Sooty Grouse ( <i>Dendragapus fuliginosus</i> ) on Haida Gwaii nest from ground to tree canopy–nest site selection and predation risk
Bibliographic record
Abstract
Forest grouse species generally nest on the ground in well-concealed sites to avoid nest predation. We studied the nesting behaviour of Sooty Grouse ( Dendragapus fuliginosus sitkensis Swarth, 1921) in managed forests overbrowsed by introduced deer on the Haida Gwaii archipelago of British Columbia, Canada. As elsewhere across its range, Haida Gwaii Sooty Grouse avoided nesting in mid-forest stages (41–100 years) using both early <40-year (42.4%) and older >100-year (50.6%) forest stages. But unlike other forest grouse populations and species, they used elevated nests (54.1%), in both large old trees (14.1%) and on wood structures (40%), as well as ground nests (45.9%). Egg predation by Pacific martens ( Martes caurina (Merriam, 1890)) was the leading cause of nest failure. While the grouse expanded space use vertically for nesting, nest fate was not related to this behaviour but only to nest concealment. Few bird species show such plasticity in nest selection, and we postulate that Haida Gwaii Sooty Grouse diversify and randomize their nest site locations using an adaptive bet-hedging strategy to distribute predation risk. Forest harvest retention of large trees and wood structures for nesting may help support the Haida Gwaii grouse population.
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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".