Landscape patterns of species-level association between ground-beetles and overstory trees in boreal forests of western Canada (Coleoptera, Carabidae)
Bibliographic record
Abstract
Spatial associations between species of trees and ground-beetles (Coleoptera: Carabidae) involve many indirect ecological processes, likely reflecting the function of numerous forest ecosystem components. Describing and quantifying these associations at the landscape scale is basic to the development of a surrogate-based framework for biodiversity monitoring and conservation. In this study, we used a systematic sampling grid covering 84 km(2) of boreal mixedwood forest to characterize the ground-beetle assemblage associated with each tree species occurring on this landscape. Projecting the distribution of relative basal area of each tree species on the beetle ordination diagram suggests that the carabid community is structured by the same environmental factors that affects the distribution of trees, or perhaps even by trees per se. Interestingly beetle species are associated with tree species of the same rank order of abundance on this landscape, suggesting that conservation of less abundant trees will concomitantly foster conservation of less abundant beetle species. Landscape patterns of association described here are based on characteristics that can be directly linked to provincial forest inventories, providing a basis that is already available for use of tree species as biodiversity surrogates in boreal forest land management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".