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Record W2043542306 · doi:10.1086/318100

Costs of Locomotion and Vertic Dynamics of Cephalopods and Fish

2000· review· en· W2043542306 on OpenAlexafffund
Dale M. Webber, Jill P. Aitken, R. K. O’Dor

Bibliographic record

VenuePhysiological and Biochemical Zoology · 2000
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyPelagic zoneMesopelagic zoneForagingEcologyBuoyancyMarine invertebratesMarine ecosystemPlanktonEcosystem

Abstract

fetched live from OpenAlex

The world's oceans are three‐dimensional habitats that support high diversity and biomass. Because the densities of most of the constituents of life are greater than that of seawater, planktonic and pelagic organisms had to evolve a host of mechanisms to occupy the third dimension. Some microscopic organisms survive at the surface by dividing rapidly in vertically well mixed zones, but most organisms, small and large, have antisinking strategies and structures that maintain vertical position and mobility. All of these mechanisms have energetic costs, ranging from the “foregone metabolic benefits” and increased drag of storing high‐energy, low‐density lipids to direct energy consumption for dynamic lift. Defining the niches in the mesopelagic zone, understanding evolution, and applying such ecological concepts as optimal foraging require good estimates of these costs. The extreme cases above are reasonably well quantified in fishes, but the energetic costs of dynamic physiological mechanisms like swim bladders are not; nor are the costs of maintaining vertical position for the chief invertebrate competitors, the cephalopods. This article evaluates a matrix of buoyancy mechanisms in different circumstances, including vacuum systems and ammonium storage, based on new data on the metabolic cost of creating buoyancy in Sepia officinalis.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.989
Threshold uncertainty score0.505

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.023
GPT teacher head0.256
Teacher spread0.233 · 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 designOther design
Domainnot available
GenreReview

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

Citations49
Published2000
Admission routes2
Has abstractyes

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