Exploring the use of environment-agent-based models for risk assessment of Great Lakes piping plovers
Bibliographic record
Abstract
Abstract The Piping Plover Charadrius melodus is an endangered species of shorebird endemic to North America. This species has been the centre of many modelling studies in the last decade. One model type that has been underused in Piping Plover studies is agent-based modelling, which can be used as an accurate risk assessment tool in simulating effects of anthropogenic activities on a given animal species. Recent innovations in ecological modelling have given rise to the environmental agent-based model (enviro-ABM), which efficiently stores information about spatially indexed environmental cells and treat those as agents. This restricts computation to only focus on environmental cells containing the species we are studying, allowing for a more efficient simulation. Using Python, a high-level programming language popular in scientific computing, we develop an enviro-ABM to provide simulations of Piping Plover hatchling growth during a given breeding season. We experiment with increasing levels of human presence and human exclosure size and observe their effects on the growth rate of the simulated Piping Plover hatchlings. Our simulations showed a clear decrease in Piping Plover growth rate as anthropogenic presence increased in the simulated environment. However, when we added a 100 m human exclosure around the nest, the effects of the anthropogenic presence were mitigated at each level. We conclude that an enviro-ABM can be used to assist with conservation and management decisions, with the caveat that the model be constantly updated and informed with results of field studies, especially those pertaining to foraging and energetics of Piping Plovers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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".