Exploring the prospects of the <i>fifth quarter</i> in the 21st century
Bibliographic record
Abstract
A variable proportion of slaughtered livestock, generally referred to as the fifth quarter, is not part of the edible dressed meat and regarded as animal byproduct. In order for the fifth quarter to play a significant role in the current effort toward a circular bio-based economy, it has to successfully support food security, social inclusivity, environmental sustainability, and a viable economy. The high volume of these low-value streams and their nutrient-dense nature can facilitate their position as a very important candidate to explore within the context of a circular bio-based economy to achieve some of the United Nations Sustainable Development Goals (UN-SDGs). While these sources have been traditionally used for various applications across different cultures and industries, it seems evident that their full potential has not yet been exploited, leaving these products more like an environmental burden rather than valuable resources. With innovation and well-targeted interdisciplinary collaborations, the potential of the fifth quarter can be fully realized. The present review intends to explore these low-value streams, their current utilization, and their potential to tackle the global challenges of increasing protein demands while preventing environmental degradation. Factors that limit widespread applications of the fifth quarter across industries and cultures will also be discussed.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".