Metabolite compositions on skins of eastern hellbenders Cryptobranchus alleganiensis alleganiensis differ with location and captivity
Bibliographic record
Abstract
Eastern hellbenders Cryptobranchus alleganiensis alleganiensis, large aquatic salamanders, are declining over most of their range. The amphibian-killing fungus Batrachochytrium dendrobatidis (Bd) has contributed to global amphibian declines and has been detected on eastern hellbenders, but infection intensities were lower than those of species that are more susceptible to Bd. The factors limiting Bd on hellbenders may include antifungal metabolites produced by their skin microbiota. We used a metabolite fingerprinting technique to noninvasively identify the presence, but not identity, of metabolites associated with eastern hellbenders. We surveyed the skin of wild eastern hellbenders to test whether the composition and richness (i.e. number of metabolites) of their metabolites are explained by Bd status or location. Furthermore, we surveyed for metabolites on captive eastern hellbenders to test whether metabolite compositions were different between captive and wild eastern hellbenders. Bd detection was not associated with either metabolite richness or composition. Both metabolite composition and richness differed significantly on hellbenders from different locations (i.e. states). For metabolite composition, there was a statistical interaction between location and Bd status. Metabolite richness was greater on captive eastern hellbenders compared to wild hellbenders, and metabolite compositions differed between wild and captive eastern hellbenders. The methods we employed to detect metabolite profiles effectively grouped individuals by location even though metabolite composition and richness have high levels of intraspecific variation. Understanding the drivers and functional consequences of assemblages of skin metabolites on amphibian health will be an important step toward understanding the mechanisms that result in disease vulnerability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".