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Record W2783219605 · doi:10.1098/rspb.2017.2299

The evolution of tail weaponization in amniotes

2018· article· en· W2783219605 on OpenAlexaff
Victoria M. Arbour, Lindsay E. Zanno

Bibliographic record

VenueProceedings of the Royal Society B Biological Sciences · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
FundersJurassic FoundationField MuseumAmerican Museum of Natural History
KeywordsBiologyExtant taxonIntraspecific competitionHerbivoreEvolutionary biologyFaunaPhylogenetic treeEcologyVertebrateTree of life (biology)CladeZoology

Abstract

fetched live from OpenAlex

Weaponry, for the purpose of intraspecific combat or predator defence, is one of the most widespread animal adaptations, yet the selective pressures and constraints governing its phenotypic diversity and skeletal regionalization are not well understood. Here, we investigate the evolution of tail weaponry in amniotes, a rare form of weaponry that nonetheless evolved independently among a broad spectrum of life including mammals, turtles and dinosaurs. Using phylogenetic comparative methods, we test for links between morphology, ecology and behaviour in extant amniotes known to use the tail as a weapon, and in extinct taxa bearing osseous tail armaments. We find robust ecological and morphological correlates of both tail lashing behaviour and bony tail weaponry, including large body size, body armour and herbivory, suggesting these life-history parameters factor into the evolution of antipredator behaviours and tail armaments. We suggest that the evolution of tail weaponry is rare because large, armoured herbivores are uncommon in extant terrestrial faunas, as they have been throughout evolutionary history.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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

Citations36
Published2018
Admission routes1
Has abstractyes

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