Exploring Ecosystem Impacts in Newfoundland and Labrador: Simulations with Harp Seal, Cod, and Capelin Across Three Historical Time Periods
Bibliographic record
Abstract
This repository documents the ecosystem role of harp seals on the Newfoundland and Labrador (NL) Shelf and Grand Banks, Canada, using the Ecopath with Ecosim (EwE) ecosystem model approach. Ecosystem simulations were conducted for three distinct time periods (1985–1987, 2013–2015, and 2018–2020) across two regions: NL Shelf and Grand Banks. The first Ecopath model represents the combined NL Shelf and Grand Banks for 1985–1987 (published here: https://doi.org/10.1139/f01-063), the second covers the same region for 2013–2015 (https://www.researchgate.net/publication/336145905). The third and fourth models separate the NL Shelf and Grand Banks for 2018–2020 (https://doi.org/10.1101/2024.10.22.619726), assuming an equal biomass distribution of harp seals between the two regions (50:50). Sensitivity analyses were also conducted, exploring an alternative distribution of harp seals (80% of biomass on NL Shelf and 20% of biomass on the Grand Banks), resulting in two additional models. All Ecopath models are included here for convenience and accessibility. For each period and sensitivity analysis, exploratory markdown reports were generated to assess the impacts of changes in harp seal, Atlantic cod, and capelin biomass under varying levels of depletion (LOD) and recovery (LOR). Results are presented at both the ecosystem scale (averages and trends) and the functional group scale (for all functional groups present in the Ecopath models). While this repository focuses on harp seals, Atlantic cod, and capelin, it also provides data for all other functional groups, enabling users to explore the effects of biomass changes on species of interest, such as shrimp, marine mammals, groundfish or invertebrates for example. Additionally, the repository provides all simulation input and output files, results, markdown reports, ecological indicators, and aggregated comparisons in a dedicated summary folder. Fully reproducible R projects and scripts are included to facilitate data processing and visualization. Rather than prescribing workflows, this repository aims to contribute to the Ecopath with Ecosim community by offering an example of how EwE simulations can be organized, reproduced, and explored. Further explanations are available in the README and in the “Nature of Analyses and Usage” section.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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 | 0.000 |
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.
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".