Distribution of pair quality in a tree-nesting waterbird colony: central-periphery model vs. satellite model
Bibliographic record
Abstract
The spatial distribution of pair quality within waterbird colonies has been suggested to follow one of three theoretical models: central-periphery, satellite, or random. The central-periphery pattern occurs in homogeneous habitats, where good-quality pairs occupy better protected, central nesting sites. In contrast, the satellite and random patterns are associated with heterogeneous habitats and they assume that good-quality pairs occupy the most attractive nesting sites irrespectively of their location within the colony. Spatial patterns of laying date, clutch size, and fledging success were analysed with geostatistical tools in the colony of tree-nesting subspecies of Great Cormorant ( Phalacrocorax carbo sinensis (Blumenbach, 1798)) in central Poland. There was support for the random or satellite model in the distribution of clutch size, which was considered a reliable proxy of pair quality. We also found a positive correlation of clutch size with nest height. These results implicate that the habitat of tree-nesting colonial waterbirds may produce sufficient variation in the nesting-site quality to disrupt the central-periphery gradients of pair-quality distribution. In contrast, distribution of fledging success within the colony followed a clear central-periphery pattern, which was suggested to reflect an increased predation rate at the edges of the colony, rather than the intrinsic quality of breeding birds.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".