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Record W3093764953 · doi:10.1016/j.isci.2020.101574

Aerodynamics Show Membrane-Winged Theropods Were a Poor Gliding Dead-end

2020· article· en· W3093764953 on OpenAlexaff
T. Alexander Dececchi, Arindam Roy, Michael Pittman, Thomas G. Kaye, Xing Xu, Michael Habib, Hans C. E. Larsson, Xiaoli Wang, Xiaoting Zheng

Bibliographic record

VenueiScience · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsMcGill University
FundersUniversity of Hong KongResearch Grants Council, University Grants CommitteeUniversity of Pittsburgh at Johnstown
KeywordsAerodynamicsDead endAstrobiologyBiologyEngineeringAerospace engineeringPhysicsMechanicsFlow (mathematics)

Abstract

fetched live from OpenAlex

The bizarre scansoriopterygid theropods Yi and Ambopteryx had skin stretched between elongate fingers that form a potential membranous wing. This wing is thought to have been used in aerial locomotion, but this has never been tested. Using laser-stimulated fluorescence imaging, we re-evaluate their anatomy and perform aerodynamic calculations covering flight potential, other wing-based behaviors, and gliding capabilities. We find that Yi and Ambopteryx were likely arboreal, highly unlikely to have any form of powered flight, and had significant deficiencies in flapping-based locomotion and limited gliding abilities. Our results show that Scansoriopterygidae are not models for the early evolution of bird flight, and their structurally distinct wings differed greatly from contemporaneous paravians, supporting multiple independent origins of flight. We propose that Scansoriopterygidae represents a unique but failed flight architecture of non-avialan theropods and that the evolutionary race to capture vertebrate aerial morphospace in the Middle to Late Jurassic was dynamic and complex.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

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)0.0030.001

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 head0.025
GPT teacher head0.227
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 designSimulation or modeling
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

Citations23
Published2020
Admission routes1
Has abstractyes

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