Bibliographic record
Abstract
Cattle markets in North America were already showing signs of tight supplies long before the 2011 and then 2012 drought occurred in the US. In many parts of cattle producing areas in the US supplies have been tightened even further as we head into 2013. In Canada, after five years of downsizing post-ESE, the beef herd appears to be poised to see growth through heifer retention. Contrary to the US, western Canada has seen two fairly good years in terms of moisture for grass and forage production - critical to growing the herd. But higher grain prices in 2012 are having impacts on both sides of the border as higher costs have meant cattle feeding losses for much of this year. Margin operators have been caught between fewer cattle supplies/higher cattle prices and record high costs. A key concern in both the cattle feeding and cattle packing industries is that there is too much capacity chasing these fewer cattle supplies. Add to that the global economy, which has been fragile at best, and you get a very volatile environment ranging from: tight supplies = higher prices; all the way to: cautious consumers = competitive protein markets both here and abroad. Record high cattle and beef prices would have historically meant great profits but with today's cost structure (namely grains), it's not quite that simple.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.318 | 0.109 |
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".