Combining stable-isotope (δD) and band recovery data to improve probabilistic assignment of migratory birds to origin
Bibliographic record
Abstract
The recent application of stable-isotope analyses, particularly the use of stable-hydrogen-isotope (deltaD) measurements of animal tissues, has greatly improved our ability to infer geographic origins of migratory animals. However, many individual sources of error contribute to the overall error in assignment; thus likelihood-based assignments incorporating estimates of error are now favored. In addition, globally, the nature of the underlying precipitation-based deltaD isoscapes is such that longitudinal resolution is often compromised. For example, in North America, amount-weighted expected mean growing-season precipitation deltaD is similar between the boreal forest of southwestern Canada and areas of northern Quebec/Labrador and Alaska. Thus, it can often be difficult to distinguish objectively between these areas as potential origins for broadly distributed migrants using a single isotopic measurement. We developed a Bayesian framework for assigning geographic origins to migrant birds based on combined stable-isotope analysis of feathers and models of migratory directions estimated from band recovery data. We outline our method and show an example of its application for assigning origins to a population of migrant White-throated Sparrows (Zonotrichia albicollis) sampled at a Canadian Migration Monitoring Network station at Delta Marsh, Manitoba, Canada. We show that likelihood-based assignments of geographic origins can provide improved spatial resolution when models of migration direction are combined with assignments based on deltaD analysis of feathers.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".