Differential forage use makes carrying capacity equivocal on ranges of Scandinavian moose (Alces alces)
Bibliographic record
Abstract
Availability of preferred forage is hypothesized to be positively related to demographic performance in selective ungulates. Comparing two regions with high density of moose ( Alces alces (L., 1758)) having contrasting demographic performance and different composition of available plant species, we show that such a positive relationship may not always apply. The high-performance region (HP) had an estimated 41% higher total availability of browse per capita than the low-performance region (LP), but the availability of preferred species did not differ between the two regions. Although birch (genus Betula L.) was the most abundant browse in both regions (comprising 66% and 50% of the shoot amount available per m2in HP and LP, respectively), it dominated the diet of moose only in HP (constituting, e.g., 69% of all trees browsed in summer compared with 22% in LP). Further research is needed to identify the cause of the seemingly suboptimal use of birch in LP. We also quantified factors that determine forage availability, of which recent logging clearly was the most important: it multiplied browse availability but also reduced coverage of bilberry ( Vaccinum myrtillus L.), an important forage plant. Our study shows that for selective ungulates, indices of carrying capacity based on forage availability may not apply uniformly across ranges.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".