Multi-cohort stand structure as a coarse filter of variation in mixedwood boreal bird communities
Bibliographic record
Abstract
In targeting mature and over-mature forests for harvesting, management in the boreal forest has resulted in a net loss of older forests that often exhibit complex structural variation and multiple cohorts of trees. Multi-cohort forest management has been proposed as a management approach for these older forests that maintains structural wildlife habitat attributes. At the stand level, the approach relies on various partial harvest techniques to emulate the range of structural variation found in natural boreal landscapes. Here, we examine the extent to which boreal bird communities respond to multi-cohort-related structural variation in boreal mixedwood forests. In particular, we test the utility of parameters of Weibull distributions fitted to stand stem diameter distributions, which have figured prominently in methods to characterize multi-cohort structure, to explain variation in the entire bird community and in various species groupings defined by feeding guilds and forest-type associations. We also compare the explanatory power of the two Weibull parameters against 21 forest structure variables and stand age. In general, Weibull parameters outperformed stand age as a correlate of bird community variation and they were significant explanatory variables for the matrix of all species and for four species groupings, whereas age was significant for only one species grouping. When one or the other Weibull parameter was significant, it also tended to be significant even when variation due to the other was partialled out, supporting the importance not only of forest stature, but also of forest heterogeneity in understanding bird community composition. Thus, we found that multi-cohort-associated structural variation was important in explaining variation among boreal bird communities, supporting the idea of silvicultural approaches that aim at diversifying stand structural characteristics.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".