Peer Review #2 of "Using trace elements to identify the geographic origin of migratory bats (v0.1)"
Bibliographic record
Abstract
The expansion of the wind energy industry has had benefits in terms of increased renewable energy production but has also led to increased mortality of migratory bats due to interactions with wind turbines.A key question that could guide bat-related management activities is identifying the geographic origin of bats killed at wind-energy facilities.Generating this information requires developing new methods for identifying the geographic sources of individual bats.Here we explore the viability of assigning geographic origin using trace element analyses of fur to infer the summer molting location of eastern red bats (Lasiurus borealis).Our approach is based on the idea that the concentration of trace elements in bat fur is related through the food chain to the amount of trace elements present in the soil, which varies across large geographic scales.Specifically, we used inductively coupled plasma -mass spectrometry to determine the concentration of fourteen trace elements in fur of 126 known-origin eastern red bats to generate a basemap for assignment throughout the range of this species in eastern North America.We then compared this map to publicly available soil trace element concentrations for the U.S. and Canada, used a probabilistic framework to generate likelihood-of-origin maps for each bat, and assessed how well trace element profiles predicted the origins of these individuals.Overall, our results suggest that trace elements allow successful assignment of individual bats 80% of the time while reducing probable locations in half.Our study supports the use of trace elements to identify the geographic origin of eastern red and perhaps other migratory bats, particularly when combined with data from other biomarkers such as genetic and stable isotope data.
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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 | 0.013 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.503 | 0.346 |
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".