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Record W6967277490 · doi:10.5061/dryad.gt5c0

Data from: Novel energy-saving strategies to multiple stressors in birds: the ultradian regulation of body temperature

2016· dataset· en· W6967277490 on OpenAlexaff

Bibliographic record

VenueData Archiving and Networked Services (DANS) · 2016
Typedataset
Languageen
Field
Topic
Canadian institutionsBrock University
Fundersnot available
KeywordsEndothermThermoregulationHomeothermyUltradian rhythmStressorHuman body temperature

Abstract

fetched live from OpenAlex

This study aimed to examine thermoregulatory responses in birds facing two commonly experienced stressors, cold and fasting. Logging devices allowing long-term and precise access to internal body temperature were placed within the gizzards of ducklings acclimated to cold (CA) (5°C) or thermoneutrality (TN) (25°C). The animals were then examined under three equal 4-day periods: ad libitum feeding, fasting and re-feeding. Through the analysis of daily as well as short-term, or ultradian, variations of body temperature, we showed that while ducklings at TN show only a modest decline in daily thermoregulatory parameters when fasted, they exhibit reduced surface temperatures from key sites of vascular heat exchange during fasting. The CA birds, on the other hand, significantly reduced their short-term variations of body temperature while increasing long-term variability when fasting. This phenomenon would allow the CA birds to reduce the energetic cost of body temperature maintenance under fasting. By analysing ultradian regulation of body temperature, we describe a means by which an endotherm appears to lower thermoregulatory costs in response to the combined stressors of cold and fasting.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0150.014

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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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 designNot applicable
Domainnot available
GenreDataset

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
Published2016
Admission routes1
Has abstractyes

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