Using programmable infusion pump in avian species – a validation methodology in geese
Bibliographic record
Abstract
Wildlife face anthropogenic and natural stressors that affect their physiology and behaviour. The activity of the hypothalamic–pituitary–adrenal axis and glucocorticoid (GC) production is intrinsically linked with energy regulation and environmental stressors. Understanding the physiological mechanisms behind animals' decisions requires experiments that manipulate GC levels and measure behavioural or life‐history outcomes. Current phenotypic engineering experiments lack the ability to deliver hormones in a pulsatile or circadian manner, limiting our ability to mimic chronic stress or isolate the effects of capture and handling from GC manipulation. We developed a method to externally attach commercial programmable infusion pumps (iPrecio SMP‐200) to birds to deliver hormones subcutaneously at pre‐programmed rates. We tested this method on five semi‐captive Canada geese Branta canadensis and five greater snow geese Anser caerulescens atlanticus , monitoring changes in body mass, wound healing, and the operational state of the pumps. In snow geese, we conducted a hormonal experiment by delivering adrenocorticotropic hormone (ACTH) via the pumps to stimulate GC production. We then measured body mass at three time points, recorded activity using dynamic body acceleration, and collected fecal samples daily to assess corticosterone levels (the main GC in birds). Pumps were deployed for 11 to 14 days, with 7 out of 10 remaining functional throughout. One bird showed minor inflammation, but no apparent adverse effects on body mass or activity were observed. ACTH delivery led to significant increases in fecal corticosterone levels and dynamic body acceleration. This study represents the first successful external use of iPrecio pumps in birds and provides a promising tool for investigating circadian hormone dynamics, chronic stress, and ecotoxicology in wild species under more natural conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".