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Record W3040696035 · doi:10.1242/jeb.207613

Surviving anoxia: the maintenance of energy production and tissue integrity during anoxia and reoxygenation

2020· review· en· W3040696035 on OpenAlexafffund
Georgina Cox, Todd E. Gillis

Bibliographic record

VenueJournal of Experimental Biology · 2020
Typereview
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAnoxic watersHypoxia (environmental)BiologyAnaerobic exerciseEnergy metabolismProduction (economics)OxygenPhysiologyEcologyChemistryEndocrinology

Abstract

fetched live from OpenAlex

ABSTRACT The development of anoxia within tissues represents a significant challenge to most animals because of the decreased capacity for aerobic ATP production, the associated loss of essential cellular functions and the potential for detrimental tissue oxidation upon reoxygenation. Despite these challenges, there are many animals from multiple phyla that routinely experience anoxia and can fully recover. In this Review, we integrate knowledge gained from studies of anoxia-tolerant species across many animal taxa. We primarily focus on strategies used to reduce energy requirements, minimize the consequences of anaerobic ATP production and reduce the adverse effects of reactive oxygen species, which are responsible for tissue damage with reoxygenation. We aim to identify common strategies, as well as novel solutions, to the challenges of anoxia exposure. This Review chronologically examines the challenges faced by animals as they enter anoxia, as they attempt to maintain physiological function during prolonged anoxic exposure and, finally, as they emerge from anoxia. The capacity of animals to survive anoxia is also considered in relation to the increasing prevalence of anoxic zones within marine and freshwater environments, and the need to understand what limits survival.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Review · Consensus signal: Review
Teacher disagreement score0.631
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.296
Teacher spread0.264 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreReview

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

Citations47
Published2020
Admission routes2
Has abstractyes

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