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Postprandial thermogenesis in Bothrops moojeni (Serpentes: Viperidae)

2011· article· en· W1772855314 on OpenAlexaff
DR Stuginski, Wilson Roberto Fernandes, Glenn J. Tattersall, AS Abe

Bibliographic record

Venue˜The œJournal of venomous animals and toxins including tropical diseases · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicAmphibian and Reptile Biology
Canadian institutionsInstitute for Biological Sciences
FundersFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPostprandialSpecific dynamic actionThermogenesisDigestion (alchemy)BiologyViperidaeMealMetabolic rateAnimal scienceThermoregulationPredationFood scienceChemistryEcologyEndocrinologyObesity

Abstract

fetched live from OpenAlex

Snakes that can ingest prey that are proportionally large have high metabolic rates during digestion. This great increase in metabolic rate (specific dynamic action - SDA) may create a significant augment in the animal's body temperature. The present study investigated postprandial thermogenesis in Bothrops moojeni. Briefly, two groups of snakes were fed meals equivalent to 17 ± 3% and 32 ± 5% of their body weight and were observed for 72 hours, in which thermal images of each snake were taken with an infrared camera in a thermostable environment with a constant air temperature of 30°C. The results showed a significant increase in snake surface temperature, with a thermal peak between 33 and 36 hours after feeding. The meal size had a great impact on the intensity and duration of the thermogenic response. Such increase in temperature appears to be connected with the huge increase in metabolic rates during digestion of relatively large prey by snakes that feed infrequently. The ecologic implication of the thermogenic response is still not well understood; however, it is possible that its presence could affect behaviors associated with the snake digestion, such as postprandial thermophily.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.027
GPT teacher head0.243
Teacher spread0.215 · 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.

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

Citations6
Published2011
Admission routes1
Has abstractyes

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