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Record W2999254569 · doi:10.1242/jeb.211375

Electric fish turn down the power

2020· article· en· W2999254569 on OpenAlexaffabout
Andy J. Turko

Bibliographic record

VenueJournal of Experimental Biology · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsMcMaster UniversityUniversity of Windsor
Fundersnot available
KeywordsElectricityOxygenElectric fishFish <Actinopterygii>Electric powerEnvironmental scienceElectricity generationEnergy budgetPower (physics)FisheryEcologyBiologyChemistryElectrical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Animals have all sorts of neat tricks to find their way in the dark. Bats have sonar, ants follow their noses, and some fishes surround themselves with electric fields that can detect nearby objects. Electrical navigation is wonderful for fishes that live in murky water, but it comes at a cost – electricity is expensive to generate and can represent almost a third of the overall energy budget in these fishes. When conditions are good, paying this cost doesn't seem to be a problem. But electrical sensing is most useful in murky and stagnant habitats that also tend to be low in oxygen. Oxygen is critical for fuelling metabolic energy production and survival in oxygen-limited environments often, therefore, depends on the ability of animals to reduce their metabolic rates. How do electric fishes balance this energetic budget and deal with the expense of electricity generation in the face of severe oxygen austerity?A new study, led by Shelby Clarke at McGill University, Canada, has unravelled the details of this trade-off by studying the electric fish Petrocephalus degeni. The authors captured wild fish from a low-oxygen Ugandan swamp and brought them into a lakeside laboratory, where they measured metabolic rate and electricity production first under conditions of abundant oxygen and then after the fish were challenged with low-oxygen conditions.As oxygen levels decreased in the experimental chamber, electricity production initially remained steady. However, under more severe conditions – when about 80% of the oxygen was gone – electrical activity began to decrease. The energy saved from minimizing electrical output could then be allocated to other vital processes, allowing the fish to continue to obtain enough oxygen to maintain normal metabolism until almost 90% of the oxygen was gone from the water. Amazingly, even below this critical point where the fish could not breathe as much oxygen as they required, electrical production did not cease despite its high energetic cost. Low levels of electricity persisted, perhaps representing a desperate attempt to find an escape route.If electrical activity is constrained by oxygen supply, the authors reasoned the electric fish should get even more electric if oxygen is abundant. To test this idea, Clarke moved electric fish from their typical low-oxygen swampy habitat to a life of luxury in well-aerated aquariums. After several weeks in this housing arrangement, electrical production was indeed higher than in fish from the harsh wild conditions. However, the ability of these pampered fish to tolerate low-oxygen conditions was diminished. The authors conclude that when these electric fish have easy access to oxygen, they spend less energy on the organs used to acquire more of the gas, such as the heart, gills or blood. Instead, the energy is allocated to increased electrical capacity that presumably improves their ability to perceive their physical environment.Like any utility company, electric fish must continually evaluate the budgetary landscape when deciding how much to invest in electricity production. And, while the mechanistic details of electrical output regulation remain to be discovered, it is clear that these fish have an impressive ability to re-organize their power system over both the short and long term, allowing them to cope with whatever conditions nature throws their way.

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: none
Teacher disagreement score0.058
Threshold uncertainty score0.193

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0580.017

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 head0.018
GPT teacher head0.251
Teacher spread0.234 · 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".

Quick stats

Citations0
Published2020
Admission routes2
Has abstractyes

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