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
One of farm bankers' challenges, for years, has been convincing their ag borrowers to get involved in pricing protection through hedging and other mechanisms that can save them from taking it in the neck when markets threaten to turn their season into a loss. This, of course, is not only out of concern for the farmer's neck, but for what the bank has on the line in lending to the farmer. If the farmer has a bad year, chances are the bank will also feel its share of pain. While more farmers than ever explore various hedging options today, many still won't touch them. But a new wrinkle in the market holds the promise of making price protection a great deal more palatable for producers, with advantages-beyond simple protection--to their lenders as well. Making hedging appeal to farmers The reticence of some producers to get involved with hedging isn't simply a matter of ignorance. Not every hedge provides the protection it is cracked up to do. Nor are all hedges created equal, and sometimes producers have been hurt by hedges--consider the hedge-to-arrive mess of the previous decade as an extreme example. There are a few good hedge brokers out there, and there are a whole lot of hedge brokers who aren't so good, said Dennis Everson, president for agri-business, First Dakota National Bank., Yankton, S.D., speaking during a session at last fall's North American Ag Lenders Conference, by the ABA and the Canadian Bankers Association. And hedging doesn't come for flee; consultants and brokers charge for their assistance. An alternative means of fixing a price, forward contracting, creates a specific relationship with a specific buyer prior to production, a commitment that not all producers care to make. Everson's $577.5 million-assets bank has been offering selected customers a special loan package since April 2005 that avoids some of this expense. The bank, one of the nation's largest community-bank farm lenders, is the first to team up with Cargill, Inc., to provide what the bank markets as the Extracense[TM] loan. Cargill has filed for patent protection on the concept. In time, other banks that work with Cargill will be able to adopt their own product names, and additional bank deals are in the works. Cargill first described the concept of Extracense loans at the 2002 ag conference, and has been working it through the practical and regulatory hurdles since. In essence, the idea revolves around a loan with a hedge contract built into it. With Cargill effectively providing the hedge to the bank, however, there is no fee or commission involved. A number of features make this choice particularly attractive to farmers. An example of how the hedge works The concept can work with various crops and livestock types, but for argument's sake, let's use corn, one of the examples demonstrated at the conference by Everson and Jeff Seeley, vice-president, risk management, Cargill. Under an ordinary lending relationship, the borrower would grow corn on 1,000 acres, with an estimated average yield of 150 bushels per acre. To produce this, the farmer would need to borrow $100 per acre at prime plus one percentage point for eight months (all rates mentioned in this article are annualized). Under the new concept, the producer would borrow at prime minus two. That will certainly bring the farmer in the door for a look. Here's the appeal of this concept, in its standard form, for borrowers: * They borrow at three percentage points below the market rate. * If the price of the producer's crop has fallen by an agreed-upon amount--say, corn falls by 25 cents per bushel on the Chicago Board of Trade--then the loan principal will be reduced. In the example of corn, a one percentage point reduction of the principal due at maturity would be made for every penny of price decline beyond 25 cents per bushel. (This is up to a limit of half of principal. …
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 | 0.001 | 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 it