Shrub densification heterogeneity in subarctic regions: the relative influence of historical and topographic variables
Bibliographic record
Abstract
Expansion of shrub species is widely reported in northern regions, although its extent varies across the landscape. In subarctic Québec (Canada), where dwarf birch (Betula glandulosa Michx) is the main species responsible for shrub expansion, little is known about the causes and consequences of this phenomenon. This study aims to identify the drivers of dwarf birch densification heterogeneity at the landscape level and to evaluate its influence on other shrub species. We used model selection with Akaike’s information criterion to rank ecologically relevant models including topographic, historical and edaphic variables. The influence of dwarf birch densification was evaluated through regression analysis. We found that the best model explaining the heterogeneity in dwarf birch densification in western Nunavik includes factors related to both historical conditions (initial shrub cover, time elapsed since last wildfire) and topography (type of environment). Among these factors, only the initial shrub cover had a significant positive influence on the shrub densification. Increase in dwarf birch cover was found to negatively influence the cover of other shrub species. However, no relation was found between dwarf birch densification and other shrub species richness, suggesting that the densification did not yet lead to the exclusion of less competitive species.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".