Opportunities for Camelina Meal as a Livestock Feed Ingredient
Bibliographic record
Abstract
Camelina sativa is an annual oilseed crop that requires low inputs. Recently, interest in camelina oil for both human use and biofuel production has increased. Camelina oil extraction is performed through two main methods, namely, mechanical expulsion and solvent extraction. The resulting meals from the oil extraction process show promise as an animal feed due to their high crude protein content. Solvent extraction removes more oil from the seed, which results in a meal that is lower in fat and higher in crude protein concentration than expelled meal (3.52 vs. 13.69% and 41.04 vs. 34.65%, respectively). Solvent-extracted camelina meal has a similar chemical composition to canola meal but less crude protein and more fiber than soybean meal. Camelina meal is also limited by its anti-nutritional factors, mainly glucosinolates. Camelina meal contains 23.10 to 44.90 mmol/kg of glucosinolates, but processing methods may be able to decrease the total glucosinolates. Heat-treating the camelina meal can decrease glucosinolates and remove residual solvent in the solvent-extracted meal. The fungal fermentation of canola meal has also decreased glucosinolates, which could be used in camelina meal as well. The selective breeding of camelina varieties to decrease glucosinolates in the plant is also a solution to the high glucosinolates found in camelina meal. Current feed regulations in the US and Canada limit camelina meal to 10% inclusion in broiler chicken, laying hen, and cattle diets.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".