Exploring the potential for Deep Raman Spectroscopy for non-invasive sex determination of chicken eggs
Bibliographic record
Abstract
In order to meet the demand for consumption eggs, billions of specially bred layer chickens are hatched every year. Serving no purpose in the industry, over 372 million one-day old male chickens are culled every year in Europe. Current, accurate (>95%) commercial in-ovo sexing techniques are unfit for sexing before day 9 of incubation (E9) and their invasive nature imposes a risk for bacterial infection. With upcoming new legislation aiming to outlaw the culling of chicken embryos after E7, there is a need for non-invasive early in-ovo chicken sex determination methods. In recent years, fluorescence and Raman spectroscopy were demonstrated as promising techniques for the retrieval of sex-related biomarkers from embryonic blood for early and accurate in-ovo sexing. However, the high optical scattering of the eggshell has proven a yet insurmountable challenge in the application of these techniques in a non-invasive manner.Seeking to overcome this issue, this work assesses the suitability of spatially offset-, transmission and time-resolved Raman Spectroscopy (Deep Raman Spectroscopy, DRS) techniques for the non-invasive retrieval of sex-related biomarkers from extra-embryonic tissues. To estimate the impact of the large sample volume inherent to DRS on the retrieval of key biomarkers, the presence, distribution, and discriminative value of hemoglobin, protoporphyrin IX, and nucleic acids in-vivo were determined in different extra-embryonic blood vessels during early incubation using backscatter Raman microscopy. The weak contributions of these biomarkers highlights the anticipated challenges and limitations of DRS for subsurface analysis in extremely turbid media.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".