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Record W3033752573 · doi:10.1242/jeb.214346

Most salamanders glow: now what?

2020· article· en· W3033752573 on OpenAlexaff
Brittney G. Borowiec

Bibliographic record

VenueJournal of Experimental Biology · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicbioluminescence and chemiluminescence research
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsCreaturesTiger salamanderSalamanderCamouflageAmphibianBiologyBlue lightEcologyZoologyNatural (archaeology)LarvaPaleontologyOptics

Abstract

fetched live from OpenAlex

Jellyfishes, fireflies and many deep-sea creatures are famously bioluminescent, harnessing specialized chemical reactions to emit an eerie glow. The biological flashlights may be involved in camouflage, hunting and even enhanced communication in low-light environments. However, other animals use a different mechanism to produce light – biofluorescence – by absorbing high-energy wavelengths of light (blue and UV) before re-emitting them as a blue, green or even red glow. Recent work has shown that a smattering of creatures, including chameleons, parrots, penguins and even some rodents, have added biofluorescence to their palette of colours. However, instead of looking for individual examples of glowing animals, Jennifer Lamb and Matthew Davis from St Cloud State University in Minnesota, USA, took a different, broader approach. They conducted a survey of amphibians, focusing on newts and salamanders, to find out how widespread the phenomenon is.Lamb and Davis scoured the pet trade, the natural environment and the Shedd Aquarium in the USA for as many amphibians as they could find. Then, they gave each species its moment in a blue or UV spotlight, beaming the animals with enough light to bring out their hidden biofluorescent colours. The researchers then viewed their subjects through a filter that blocked light bouncing off the skin, revealing the subtle, inner gleam of biofluorescence.Every amphibian examined (mostly salamanders, but also some frogs and a caecilian) glowed under the right conditions, even the aquatic larvae. Bold markings and colours, like the orange tummy of fire-belly newts or the yellow blotches of the tiger salamander, glowed bright green or greenish orange. Animals with demure patterns had a more subtle sheen to them. In a few cases, species fluoresced from unusual places: some had glimmering bones and others shone from a coating of glowing mucous.Taking stock of their diverse collection of shimmering specimens, Lamb and Davis reasoned that biofluorescence is widespread in amphibians and probably appeared early in their evolutionary history. The next logical questions are exactly how do salamanders and their relatives glow and why did they do it in the first place?The authors suggest that the amphibians’ light shows might originate from pigments in the skin, such as carotenoids, pterins and structures with guanine crystals, which all fluoresce. Alternatively, the glow could have nothing to do with pigments, relying instead on something like the green fluorescent protein common in jellyfish and molecular biology labs worldwide, or hyloins – compounds produced by the mucous glands of neotropical frogs. Which, if any, of these mechanisms apply to salamanders and their cousins remains uncharted territory.And the team suspects that these amphibians probably use their glowing abilities in much the same way as other biofluorescent animals. Like a neon sign, the green blotches and markings could send messages. Perhaps female salamanders judge their suitors by their ‘glow up’, tolerating only the brightest mates. The fact that some species show strong biofluorescence around the cloacal region, a part of the anatomy that many species investigate during courtship, makes this possibility especially intriguing. Bizarrely, glowing in the dark could also be a form of camouflage, if the patterns mimic those of other fluorescent predators, yet how biofluorescence impacts the daily life of amphibians remains a mystery.As is often the case, good science can lead to more questions than it answers and the results of this study reveal how little we know about the hidden world of amphibians.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.046

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0140.005

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.

Opus teacher head0.021
GPT teacher head0.313
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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