Production information needs of American Boer Goat Association members in the Midwestern United States
Bibliographic record
Abstract
The goat industry in the United States is growing. The low input costs of production (USDA, 2005) has attributed to the gaining popularity of goats to producers in developed countries, such as the United States. With this growth comes a demand for a quality product to allow producers to compete in a growing world market. Without timely and adequate information producers could get pushed out of the market because of a less than standard product. The purpose of this study was to determine the adequacy in terms of quality and quantity of the goat production information available to producers. This study was also conducted to report the demographics of American Boer Goat Association members in the Midwestern United States; determine the knowledge level of goat producers in regards to the goat industry; and determine the barriers that are limiting respondents’ operations. Additionally, this study sought to identify the perceived level of preference for delivery/method and level of goat production information materials used in the industry currently, as well as what goat producers would like to see in the future. Producers were also asked which production categories they would like to see additional research information. The population (N=944) for this study consisted of American Boer Goat Association members in the Midwestern United States. A random sample (n=300) was drawn from the population to complete a researcher-developed, baseline survey instrument. Results from the study suggested that producers do not find the amount of goat production information adequate to meet their needs. Producers suggested that there should be more goat information in all production information categories, especially health, marketing, meat production and quality, and nutrition. The Internet is the most frequently utilized resource by respondents to this study,
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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 | 0.001 | 0.003 |
| 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) | 0.014 | 0.002 |
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".