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Record W2094735776 · doi:10.1139/z00-112

Bipedal locomotion in birds: the importance of functional parameters in terrestrial adaptation in Anatidae

2000· article· en· W2094735776 on OpenAlexvenueno aff
Anick Abourachid

Bibliographic record

VenueCanadian Journal of Zoology · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsnot available
Fundersnot available
KeywordsCursorialBiologyTerrestrial locomotionAnasKinematicsAdaptation (eye)AnatidaeEcologyZoologyPredation

Abstract

fetched live from OpenAlex

The kinematic characteristics of a bird's walk vary according to whether the species is cursorial or not. To increase their speed, running birds increase the frequency of their movements, whereas non-running birds preferentially increase the amplitude. Previous studies have shown that these differences are accompanied by differences in posture; however, the observations were carried out on different species. Do these differences correspond to morphological differences linked to the history of the particular species, or do they reflect more effective solutions from a mechanical point of view? Two breeds of the duck Anas platyrhynchos platyrhynchos, the mallard and the Indian runner, which have different locomotor behaviours, are compared. The mallard is a dabbling duck with the typical horizontal duck posture, while the Indian runner is a terrestrial duck that carries its trunk very erect. The kinematic characteristics of the walks of both breeds were studied. The observed differences in posture between the mallard and the Indian runner have repercussions in the kinematic features of locomotion. The strategies used to increase speed differ in the two breeds: the mallard increases the amplitude of its movements, like other non-running birds, while the Indian runner increases the frequency of its movements, as cursorial birds do. Thus, behavioural adaptation to terrestrial locomotion is associated with functional adaptation (upright posture) that permits a more effective mechanical solution without requiring obvious morphological modifications.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.205
Teacher spread0.180 · 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

Citations32
Published2000
Admission routes1
Has abstractyes

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