The use of <sup>15</sup>N-enriched feed to label pig excreta for N cycling studies
Bibliographic record
Abstract
Isotopic labelling can help improve our knowledge of the fate of manure N in agroecosystems. Our objective was to investigate the labelling dynamics of excreta N by feeding a pig with a 15N-enriched diet (2.808 atom % 15N) and to establish the implications of using the labelled excreta for N cycling studies. Pig urine and feces were collected and pooled each day for 20 d following the start of 15N-feeding. Each of the 20 excreta samples were analyzed for pH, dry matter content, C and N contents, and 15N distribution between the mineral and organic N pools. Sub-samples of each excreta sample were incubated for 84 d, and the 15N abundance of N mineralized after 7, 21 and 84 d of incubation was determined. The 15N concentration in pig excreta increased sharply during the first 3 d of 15N-feeding and slowly thereafter. The 15N concentration in excreta decreased rapidly when an unlabelled feed was served after 12 d of 15N-feeding. On the first day and after 9 d of 15N-feeding, the mineral and the organic N pools of the collected excreta had similar 15N content. However, from day 2 to 9 of 15N-feeding, the 15N abundance of excreta mineral N was 0.1 to 0.3 atom % lower than in the organic N pool. During incubation of the excreta samples, the 15N content of the mineralized N was 0.1 to 0.4 atom % lower after 84 d than after 21 d of incubation, indicating a heterogeneous distribution of 15N between the rapidly and the slowly mineralizable N pools of pig excreta. Despite some heterogeneity, the measured differences in 15N enrichment among the various excreta N pools were generally less than 15% for the first 9 d of 15N-feeding, and less than 5% afterwards. The labelled excreta were thus considered appropriate for short-term studies on the fate of manure N in the soil-plant system, especially for excreta collected after 9 d of 15N-feeding. Key words: 15N labelling, animal feeding, swine manure, pig slurry
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".