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Record W4381161835 · doi:10.1093/icb/icad076

It Pays to Be Bumpy: Drag Reducing Armor in the Pacific Spiny Lumpsucker, <i>Eumicrotremus orbis</i>

2023· article· en· W4381161835 on OpenAlexfundno aff
Richard C. Hoover, Olivia H. Hawkins, Jack Rosen, Conrad D Wilson, Callie H. Crawford, Meghan M. Holst, Jonathan M. Huie, Adam P. Summers, Cassandra M. Donatelli, Karly E. Cohen

Bibliographic record

VenueIntegrative and Comparative Biology · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCephalopods and Marine Biology
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Louisiana at LafayetteNational Science Foundation
KeywordsArmourDragBiologyFisheryMarine engineeringMechanicsPhysicsMaterials scienceEngineeringNanotechnology

Abstract

fetched live from OpenAlex

Armor is a multipurpose set of structures that has evolved independently at least 30 times in fishes. In addition to providing protection, armor can manipulate flow, increase camouflage, and be sexually dimorphic. There are potential tradeoffs in armor function: increased impact resistance may come at the cost of maneuvering ability; and ornate armor may offer visual or protective advantages, but could incur excess drag. Pacific spiny lumpsuckers (Eumicrotremus orbis) are covered in rows of odontic, cone-shaped armor whorls, protecting the fish from wave driven impacts and the threat of predation. We are interested in measuring the effects of lumpsucker armor on the hydrodynamic forces on the fish. Bigger lumpsuckers have larger and more complex armor, which may incur a greater hydrodynamic cost. In addition to their protective armor, lumpsuckers have evolved a ventral adhesive disc, allowing them to remain stationary in their environment. We hypothesize a tradeoff between the armor and adhesion: little fish prioritize suction, while big fish prioritize protection. Using micro-CT, we compared armor volume to disc area over lumpsucker development and built 3D models to measure changes in drag over ontogeny. We found that drag and drag coefficients decrease with greater armor coverage and vary consistently with orientation. Adhesive disc area is isometric but safety factor increases with size, allowing larger fish to remain attached in higher flows than smaller fish.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.730
Threshold uncertainty score0.555

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 head0.083
GPT teacher head0.311
Teacher spread0.228 · 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 designNot applicable
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
Published2023
Admission routes1
Has abstractyes

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