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Record W2885495075 · doi:10.1002/9781119391128.ch9

Brain Dead: The Dynamic Neuroendocrinological Adaptations During Hypometabolism in Mammalian Hibernators

2018· other· en· W2885495075 on OpenAlexaff
Samantha M. Logan, Alex J. Watts, Kenneth B. Storey

Bibliographic record

Venuenot available
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsCarleton University
Fundersnot available
KeywordsHibernation (computing)BiologyNeuroscienceThermoregulationHypothalamusCentral nervous systemEcologyState (computer science)

Abstract

fetched live from OpenAlex

Some mammals that are faced with severe climates or reduced food availability may overcome these challenges by making use of hibernation – a state coordinated by the central nervous system in response to normally lethal environmental challenges to the animal. Hibernators can survive extended periods of unfavorable conditions while maintaining lowered levels of essential cellular processes (i.e., transcription/translation), by significantly reducing energy use in the brain as well as most peripheral organs. A requirement for significantly lower energy use during hibernation is remarkable phenotypic plasticity within several physiological processes including endocrine activity, metabolic activity, thermoregulation and rhythmic behaviors and mechanisms. Of major importance during hibernation is the hypothalamus, which, in cooperation with major neuroendocrine axes, regulates almost every aspect of hibernation including robust monitoring of metabolic needs or seasonal photoperiod and temperature rhythms. Information encoded by endocrine signals is integrated with incoming information to the hypothalamus to control the timing of euthermic arousals or re-entry into a heterothermic state. To survive the drastic changes imposed upon itself, a hibernator similarly requires mechanisms for its survival upon hibernation cessation, namely through defense of its neurons against neurotoxic and degenerative signals. The following chapter describes the plethora of changes within the mentioned systems and pathways, that are either required for entry into, or for the continuation of hibernation. Exploring differences between hibernating and euthermic organisms, and comparisons of mechanisms that allow hibernation in different species, broadens researchers' understanding of the organization and regulation of mammalian nervous systems, and may prove fruitful for further discoveries about the extent of mammalian capabilities.

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)0.000
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.016
GPT teacher head0.225
Teacher spread0.209 · 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

Citations1
Published2018
Admission routes1
Has abstractyes

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