Agent Based Modelling of Caribou Environmental Interactions in the Canadian Arctic
Bibliographic record
Abstract
Agent or individual based modelling (IBM) is recognized as an important tool in biological modelling for simulating animal behaviour and interactions with the environment at the level of the individual while maintaining a collective perspective. The method allows rules to be programmed for the interactions between like and unlike species, responses to geographic factors and impacts from human activities. By emphasizing the development of appropriate rules for the individual, one can avoid imposing ad hoc assumptions concerning the population as a whole and instead allow emergent phenomena to unfold at the larger scale. Here we report on preliminary results using IBM to simulate the movement and population changes of an idealized caribou herd in a Northern Canadian Arctic setting. A wolf population is included in the simulation to study how the predator-prey relations impact the fluctuations in total population. The simulations use GIS data to account for a varying landscape including topography, water bodies and vegetation. The challenges of developing the software components will be discussed including the relative merits of using C# and NetLogo as programming languages. Future inclusion of Inuit hunter agents will be discussed.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".