US wool industry perceptions of digital record keeping and wool supply-chain traceability
Bibliographic record
Abstract
The objectives of this research were to assess the awareness and perceptions of technology currently available to the US sheep industry, to determine how each industry segment prioritizes data records, to understand what level of premiums are needed and realistic, and to assess changes by industry segment and operation size. An online survey was distributed at the 2022 American Sheep Industry Association (ASI) Convention and through the University of Wyoming Sheep Extension and ASI Emerging Entrepreneurs social media pages. Respondents were asked demographic questions and specific segment questions that assessed knowledge of blockchain, importance of records, and current adoption of technology. All descriptive and ANOVA analyses used R statistical procedures (R Core Team; version 2023.03.0). Least squares means were calculated, and the glm procedure of R was used to develop a binary logit model to assess statistical probabilities. Significance was considered at α = 0.05. A total of 61 responses were acquired (n = 52 producers; n = 9 wool warehousers/ processors), representing operations in 19 US states and Ontario, Canada. Respondents indicated they are somewhat familiar with using blockchain technology for tracking records of importance. Adoption of electronic identification (EID) technology and digital record keeping were significant by operation size, with inflection points for operations with <99 head or >2,000 head (i.e., extremes). A market premium 8.1–12% over base price is needed to submit a raw wool core test into a blockchain-based system, but wool warehousers/processors are willing to pay a premium to have access to the records on a blockchain- based system at 4.1–8% over base price. Producers and wool warehousers/processors are somewhat familiar with block-chain technology for tracking records of importance, but producer adoption of the current technologies (e.g., EID, software) is limited. Adoption of current management technologies is influenced by operation size, so further work should be conducted to determine the largest barriers to adoption. Further adoption of the current technologies is needed before blockchain technology can be used to its full potential in the sheep industry.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".