The Use of Intrinsic Markers for Studying the Migratory Movements of Bats
Bibliographic record
Abstract
Mortality of migratory bat species at wind energy facilities is a well-documented phenomenon, and mitigation and management are partially constrained by the current limited knowledge of bat migratory movements. Analyses of biochemical signatures in bat tissues ("intrinsic markers") can provide information about the migratory origins of individual bats. Many tissue samples for intrinsic marker analysis may be collected from living and dead bats, including carcasses collected at wind energy facilities. In this paper, we review the full suite of available intrinsic marker analysis techniques that may be used to study bat migration, with the goal of summarizing the current literature and highlighting knowledge gaps and opportunities. We discuss applications of the stable isotopes of hydrogen, oxygen, nitrogen, carbon, sulfur; radiogenic strontium isotopes; trace elements and contaminants; and the combination of these markers with each other and with other extrinsic markers. We further discuss the tissue types that may be analyzed for each and provide a synthesis of the generalized workflow required to link bats to origins using intrinsic markers. While stable hydrogen isotope techniques have clearly been the leading approach to infer migratory bat movement patterns across the landscape, here we emphasize a variety of lesser used intrinsic markers (i.e., strontium, trace elements, contaminants) that may address new study areas or answer novel research questions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".