SUPPLY CHAIN COMPETENCY: RECIPE FOR CEREAL AND LIVESTOCK MARKETING IN ALBERTA?
Bibliographic record
Abstract
This study examines the nature of Supply Chain Management (SCM) in the Canadian barley industry, economic theories related to SCM, identifies SCM drivers and reviews the Canadian barley marketing system. Two surveys were conducted; one on the feed barley segment of the market; another on the malt barley segment of the market. These surveys provide an outline of the attributes sought by buyers of feed barley in Alberta and by buyers of malt barley in Canada and the United States. A further goal of these surveys was to assess the extent of motivations for SCM in the barley supply chain. Study methods include scaling, factor analysis and stated preference techniques to analyze purchasers' preferences for specific product attributes, business relationships and product source. The major attributes of feed barley sought by Alberta feed manufacturers appear to be physical characteristics such as moisture level, absence of foreign material, high bushel weight and uniform appearance of kernels. Features identified as of moderate importance included levels of certain key amino acids, starch level in the barley sample, as well as such seller characteristics as whether the seller was personally known to the buyer, and willingness of the seller to enter into a long-term supply contract. At the level of the Alberta feed mill industry, results therefore indicate that physical, readily identifiable attributes dominate in the selection of feed barley. As a result, the study identified that SCM is not yet a part of the awareness of barley buyers at feed mills. Among buyers of malt barley, physical or easily assessed attributes such as size of kernel, germination percentage, variety and location where produced ranked highly in a factor analysis as important to malt barley buyers. While results from the sample of Canadian and US buyers did not indicate strong potential for SCM in the malt barley sector, the study found there to be differences in attributes desired by US versus Canadian malt purchasers. Main differences were the concern of US buyers with the region where the barley was grown, and the apparently much higher willingness of US buyers to obtain their malt barley from more than one source. These differences may suggest a potential for SCM in malt barley focused on procuring supplies from regions identified as preferred locations for barley used in malt production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".