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
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.051 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".