Characterization of hatchery residues for on farm implementation of circular waste management practices
Why this work is in the frame
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Bibliographic record
Abstract
The conventional management of hatchery residues is associated with greenhouse gas and unpleasant odor emissions, the presence of pathogens and high disposal costs for producers. To address these issues, on-farm alternatives like composting, fermentation, and insect valorization are promising approaches. This study aims to characterize hatchery residues and define critical quality thresholds to identify effective processes for their management. Hatchery residue samples were collected bi-monthly over a year (N = 24) and were analyzed for proximate composition (dry matter, ash, energy, crude protein, crude lipid, crude fiber, carbohydrates), pH, color (L*a*b*, Chroma) and microbiological loads (total aerobic mesophilic counts, coliforms, lactic acid bacteria). Volatile fatty acid composition was also measured (N = 8). Significant correlation coefficients were found between TAM and LAB loads and residue characterization (pH, chroma, crude fibers, carbohydrates, and temperature). On a dry matter basis, residues were high in energy (2498 to 5911 cal/g), proteins (21.3 to 49.4 %) and lipids (14.6 to 29.1 %), but low in carbohydrates (0 to 15.3 %) despite temporal fluctuations. Ash content varied widely (8.6 to 49.1 %, dry matter) and is influenced by eggshell content. Microbiological loads were high for total aerobic mesophilic bacteria (6.5 to 9.1 log cfu/g), coliforms (5.4 to 8.5 log cfu/g) and lactic acid bacteria (6.7 to 9.0 log cfu/g). Valorization of hatchery residues on the farm will depends on the optimization of effective upstream stabilization processes. The critical points are discussed according to the valorization potentials that could be implemented on the farm from composting to upcycling by insects.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 it