Plasma <sup>1</sup>H-nuclear magnetic resonance metabolomics reveals avian responses to long-term seaweed supplementation in two genetic lines of <i>Gallus gallus domesticus</i>
Bibliographic record
Abstract
Blood metabolic profile may be useful for assessing bird responses and identifying key biomarkers and pathways linked to host health and production. We used a prebiotic supplement and heat stress in Lohmann LSL-Lite (White) and Lohmann Brown-Lite (Brown) laying hens to investigate the utility of plasma metabolome in understanding chicken physiological responses. In a short-term trial, red seaweed Chondrus crispus (CC) at 3% was offered to 100 laying hens of the two genetic lines for 21 days. In a long-term trial, 240 hens of the same two strains were given 0%, 3% CC, or 0.5% brown seaweed Ascophyllum nodosum for 41 weeks, then assigned to a control or heat-stress period for 4 weeks. Seaweed supplementation induced the greatest metabolite changes, affecting 50 of 57 compounds, while genetic strain induced the least metabolic change (30 compounds, p < 0.05). Heat stress affected 40 metabolites ( p < 0.05). The short-term inclusion of CC impacted more pathways than the long-term (nine versus four). Heat stress and genetic strain were associated with four and two metabolic pathways, respectively. These results suggested that metabolomics could be used in poultry to identify pathways that explain the effects of genetic and environmental factors linked to chicken health and production.
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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) | 0.001 | 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".