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Record W4385275423 · doi:10.1016/j.psj.2023.102966

Research Note: Validation of a low-cost accelerometer to measure physical activity in 30 to 32-d-old male Ross 708 broilers

2023· article· en· W4385275423 on OpenAlexafffund
Carolin Adler, Dilshaan Duhra, T. Shynkaruk, K. Schwean-Lardner

Bibliographic record

VenuePoultry Science · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Saskatchewan
KeywordsMeasure (data warehouse)AccelerometerAnimal sciencePhysical activityMathematicsMedicinePhysical therapyBiologyComputer scienceData mining

Abstract

fetched live from OpenAlex

Poultry activity measurements are often associated with expensive equipment or time-consuming behavior observations. Since low-cost accelerometers are available, the current study validated the FitBark (FitBark 2, FitBark Inc., Kansas City, MO) accelerometer for use on 30 to 32-d-old male Ross 708 broilers. The FitBark provides aggregated activity levels based on tri-axial accelerometer technology. Broilers were housed in 5 rooms, each divided into 12 2 × 2.3 m pens (60 birds per pen, 31 kg m−2 final density). From 30 to 32 d, one broiler per room (n=5) was randomly selected and equipped with a 13 g FitBark. Elastic loops were placed around the wings to secure the FitBark medially on the back. During the same time, validity was assessed via ceiling-mounted video cameras. The video recordings were analyzed using 20-min continuous sampling during the photo phase at 8 time periods per bird. Behavior was assessed every second using an ethogram (9,600 data points per bird). In the first step, the FitBark data were matched and correlated with the corresponding video-based observed activity (OA) data. The FitBark and OA data were not normally distributed (1-sample KS test, all n=800, ZFitBark=0.21, ZOA=0.24, all P<0.001). Therefore, data were transformed, and a repeated measures correlation was performed for each bird, showing a positive correlation between the FitBark and OA data (rrm=0.76, 95% CI=0.72-0.78, df=794, P<0.001). In the second step, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were calculated. The FitBark correctly identified 91% (sensitivity) of the active and 74% (specificity) of the inactive birds. When the FitBark detected an active or inactive bird, there was a probability of 89% (PPV) and 78% (NPV) that the bird was observed to be active or inactive based on the OA data. Accuracy was at 86%. Overall, FitBark are useful for 1-min interval activity measurements in 30 to 32-d-old male Ross 708 broilers. Further research should focus on validating the FitBark at other ages and in different poultry species.

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)0.003
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.148
GPT teacher head0.380
Teacher spread0.232 · 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 designBench or experimental
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

Citations9
Published2023
Admission routes2
Has abstractyes

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