Development of an ecological model for the Riding Mountain National Park elk population
Bibliographic record
Abstract
The state of ecological integrity can be determined by assessing the viability of a species that is considered vital to the ecosystem (Woodley 1993). This study involved modeling the Riding Mountain National Park elk population and components, which influenced and impacted the viability of the elk population, thereby indicating the state of ecological integrity within RMNP. Main components included in the model were wolves, human harvest, bear predation and winter severity. The development of the model utilized the STELLA software. As well, data were gathered from RMNP, the Manitoba Department of Conservation and various studies. The data was used to build the formulae that were placed in the model and then run. Sensitivity runs were conducted by varying the values of human harvest rate on elk, bear predation rate of elk calves, the adult and yearling birth rate of elk, and the wolf population. As well, additional runs were also carried out to test the elk population. The results of the run with the initial values showed an elk population that is beginning to decline. As well, the sensitivity runs indicated that the elk population was sensitive to changes in the human harvest rate on elk, the bear predation rate on calves and the adult birth rate. The model results also indicated that elk population was not that sensitive to changes in the wolf population. As well, the effect of winter severity on the elk population was minimal. The sensitivity of the elk population to the human harvest rate component indicates that the viability of the elk population could be significantly influenced by increases in the harvest rate. Because this is the a component of the model that RMNP managers can influence because RMNP managers are involved in setting the regulated harvest rates outside RMNP, the results of the model runs are beneficial for developing management practices that will help manage this component to prevent it from compromising the viability of the elk and possibly the ecological integrity of RMNP.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".