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Record W4402541832 · doi:10.1093/jas/skae234.017

422 The use of 3D kinematics for improved gait assessment in broilers

2024· article· en· W4402541832 on OpenAlexaff
C.J. Bench, Emily Lowen, Nicole Desrosiers, D.R. Korver

Bibliographic record

VenueJournal of Animal Science · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsKinematicsGaitPhysical medicine and rehabilitationMedicinePhysics

Abstract

fetched live from OpenAlex

Abstract Genetic selection for high growth rates in broiler chickens has resulted in larger broilers and changes in broiler gait. Traditionally, broiler gait is assessed using visual observation which is subjective and labor intensive. In contrast, newer technologies are available which can be adapted to objectively score changes in broiler gait, thereby, increasing the validity and reliability of gait scoring with less labor required. The objective of this study was to compare the use of 3D kinematics versus visual scoring to assess broiler gait. Second, to determine how strain and age affect 3D kinematic gait metrics in broilers. Four strains of broiler chickens were used; two heritage strains: 1957 (H57; n = 52) and 1977 (H77; n = 51); and two commercially available strains: Cobb (n = 50) and Ross (n = 53). All broilers were weighed weekly throughout the study. Kinematic markers were placed along the spine and bony landmarks of each leg. Commercial strains had kinematic recordings and body weights (BW) recorded every week for 4 wk while Heritage strains had measurements recorded every week for 6 wk. Visual gait scoring was completed using a 5-point scale each week by two trained observers. Data were analyzed using the correlation procedure in SAS to determine the relationship between visual gait score and kinematic metrics as well as the GLIMMIX procedure to determine the effects of strain, age, and strain x age on all gait parameters. There was a weak positive correlation (r < 0.2) between visual gait score and all kinematic metrics. Cobb were the heaviest strain (1,292.6 ± 14.71 g P < 0.001) while H57 broilers were the lightest (326.4 ± 9.99 g P < 0.001). Each week, the BW of all strains increased significantly. Total Stride Time differed significantly by strain (P < 0.001; Cobb: 26.3 ± 0.47 cs, Ross: 23.1 ± 0.39 cs, H77: 23.7 ± 0.28 cs, H57: 21.5 ± 0.26 cs) and Age (P < 0.01; Commercial: wk 3: 21.8 ± 0.54 cs, wk 6: 28.1 ± 0.68 cs and Heritage: wk 3: 19.7 ± 0.42 cs, wk 8: 25.4 ± 0.52 cs). Total stride length (SL) also significantly differed by strain. Cobb had the longest SL of the two commercial strains (P < 0.001; 88.6 ± 1.94 mm) while H77 had the longest stride of the heritage strains (97.9 ± 1.51 mm). Total SL also increased with age resulting in longer stride times. Heavier birds, such as Cobb, had longer stride Times and SL compared with lighter birds such as the H57 strain. In conclusion, visual gait score and kinematic metrics were not found to be measuring the same things. However, kinematics can be a used as an informative and objective tool for assessing gait metrics associated with broiler lameness such as changes in stride length and stride time.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.316
Teacher spread0.240 · 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".

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Citations1
Published2024
Admission routes1
Has abstractyes

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