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Record W2313665502 · doi:10.1242/jeb.092007

ALASKAN WOOD FROGS STOCK UP ON SOLUTES TO SURVIVE

2013· article· en· W2313665502 on OpenAlexaboutno aff
Nicola Stead

Bibliographic record

VenueJournal of Experimental Biology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsnot available
Fundersnot available
KeywordsPopulationEcologyBiologyDemography

Abstract

fetched live from OpenAlex

Outwardly, the tiny wood frog, Rana sylvatica, does not look like your regular arctic inhabitant. Yet despite their tiny stature, these little frogs are actually quite hardy and can tolerate freezing of up to two-thirds of their body water. For 25 years, Jon Costanzo and Richard Lee, Jr, both from Miami University, USA, have been studying these wood frogs, which are native to Alaska, Canada and the northern USA, to unravel their secrets. Costanzo explains that one well-known technique in the Ohioan population is to stock up on cryoprotectant solutes: ‘When you load your blood with solutes, whether it's glucose, urea, glycerol or whatever, you drive down the [blood's] freezing point. Therefore, at any given temperature, you reduce the amount of ice that forms.’ So, even when temperatures drop to −5°C in Ohio, some water remains unfrozen. In Alaska, however, temperatures go down to −30°C during winter and so Costanzo and Lee wondered whether Alaskan frogs have some additional tricks to aid their survival (p. 3461).To collect the frogs, Costanzo sent two of his graduate students, Clara do Amaral and Andrew Rosendale, out into the Alaskan wilderness during early August. After capturing their amphibian subjects, the duo then made a long 6-hour drive to Anchorage, Alaska, to ship them back to the lab in Ohio. Upon their return the team then set about preparing the frogs for winter; they mimicked the Alaskan fall by gradually shortening day length and lowering the temperature. Once acclimatized, the team then kept the frogs in simulated hibernation in the dark at 4°C for 8 weeks, alongside some hibernating Ohioan frogs.To begin, the team first tested the Alaskan frogs' freeze tolerance and endurance; some were lowered to an icy −16°C over 2 weeks, while others were kept at chilly −4°C for up to 12 weeks. In both cases, the frogs recovered remarkably quickly, but, as Costanzo recalls: ‘If you take the Ohio frogs and freeze them to −4 or −5°C it would take them days to fully revive. These Alaskan frogs were back on their feet within 2 days.’So, how exactly were the Alaskan frogs able to prevent total body freezing at temperatures that would freeze stiff an Ohioan frog? To find out, the team turned to frogs that they had euthanized at each stage (after the natural summer, and the mimicked fall and winter periods). The team were struck by how large the livers in the Alaskan fall and winter frogs were – they accounted for 22% of the total body mass (compared with just 8% in Ohioan frogs). To fuel such a huge change, the team thinks the Alaskan frogs trim down and use their body fat and some of their muscle protein to make glycogen – an essential precursor of the cryoprotectant solute glucose that is mobilised when the tissues begin to freeze.The team also found that levels of another cryoprotectant solute, urea, had also shot up by 10-fold. This huge surge in urea concentration, along with a modest increase in glucose levels, caused an increase in plasma osmolality (a measurement of solute amount in liquids) by 100 mOsmol kg−1. However, the team saw that total plasma osmolality had increased by 173 mOsmol kg−1. What solute accounts for this additional 73 mOsmol kg−1? The team have ruled out a few contenders, but they don't know exactly what it is yet. One thing's for certain, it's not a solute found in the Ohioan population. So it seems the Alaskan frogs stockpile the same solutes as their southern relatives (albeit to greater levels), but they also have some unique tricks of their own.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.004

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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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 designBench or experimental
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".

Quick stats

Citations0
Published2013
Admission routes1
Has abstractyes

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