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Record W3010449663 · doi:10.3389/fphys.2020.00142

Comparative Respiratory Physiology in Cetaceans

2020· article· en· W3010449663 on OpenAlexaff
Andreas Fahlman, Alicia Borque‐Espinosa, Federico Facchin, Diana Ferrero Fernandez, Paola Muñoz Caballero, Martin Haulena, Julie Rocho‐Levine

Bibliographic record

VenueFrontiers in Physiology · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience of respiration and sleep
Canadian institutionsVancouver Aquarium
FundersOffice of Naval Research
KeywordsComparative physiologyRespiratory physiologyRespiratory systemPhysiologyBiologyZoologyComputational biologyAnatomy

Abstract

fetched live from OpenAlex

In the current study we used breath-by-breath respirometry to evaluate respiratory physiology under voluntary control in a male beluga calf (Delphinapterus leucas, body mass range [Mb]: 151-175 kg), an adult female (estimated Mb = 500-550kg) and a juvenile male (Mb = 279kg) false killer whale (Pseudorca crassidens) housed in managed care. Our results suggest that the measured breathing frequency (fR) is lower, while tidal volume (VT) is significantly greater as compared with allometric predictions from terrestrial mammals. Including previously published data from adult bottlenose dolphin (Tursiops truncatus) beluga, harbour porpoise (Phocoena phocoena), killer whale (Orcinus orca), pilot (Globicephala scammoni), and gray whale (Eschrichtius robustus) show that the allometric mass-exponents for VT and fR are similar to that for terrestrial mammals (VT: 1.00, fR: -0.20). In addition, our results suggest an allometric relationship for respiratory flow Respiratory physiology in cetaceans, with a mass-exponent between 0.63-0.70, and where the expiratory was an average 25% higher as compared with inspiratory flow. These data provide enhanced understanding of the respiratory physiology of cetaceans and are useful to provide proxies of lung function to better understand lung health or physiological limitations.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.294
Threshold uncertainty score0.698

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.067
GPT teacher head0.301
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 teacher head, not a consensus.

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

Citations87
Published2020
Admission routes1
Has abstractyes

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