The dynamics of hazel grouse (<i>Bonasa bonasia</i> L.) occurrence in habitat fragments
Bibliographic record
Abstract
The aim of this study was to evaluate the effects of habitat fragment size and isolation on the dynamics of hazel grouse (Bonasa bonasia L.) occurrence. Habitat fragments surrounded by nonhabitat coniferous forest, in an intensively managed forested landscape, were censused during seven seasons. None of the 33 habitat fragments were occupied in all seven seasons and 7 were never occupied. Turnover occurred in 79% of the habitat fragments. The most common occupation of a habitat fragment was by only one hazel grouse male (84%). Thus, the dynamics of hazel grouse occurrence in the habitat fragments was basically monitored on the scale of individuals. Large and less isolated habitat fragments with a high amount of cover were occupied significantly more often than small, isolated fragments. The effect of size appeared most clearly when analyzing the total number of hazel grouse occupying a habitat fragment. The appearance of hazel grouse in the habitat fragments was best explained by the amount of cover, distance to the nearest suitable habitat, and size of the habitat fragment. The effects of interfragment distance on the occurrence and appearance of hazel grouse implies that the habitat has become functionally disconnected for hazel grouse and suggests that the amount of suitable hazel grouse habitat left in this landscape has fallen below a critical level.
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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 | 0.001 | 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".