Trace element profiles as unique identifiers of western sandpiper (<i>Calidris mauri</i>) populations
Bibliographic record
Abstract
Understanding the ecology and evolution of migratory animals requires information on how populations are geographically linked between periods of the annual cycle. To examine whether trace elements could be used to track migratory birds, we analyzed concentrations of 42 trace elements in feathers of western sandpipers ( Calidris mauri (Cabanis, 1857)) that were grown at five different wintering sites ranging from San Francisco Bay (USA) to the Bay of Panama. Linear discriminant analysis of 15 elements correctly classified all 26 individuals to their wintering sites, including two sites that were separated by less than 3 km. A randomization procedure confirmed the robustness of these findings. Our analysis suggests that trace elements can be used to assign individuals to specific sites of origin. Although we did not sample feathers from all wintering areas, the regions our sites represented comprised a significant percentage of the global population. However, since trace element profiles appear to be highly specific to geographic sites, we suggest that this technique is best suited for cases where samples can be obtained from the majority of populations throughout a species range. Thus, under certain circumstances, trace element profiles may provide the potential to identify populations with a high degree of spatial accuracy.
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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 | 0.002 | 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".