Large Mammals in the North: Climate Change and Bottom Up and Top Down Influences
Bibliographic record
Abstract
the literature indicates a continued controversy whether ungulate populations are controlled from the bottom-up or the top-down and whether wolf predation is benefi cial removing sick and unfi t in-p or the top-down and whether wolf predation is beneficial removing sick and unfit individuals or detrimental, driving populations into the so-called "predator pit". a macro-ecological approach was used to address these questions supporting the following conclusions: ungulates have evolved at the biome spatial scale, late and early succession specialists occur in each biome, one is larger and one is smaller, historically wolves occurred in all North american biomes as primary predators of ungulates, wolves specialize on the most common ungulate species, wolves change morphologically in relation to the size of their primary prey, pack size changes in relation to the size of their primary prey, wolf predation can be beneficial or detrimental depending upon the numerical and size relationship between the ungulate species in the system. Climate changes such as fire, drought and insect infestation will create early successional habitat increasing early successional specialists numbers and decreasing late successional ungulate population numbers. Bottom-up and top-down forces exist in all populations where wolves occur and managers need policies that support the smaller sized ungulate in the ecosystem, if they want to maintain both species at stable or increasing population levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".