Composición y preferencia de materiales en nidos de vertebrados nidificadores de cavidades en el bosque templado andino de Chile
Bibliographic record
Abstract
The composition and quality of nesting materials may influence the breeding success of cavity-nesting vertebrates, providing protection and optimal temperature to eggs and chicks.Between 2010 and 2013, we studied the composition and preferences regarding nest materials used by cavity-nesters in Andean temperate forests, southern Chile.We deployed 240 nest-boxes, obtaining data for 290 nests for three bird species (Aphrastura spinicauda, Troglodytes aedon and Tachycineta meyeni) and four mammals (Dromiciops gliroides, Rattus rattus, Oligoryzomys longicaudatus and Irenomys tarsalis).Aphrastura spinicauda (n = 170) and D. gliroides (n = 91) comprised 90 % of the nests.A. spinicauda nests were composed mainly of vine tendrils (44.2 %) and branches of trees and shrubs (25.7 %).Dromiciops gliroides used mostly leaves of trees and shrubs (72.9 %), and bryophytes (20.2 %).The predominant and preferred leaf in the nests of both species was from bamboo Chusquea species.Aphrastura spinicauda avoided the leaves of Lophozonia obliqua, whereas D. gliroides avoided vines.These results suggest a previously unknown role of Chusquea spp. in the breeding ecology of cavity-nesting forest vertebrates.The thick matted nests and the acidic pH of the Chusquea spp.leaves may provide protection from predators and have a biocidal effect on pathogens.Chusquea spp.may have an important influence on the breeding success of cavity-nesters, although the benefits of using this nest material need to be confirmed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".