosim (EwE) with Ecotracer
Bibliographic record
Abstract
This dataset, generated through trophodynamic Ecopath with Ecosim (EwE) modeling and Ecotracer, encompasses two models—a novel model by McMullen K. (2023), featuring the Galápagos penguin as a sentinel species for microplastics in the Galápagos Marine Reserve, and a model by Ruiz DJ and Wolff M (2011) focusing on the Bolivar Channel ecosystem. Empirical data was collected in October 2021 (McMullen K., 2023), including surface seawater, zooplankton, penguin prey, and penguin scat. This empirical data and other data from available literature feed the model. Various scenarios, notably a 99% elimination rate, were employed to gauge model sensitivity. Results indicate that microplastics can accumulate in predator-prey relationships, with biomagnification contingent on elimination rates. This underscores the urgent need for further investigation into the elimination rates of distinct plastics, addressing a crucial gap in contemporary microplastic ecotoxicology and bioaccumulation research. The dataset offers valuable insights into the complex dynamics of plastic pollution in marine ecosystems, urging heightened scrutiny of plastic elimination rates for informed conservation strategies. For methods see McMullen K. (2023). References: McMullen K. The Galápagos penguin as the “canary in the coal mine” for microplastics research in the Galápagos Marine Reserve & plastic pollution perceptions in Ecuadorian mangrove communities. M.Sc. Thesis. University of British Columbia. 2023. Available from: http://hdl.handle.net/2429/84664 Ruiz DJ, Wolff M. The Bolivar Channel Ecosystem of the Galapagos Marine Reserve: Energy flow structure and role of keystone groups. J. Sea. Res. 2011 Aug;66(2):123–34.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".