Bibliographic record
Abstract
The emergence of the middle class in countries such as Brazil, Russia, India, and China is resulting in increasing global demand for animal-based food products. This increase represents a unique opportunity for Canadian livestock producers to export their products to new markets and expand Canada's reputation as a global provider of safe and highest quality food items. This article has two major themes. First, current Canadian contributions to livestock genomics in the cattle and swine industries are outlined. Second, important future opportunities are discussed, including the high throughput collection of phenotypic data, development of environmentally friendly livestock, emergence of decision support software, and the use of Web 2.0. Through the use of genomic technologies, livestock producers can not only ensure that the nutritional demands of Canada are secured, but also play a pivotal role in ensuring the rest of the world is fed as well. Furthermore, investment through initiatives led by Genome Canada has ensured that Canada is favorably positioned to contribute cutting-edge solutions to meet this global challenge. Ultimately, genomic-based innovations will enable producers to increase efficiency, lower production costs, decrease the use of prophylactics, and limit the expenditure of resources.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".