Use of TLC‐FID and GC‐MS/FID to examine the effects of migratory state, diet and captivity on preen wax composition in White‐throated Sparrows <i>Zonotrichia albicollis</i>
Bibliographic record
Abstract
Preen wax is important for plumage maintenance and other functions. Its chemical composition is complex, and separating and quantifying its components, commonly by gas chromatography (GC), can be challenging. We present a simple analytical system consisting of thin‐layer chromatography/flame ionization detection (TLC‐FID) using a solvent system of 100% toluene to analyse the complex compound classes present in preen wax. We used GC and TLC‐FID to investigate the effects of migratory status, diet and captivity on the preen wax composition of White‐throated Sparrows Zonotrichia albicollis , and to measure the quantity of preen wax on the head, primary and tail feathers. White‐throated Sparrows produced preen wax containing only monoesters regardless of migratory state. The monoesters contained several isomers consisting of homologous series of fatty alcohols (C10–C20) and fatty acids (C13–C19) esterified together in different combinations to form monoesters with total carbon numbers ranging from C23 to C38. Weighted average monoester carbon number was greater in captive birds than in wild birds and was greater in captives fed a formulated diet enriched with sesame oil than in birds fed the same diet enriched with fish oil. Captivity and migratory state also affected the complexity of the mixture of monoesters. There was significantly more preen wax on head feathers compared with primary and tail feathers. We suggest that among its many functions, preen wax may play a role in drag reduction by affecting the physical properties of feathers, and/or the fluid flow at their surfaces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".