Effect of forest management on a rare habitat specialist, the Bicknell's Thrush (<i>Catharus bicknelli</i>)
Bibliographic record
Abstract
Forest dwelling birds with narrow habitat preferences may be vulnerable to habitat changes from forest management. The Bicknell’s Thrush ( Catharus bicknelli (Ridgway, 1882)), a rare habitat specialist, occupies dense regenerating forest, including stands managed for timber. However, little is known of the impact of various forestry practices on Bicknell’s Thrush abundance. The purpose of our study was to determine how Bicknell’s Thrush abundance varied across the stages of a managed forest and to determine if abundance was affected by precommercial thinning, a practice that reduces stem density. Bicknell’s Thrush was most abundant in stands that were regenerating after being clear-cut 11–13 years earlier and of sufficient height and stem density to undergo thinning. Thrush abundance declined following thinning and was positively related to the amount of unthinned area remaining in the stand. Over all stand types, thrush abundance increased with increasing elevation and with the density of stems between 5 and 10 cm in diameter, but decreased with increasing amounts of bare ground. The results of this study suggest that Bicknell’s Thrush may benefit from the early successional habitat associated with managed forests, but may be negatively affected by treatments such as precommercial thinning that reduce stem densities.
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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 | 0.000 | 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) | 0.001 | 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".