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Record W3048965538 · doi:10.1098/rsif.2020.0301

Swimming and defence: competing needs across ontogeny in armoured fishes (Agonidae)

2020· article· en· W3048965538 on OpenAlexaff
Matthew A. Kolmann, Taiã Peixoto, Janne A. Pfeiffenberger, Adam P. Summers, Cassandra M. Donatelli

Bibliographic record

VenueJournal of The Royal Society Interface · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFish Biology and Ecology Studies
Canadian institutionsUniversity of Ottawa
FundersDivision of Biological Infrastructure
KeywordsArmourBiologyJuvenileOntogenyEcology

Abstract

fetched live from OpenAlex

Biological armours are potent model systems for understanding the complex series of competing demands on protective exoskeletons; after all, armoured organisms are the product of millions of years of refined engineering under the harshest conditions. Fishes are no strangers to armour, with various types of armour plating common to the 400–500 Myr of evolution in both jawed and jawless fishes. Here, we focus on the poachers (Agonidae), a family of armoured fishes native to temperate waters of the Pacific rim. We examined armour morphology, body stiffness and swimming performance in the northern spearnose poacher ( Agonopsis vulsa ) over ontogeny. As juveniles, these fishes make frequent nocturnal forays into the water column in search of food, while heavily armoured adults are bound to the benthos. Most armour dimensions and density increase with body length, as does body stiffness. Juvenile poachers have enlarged spines on their armour whereas adults invest more mineral in armour plate bases. Adults are stiffer and accelerate faster than juveniles with an anguilliform swimming mode. Subadults more closely approximate adults more than smaller juveniles, with regards to both swimming and armour mechanics. Poacher armour serves multiple functions over ontogeny, from facilitating locomotion, slowing sinking and providing defence.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.019
GPT teacher head0.230
Teacher spread0.211 · 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 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

Citations22
Published2020
Admission routes1
Has abstractyes

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