Implications for the Feed Industry
Bibliographic record
Abstract
The animal feed industry relies on cereal grains and pulses to supply energy and protein, respectively. Increasing amounts of both groups of ingredients, but in particular, cereal grains, are being used for the production of ethanol for biofuel. Currently, about a third of the maize crop produced in the United States is used for ethanol production and will rise to about 43 % by 2015 (van der Aar and Doppenberg, 2009). Although limited in impact, a considerable amount of oils produced from oilseeds such as canola, soybean, peanut and sunflower is being processed into biodiesel. This is causing a major strain in the supply of edible oil for feed manufacturing. An indirect effect of the increased use of maize for ethanol production is the change in land use, whereby, farmers in North America are converting land previously used for soybean production into maize production (Anon., 2011a). Although maize is the main cereal grain used by the ethanol industry, it is by no means the only grain used but plants in Canada and Europe tend to use more wheat while the two main plants currently in production in Australia and a few in the USA rely on sorghum.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.082 | 0.008 |
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".