The effects of partial cutting on the Rose-breasted Grosbeak: abundance, food availability, and nest survival
Bibliographic record
Abstract
Periodic partial harvesting of trees is an important economic activity within the highly fragmented woodlands of southern Ontario. We studied the population density, age structure, food abundance, productivity, and nest survival of Rose-breasted Grosbeaks (Pheucticus ludovicianus) nesting in 35 deciduous woodlots with varying intensities of harvest. Heavily cut woodlots contained higher densities of territorial males and greater abundances of fruit-bearing shrubs compared with standard cut and reference sites (uncut for >13 years). Results based on insect sampling were mixed, depending on the sampling technique and sample date. All treatments were demographic sinks, with populations in this landscape showing annual declines of 19%24%. Though the proportion of parasitized nests tended to be higher in heavily cut sites, harvesting had little effect on nest survival, nest initiation dates, clutch size, age structure, or the number of young fledged from a successful nest. Our results indicate that within the fragmented woodlots of southern Ontario, partial harvesting does not further degrade breeding habitat for Rose-breasted Grosbeaks. However, further research is needed to determine the underlying causes of population declines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".