Getting slimmer without dieting or exercising: zebra finches can rapidly lose mass while maintaining food intake and decreasing activity
Bibliographic record
Abstract
It is widely accepted that birds can adaptively regulate body mass in different ecological contexts, but little is known about how birds monitor and interpret their body mass or the mechanisms that allow for rapid changes in mass. Using captive zebra finches (Taeniopygia guttata), we experimentally increased perceived mass via attachment of weighted backpacks and provided birds with either an ad libitum mixed-seed diet or supplementary high-fat diet to investigate: (1) how birds assess their own body mass and (2) the physiological and/or behavioral mechanisms birds may employ to rapidly adjust body mass. In both experiments, and independent of diet treatment, birds with weighted backpacks rapidly lost mass within 2 days of backpack attachment while reducing overall activity and maintaining food intake. Additionally, our data suggest that birds interpret body mass via a physical mechanosensory pathway rather than a physiological pathway: rapid loss of mass between days 0 and 2 was not linked to changes in plasma metabolites (glycerol or triglyceride concentrations). We found no evidence that mass loss was a consequence of stress associated with attachment of weighted backpacks (based on plasma corticosterone measures). Our results suggest that the processes of energy balance and mass regulation involve a greater array of mechanisms than simply matching 'energy in', through the amount of food consumed, to 'energy out', dictated by activity. Zebra finches were able to decrease body mass through other, unidentified, mechanisms even while maintaining dietary intake and reducing overall activity.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".