INFLUENCE OF HABITAT VARIATION, NEST-SITE SELECTION, AND PARENTAL BEHAVIOR ON BREEDING SUCCESS OF RUDDY-CAPPED NIGHTINGALE THRUSHES (<i>CATHARUS FRANTZII</i>) IN CHIAPAS, MEXICO
Bibliographic record
Abstract
We examined the influence of ecological and behavioral factors on breeding success of Ruddy-capped Nightingale Thrushes (Catharus frantzii) in contiguous primary- and secondary-forest habitats during the 2000–2003 breeding seasons in the Central Highlands of Chiapas, Mexico. Breeding density was higher in primary, undisturbed forest than in secondary forest with less understory vegetation. Nest-site selection was related to nest-concealment attributes (visibility, canopy cover, and shrub density) at the nest-site and patch levels. Nest-site selection was stronger in secondary forest, which indicates that preferred nest-site attributes were more limited in that habitat. Overall success per breeding attempt varied annually from 20% to 35%, with a higher mean annual success in primary forest (42%) than in secondary forest (19%). Daily nest survival rates were higher in primary than in secondary forest and varied with nest cover, lateral visibility, and density of ground epiphytes. The number of parental visits to nests was higher in primary than in secondary forest and lower for successful than for failed nests during incubation but did not vary with habitat or fate during the nestling stage. Female nest-attentiveness was higher in primary forest than in secondary forest throughout the nesting attempt and was higher for successful nests during the nestling stage only. Thus, Ruddy-capped Nightingale Thrushes showed habitat-specific breeding performance, with the primary-forest habitats (cloud forest, riparian) supporting higher densities and nesting success than secondary, disturbed habitats.
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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 | 0.001 |
| 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".