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Record W2093114061 · doi:10.1139/z03-041

Salt and water balance of modern baleen whales: rate of urine production and food intake

2003· article· en· W2093114061 on OpenAlexvenueno aff
Matthı́as Kjeld

Bibliographic record

VenueCanadian Journal of Zoology · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsnot available
Fundersnot available
KeywordsBalaenopteraBaleenBiologyKrillCetaceaSeawaterBlubberAnimal sciencePelagic zoneWhaleFisheryOceanographyEcology

Abstract

fetched live from OpenAlex

Whales, as pelagic marine mammals, are thought to have evolved from fresh-water-dependent terrestrial mammals. Baleen whales feed primarily on salty euphausiids (krill) and have no access to fresh water. How have these mammals adapted to lifelong habitation in a hyperosmotic medium? A new approach is proposed for studying this by using allometry (scaling) of endogenous creatinine clearance in mammals together with determinations of creatinine concentration in fresh postmortem blood and urine of fin whales (Balaenoptera physalus) and sei whales (Balaenoptera borealis). From the predicted mean creatinine-clearance values and the measured mean creatinine concentrations, a urine-production rates of 974 and 627 L/day for the fin and sei whales, respectively, were computed. Average daily krill ingestion of about 1300 and 835 L is predicted for the fin and sei whales, respectively. The whales seem to ingest about 30% more than earlier reported of a prey, which has about 50% of the salt concentration of seawater, thus maintaining the salt and water balance with a minimum of 1–2% seawater ingestion. The method used to estimate the above volumes could be a valuable tool in further studies of the water and salt balance of the large baleen whales, which may not have the same osmoregulatory control mechanisms as the smaller Odontoceti.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.192
Teacher spread0.180 · 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

Citations21
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Zoology→Same topicMarine animal studies overview→French-language works237,207→