Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Nearly 11 years ago when I became the Chief Economist of the National Cattlemen’s Beef Association, one of the greatest jobs any son of a Kansas farmer/rancher could have, I was told that unlike wheat, corn and soybeans, beef trade just didn’t matter much. Early on during my tenure, it was the opinion of some that we needed to focus our efforts on keeping beef imports out and that beef exports would never be anything more than a niche business of little consequence to the bottom line of U.S. cattlemen. I politely disagreed then and recall what I was thinking to myself at the time…you don’t know what you’ve got until it is gone. Back in 2003 BCSC (Before the Cow that Stole Christmas), beef exports in 2003 were $3.86 billion with Japan leading the way at $1.4 billion – statistics I can still recall off the top of my head. Actually, beef exports already mattered a lot back in 2003. Back in the fall of that year, our export tonnage was hitting new monthly levels and the November 2003 value of beef exports was $150/head – a historic high. If hides and offal were added in the value of these exports came to just under $200/head. Currently, export markets account for almost $300/head, which means that 17 percent of the value of a finished steer now comes from the international marketplace. Another way to look at it is that 17 percent of the money used as payment for the product we are producing comes in the form of Pesos, Yen, Won, Yuan, Euros, Rubles, Canadian or Taiwan dollars. The point here is that producers should not get caught up in the notion that per capita domestic beef consumption is declining. Think of the marketplace in terms of every consumer on the planet who buys their food from a supermarket, “wet market” butcher or restaurant. Also consider that one of the biggest economic changes of the past decade is the increased buying power of consumers in all corners of the globe for U.S. beef.
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.
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) | 0.000 | 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) | 0.001 | 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 it