Exploring anthropogenic activities and management decisions using a novel environmental agent based model
Bibliographic record
Abstract
Lake whitefish (Coregonus clupeaformis) are an ecologically, economically, and culturally important species to the native and non-native fishers of Lake Huron, Canada. Studying the effects of anthropogenic activity on lake whitefish is of utmost importance to ensure this species remains viable in its environment for sustainable harvest. One analysis tool that is frequently used for ecological population risk assessments are agent-based models (ABMs), in which the population is represented as a network of heterogeneous individual agents that interact with one another and their environment. However, in an ABM that incorporates a high level of biological detail to model a large population moving within a spatial environment over time, significant computation is required to manage, manipulate, and store the relevant data for each agent over successive time iterations. We introduce a new approach to ABMs known as environmental ABMs (enviro-ABMs) to reduce this computational expense and simulation runtime. Specifically, we divide the environment into a collection of spatially indexed cells and treat each of these as a single agent, allowing fish to move from one contiguous cell to another. This reduces the computational requirements to a limited number of active cells. In addition to more predictable computational requirements, this method keeps all fish sorted by age and location for efficient mortality, spawning, and harvest operations, and reduces the amount of computational overhead needed. Applying the enviro-ABM to our case study in Lake Huron, we demonstrate how it can be used to model anthropogenic activities and stressors that may affect lake whitefish, and how the model can be used to facilitate fisheries management decision making. While the model is applied specifically to the case of whitefish in Lake Huron, it can be generalized to conduct risk assessment for other species in a variety of habitats.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".