Quality of mature aspen and maple forests for breeding Yellow-bellied Sapsuckers (Sphyrapicus varius)
Bibliographic record
Abstract
Mature aspen (genus Populus L..) and maple (genus Acer L.) forests appear to be high-quality breeding habitat for Yellow-bellied Sapsuckers ( Sphyrapicus varius (L., 1766)). To explore some of the mechanisms that influence quality of the breeding habitat, we measured demographic and breeding resources in four forest stands each of 95-year-old quaking aspen ( Populus tremuloides Michx.) and 182-year-old sugar maple ( Acer saccharum Marsh.) in Algonquin Provincial Park, Ontario, from 2007 to 2009. Population density was higher, egg laying earlier, clutches larger, hatching success greater, and per capita and population fledgling production higher in maple stands compared with aspen stands. Longer nest-building and prelaying stages in aspen stands delayed egg laying by 5 days relative to maple stands. The delay in egg laying in aspen stands may have been caused by lower quality sap resources, which then resulted in lower productivity. Adults delivered smaller food loads to nests in aspen stands than in maple stands, which may have increased nestling mortality in aspen stands. Although per capita fledgling production was lower in aspen stands than in maple stands, Yellow-bellied Sapsuckers in both forests were able to replace themselves, suggesting that mature deciduous and mixed-deciduous forests, in general, are high-quality breeding habitat for this sapsucker.
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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".