Structural differences between forests regenerating following spruce budworm defoliation and clear-cut harvesting: implications for marten
Bibliographic record
Abstract
American marten (Martes americana Turton) avoid recent clearcuts when establishing territories but do not avoid similarly aged stands with a history of extensive tree mortality caused by the eastern spruce budworm (Choristoneura fumiferana Clem.). We quantified differences in overstory vegetation, understory vegetation, and coarse woody debris between stands that were clear-cut or defoliated by spruce budworms 10-20 years prior to our study. Our objectives were to identify habitat features with functional significance for marten that were lacking in managed stands and to propose goals for silvicultural practices that more closely resemble a natural disturbance (insect defoliation), thus improving habitat quality for marten. In contrast to regenerating clearcuts, defoliated stands had greater volumes of snags, downed logs, and root masses and included taller trees. Although live-tree basal area was similar between stand types, our results suggest that vertical structure provided by large snags can offset limited availability of live trees for marten, particularly where coarse woody debris and understory vegetation are plentiful. In stands under even-aged management, habitat quality for marten may be enhanced by retention of >18 m2/ha cull trees and snags. Uneven-aged silvicultural systems, which more closely mimic natural disturbance by defoliating insects, may have particular promise for maintaining marten habitat.
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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 | 0.000 | 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 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".