A multi‐isotope (δ<sup>13</sup>C, δ<sup>15</sup>N, δ<sup>2</sup>H) feather isoscape to assign Afrotropical migrant birds to origins
Bibliographic record
Abstract
A universal challenge in methodology used to study the ecology, conservation and evolutionary biology of migratory species is the quantification of connectivity among breeding, wintering and stopover sites. For the avian Eurasian‐Afrotropical migratory system, knowledge of geographical wintering areas used by migrants that breed in Europe remains deficient, despite the advent of satellite transmitters and geolocators. Here we explored the use of theoretical plant δ 13 C and δ 15 N landscape distributions coupled with δ 2 H hydrologic models to construct multi‐isotopic avian foodweb clusters for Africa. The cluster analysis identified four distinct regions of Africa based on all three isotopes ( 13 C, 2 H, 15 N), and five regions based only on 13 C and 15 N. We applied known isotopic diet‐tissue discrimination factors to map equivalent feather isotopic clusters for Africa. The validity of these feather isotopic clusters was tested by examining how well known‐ and unknown‐origin species were placed in regions of Africa using previously published feather isotope data. The success of this multi‐isotopic cluster model depended upon the species of interest and additionally on how well potential winter molt origins in Africa were constrained by prior information. Ground‐truthing data suggested this approach will be useful for first‐order approximation of overwintering regions for Afrotropical migrants and will be improved as our understanding of the nature of isoscapes for Africa is refined.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".