Quantifying Differential Responses to Fruit Abundance by Two Rainforest Birds Using Long-Term Isotopic Monitoring
Bibliographic record
Abstract
Abstract Most tropical passerines feed on insects, fruit, or a combination of the two. The sugary pulps of fruit have lower amounts of protein than insects. We used stable-nitrogen isotope analysis (δ15N) of blood from two tropical rainforest birds that regularly feed on fruit—Red-throated Ant-Tanager (Habia fuscicauda) and Ochre-bellied Flycatcher (Mionectes oleagineus)—to quantify the relative amounts of assimilated protein from animal and plant sources. Because fruit and insect abundances vary seasonally in the tropics, the study was conducted during one year in Los Tuxtlas, Mexico. The study site has one major fruiting peak between April and July and a secondary peak between September and October. Some insects are more abundant from May to August. Red-throated Ant-Tanagers and Ochre-bellied Flycatchers rely heavily on insect protein when fruit is scarce, and then steadily increase their input of fruit protein as fruit abundance increases. Red-throated Ant-Tanagers rely almost entirely on fruit protein during the major fruiting peak, whereas Ochre-bellied Flycatchers have the largest input of fruit protein during the secondary fruit peak. Incubation in both species occurs from June to August, and most incubating individuals rely on a mixture of insects and fruit. In both species, examination of fecal contents showed the ingestion of the largest number of fruit species during the major fruiting peak. Cuantificación de la Respuesta Diferencial a la Abundancia de Frutos por Dos Especies de Aves Selváticas Mediante el Monitoreo Isotópico a Largo Plazo
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 | 0.001 | 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".