Bird communities associated with live residual tree patches within cut blocks and burned habitat in mixedwood boreal forests
Bibliographic record
Abstract
By retaining patches of trees in cut blocks, managers expect to retain some forest birds and create bird communities more similar to those found after fires. We surveyed birds from a range of patch sizes (1 to >3000 live residual trees), at four ages (2, 15, 30, 60 years), following two disturbance types (harvest, fire) in mixedwood boreal forest. Bird communities varied among patch sizes, forest ages, and disturbance types. Immediately post-disturbance, bird communities from large patches (>100 residual trees) were more similar to those from old forest than were communities from small patches ([Formula: see text]10 residual trees). Birds that nest or forage in open or riparian habitats had highest densities in small patches 2 years post-harvest, whereas birds that nest in large snags had highest densities in small patches 2 years post-fire. Throughout the chronosequence following both disturbance types, birds from small patches became more similar to those from old forest. Birds that nest and forage in small trees and shrubs were common 15 and 30 years post-disturbance, and generalist forest birds were common 60 years post-disturbance. Birds associated with old forest were present in all patch sizes 15, 30, and 60 years post-disturbance, although larger patches had higher densities of these species.
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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 | 0.000 |
| 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) | 0.001 | 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".