Response of growth performance, blood hematology, organ indexes, and myofiber traits to increasing dietary methionine levels in Jilin White goose
Bibliographic record
Abstract
A total of 240 geese (28 days old; 120 ganders and 120 gooses) with an average initial body weight of 1068.19 ± 6.59 g were used to evaluate the effects of increasing dietary methionine (Met) levels on growth performance, blood hematology, organ indexes, and myofiber traits. The experimental period was 42 days. All birds were randomly assigned to four treatment groups based on the initial body weight. There were six replicate cages per treatment, and 10 geese per cage (5 ganders and 5 gooses). Dietary treatments were based on a basal diet containing 0.25% Met, and extra supplied 0.25%, 0.50%, and 0.75% Met to form different dietary groups (0.25%, 0.50%, 0.75%, and 1.00% Met, as-fed basis). The results of this study indicated that final body weight, body weight gain, and feed efficiency increased quadratically, relative weight of breast muscle and myofiber diameter increased cubically, serum total protein and uric acid concentrations, relative weight of liver and abdominal fat, and myofiber diameter increased linearly, whereas myofiber density decreased linearly, with the level of Met increased. The maximized growth performance and breast muscle parameters were observed in 0.75% Met-containing group.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".