Data from:Physiological effects of increased foraging effort in a small passerine
Bibliographic record
Abstract
Foraging to obtain food, either for self-maintenance or at presumably elevated rates to provision offspring, is thought to be an energetically demanding activity but one that is essential for fitness (higher reproductive success and survival). Nevertheless, the physiological mechanisms that allow some individuals to support higher foraging performance, and the mechanisms underlying costs of high workload, remain poorly understood. We experimentally manipulated foraging behaviour in zebra finches (Taeniopygia guttata) using the technique described by Koetsier and Verhulst (2011). Birds in the “high foraging effort” (HF) group had to obtain food either while flying/hovering or by making repeated hops or jumps from the ground up to the feeder, behaviour typical of the extremely energetically-expensive foraging mode observed in many free-living small passerines. HF birds made significantly more trips to the feeder per 10min whereas control birds spent more time (perched) at the feeder. Despite this marked change in foraging behaviour we documented few short- or long-term effects of “training” (3 days and 90 days of “training” respectively) and some of these effects were sex-specific. There were no effects of treatment on BMR, hematocrit, hemoglobin, or plasma glycerol, triglyceride, glucose levels, and masses of kidney, crop, large intestine, small intestine, gizzard and liver. HF females had higher masses of flight muscle, leg muscle, heart and lung compared to controls. In contrast, HF males had lower heart mass than controls and there were no differences for other organs. When both sexes were pooled, there were no effects of treatment on body composition. Finally, birds in the HF treatment had higher levels of reactive oxygen metabolites (dROMs) and, consequently, although treatment did not affect total antioxidant capacity (OXY), birds in the HF treatment had higher oxidative stress.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.032 | 0.008 |
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".