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Record W4229448510 · doi:10.1080/00439339.2022.2026202

A comparison of the various equations published for the estimation of characteristics of hen’s eggs, the importance of reporting the compression rate for shell strength measurements, and distinction between egg specific gravity and density

2022· article· en· W4229448510 on OpenAlex
R.M.G. HAMILTON

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWorld s Poultry Science Journal · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Nutrition and Physiology
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsCompression (physics)MathematicsSpecific gravityShell (structure)Range (aeronautics)Compressive strengthStatisticsVolume (thermodynamics)SphericityMaterials scienceComposite materialPhysicsThermodynamicsGeometry

Abstract

fetched live from OpenAlex

SUMMARYPrediction equations allow the estimation of dependent variable from the value obtained from the measurements of an independent variable. Comparisons of estimates obtained for of 85 equations that were published for the prediction of shell strength parameters were made. Egg weight, specific gravity, length, width and thickness were the independent factors use to estimate surface area (SA), egg volume (EV), shell weight, percent shell, sphericity, thickness, compression and impact fracture strength, and shape index. Values (n = 5–20) from published results were used to create a data set for the testing of these equations.Comparisons, based on coefficient of variation (CV), among the calculated estimates obtained with the majority of the equations (72) showed the variability was small, especially those for SA and EV, However, the CV for other equations (7) showed their estimates varied over wide range; whereas, the estimates for the remainder (6) were outside the expected acceptable range. Ten equations, as published, required an ‘adjustment factor’, either multiplication or division, in order to produce an estimate that was within the expected range.It is essential that the rate of compression used to measure compression fracture strength of egg shell be reported because, since the egg shell is a brittle material, the value obtained when fracture strength is measured by compression is dependent on the compression rate. Without knowing the compression rate, it is not possible to establish whether the difference among published shell strength measurements is actual or due to differences in compression rates. There is a need to clarify that the ‘saline flotation method’ measures the density of the egg, NOT specific gravity. In addition, the use of various abbreviations for the same shell strength variable causes confusion that could be clarified by the development of standardised abbreviations. Finally, more care is needed to ensure the original authors are cited when reporting the sources of prediction equations.Abbreviations: EW: egg weight; SW: shell weight; EV: egg volume; SA: surface area; L: longitudinal length; W: axial width (diameter); SG: specific gravity; STk: shell thickness; sd: standard deviation; obs: observational; CFS: quasi-static compression fracture strength; DFm: non-destructive deformation; SW/SA: shell weight per unit surface area; Dg: geometric diameter; SI: shape index; μm: micro metre; N: Newton; n: number of observations; CV: coefficient of variation

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.096
GPT teacher head0.329
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