Determination of the net energy content of canola meal from <i><scp>B</scp>rassica napus</i> yellow and <i><scp>B</scp>rassica juncea</i> yellow fed to growing pigs using indirect calorimetry
Bibliographic record
Abstract
The net energy (NE) content of canola meals (CM; i.e. Brassica napus yellow and Brassica juncea yellow) in growing pigs was determined using an indirect calorimetry chamber or published prediction equations. The study was conducted as a completely randomized design (n=6), with (i) a basal diet and (ii) 2 diets containing 700 g/kg of the basal diet and 300 g/kg of either of the two varieties of CM. A total of 18 growing barrows were housed in metabolism crates for the determination of digestible (DE) and metabolizable (ME) energy. Thereafter, pigs were transferred to the indirect calorimetry chamber to determine heat production (HP). The NE contents of diets containing Brassica napus yellow and Brassica juncea yellow determined with the direct determination technique and prediction equations were 9.8 versus 10.3 MJ/kg dry matter (DM) and 10.2 versus 10.4 MJ/kg DM, respectively. Retained energy (RE) and fasting heat production (FHP) of diets containing Brassica napus yellow and Brassica juncea yellow were 5.5 versus 5.7 MJ/kg and 4.3 versus 4.5 MJ/kg, respectively, when measured with the direct determination technique and prediction equations. The NE contents of Brassica napus yellow and Brassica juncea yellow were determined to be 8.8 and 9.8 MJ/kg DM, respectively, using the direct determination technique.
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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 | 0.001 | 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".