Nesting-tree preferences of the black woodpecker—the biggest cavity excavator in a conifer-dominated forests in Poland
Bibliographic record
Abstract
The black woodpecker Dryocopus martius is an ecologically disproportionately important forest species owing to its abundance. Its large cavities provide breeding sites and shelter for many species—large birds, mammals, and social insects. I evaluated the nest tree preferences of black woodpeckers in the Augustów Forest, northeast Poland. Approximately 400 black woodpecker cavities were observed. The Scots pine, Pinus sylvestris, was the most commonly selected tree species, accounting for 90%. The cavity trees were 55–225 years old. All trees younger than 90 years were broadleaved tree species. The trees used to excavate the cavities had a larger diameter at the breast height (DBH) than the average of the stand. The trees selected by black woodpeckers were significantly shorter than the average height of the stands. Over 60% of the cavities were excavated 10–16 m above ground level. I found that the DBH and the first branch height were critical factors affecting the cavity entrance height. In pine-dominated forests, black woodpeckers preferred dead trees. Approximately 44% of new cavities were excavated from dead trees. Leaving dead or dying large trees in commercial forests benefits black woodpeckers and large secondary cavity nesters that depend on it and promotes biodiversity conservation. Birds excavate new cavities at a high rate yearly, in contrast with beech-dominated forests.
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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) | 0.001 | 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".