LM-9 The Market for Hawaii-Grown Natural and Organic Beef
Bibliographic record
Abstract
Producers in Hawaii are currently marketing “natural ” beef, and many people feel that this product has the potential to increase the market share of local beef. To determine how best to realize this potential, beef producers and marketers need more information about natural and organic products, and this publication presents some information to meet this need. General information on U.S. and Canadian beef consumption is presented first. The USDA definitions for natural and organic beef are discussed, along with an overview of the local market for natural and organic beef. Finally, results are presented from a survey of 50 managers of health food stores in major cities on the U.S. mainland. Demand for natural and organic products The demand for natural and organic foods in the USA has increased in recent years. The average annual growth rate for the sale of organic products from 1998 to 2001 was 24.1 percent, with sales reaching almost $9.3 bil lion in 2001. By 2005, the sale of natural and organic Table 1. Consumers ’ perceptions about the attributes of organic food. Percentage selecting Attributes this attribute Without pesticides.................................................. 78 Without antibiotics or growth hormones................. 72 Found in gourmet or specialty section................... 69 Without genetically modified organisms................. 68
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.182 | 0.016 |
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".