Impact of Fermented Liquid Potato Hash Diets on Growth Performance of Grower Pigs
Bibliographic record
Abstract
The study aimed to evaluate growth performance of grower pigs fed fermented liquid potato hash with or without exogenous enzyme. Diets containing either 200 (LFLPH) g/kg potato hash (enzyme treated or not); 400 (HFLPH) g/kg potato hash (enzyme treated or not) or no fermented potato hash (control) were formulated to provide 14 MJ/kg digestible energy (DE), 180 g/kg crude protein (CP) and 11.6 g/kg lysine. Dietary treatments were control (no FLPH), enzyme treated LFPHE and HFLPHE, and untreated LFLPH and HFLPH. The exogenous enzyme was added in treated diets to provide minimum of 560-840 TXU/TDU xylanase per kg feed, at an inclusion rate of 100 g/tonne. Diets were mixed with water at 1:2 ration and fermented for 8 hours at 25 °C room temperature before being fed to pigs. Back-slopping fermentation approach was followed to prepare diets. Twelve pigs (25±2.3 kg) per diet that were individually housed were fed ad-lib for eight weeks. Pigs were allocated to diets in complete randomized design. Average daily gain (ADG), average daily feed intake (ADFI) and feed conversion ratio (FCR) were measured. The enzyme treated FLPH diets had higher (P < 0.05) CP, lower (P > 0.05) NDF and ADF concentrations compared to untreated FLPH diets. The LFPHE diet had higher (P < 0.001) final weight, ADG and lower (P < 0.001) ADFI and FCR compared to LFPH, HLFPH and HLFPHE. It was concluded that LFPHE diet may be an alternative feed source for growing pigs as indicated by higher gain and lower intake compared to LFLPH, HFLPH and HFLPHE.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".