1998 Front Materials: 4th Quarter
Bibliographic record
Abstract
What agricultural and resource economists fire finding about food, farm, and resource issues.• OPTIONS AND FUTURES MARKET STRATEGIES.Futures and option strategies rarely consistently enhance incomes from field crops, unless the producer has superior forecasting ability or information-say Zulauf and Irwin.• MORE OPTIONS AND FUTURES MARKET STRATEGIES.Preharvest marketing strategies that used both futures and options markets sometimes increased, but sometimes decreased, profits for model corn and soybean farms in Iowa and Ohio-say Wisner, Blue, and Baldwin.• FISHERIES.For the Mid-Atlantic surf clam and ocean quahog fishery, a program to issue individual transferable quota to commercial fishers will both cut the number of fishing vessels and improve economic efficiency-says Weninger.• CANADA-U.S. MILK AND DAIRY PRODUCTS TRADE.Eliminating border tariffs and other trade impediments would have little or no net effect on milk and dairy product trade between Canada and the U.S.-say Meilke, Sarker, and LeRoy.• CANADIAN DEMAND FOR U .S .FRESH BEEF AND PORK.Western Canadians prefer fresh beef and pork products from Canada to those produced in the U.S.-say Quagrainie, Unterschultz, and Veeman.• DIETARY FAT AND CORONARY HEART DISEASE.Between 1955 and 1993, increased consumption of less saturated vegetable oils, which displaced some of the animal fats in the Canadian diet, cut the incidence of coronary heart disease by just over 10 percent, and reduced associated costs of illness by over $800 million-say Gray, Malia, and Stephen.• CONSERVING RESIDENTIAL WATER.Raising the price of water does reduce household water use, but affects lowincome households most.Other types of conservation policies, such as landscape irrigation restrictions, also reduce household water use-say Renwick and Archibald.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.935 | 0.921 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".