Estimation of the Market Competitiveness of Kelp by Municipality in Hokkaido
Bibliographic record
Abstract
Japanese cuisine, “konbu” (kelp) is an essential ingredient as a raw material for dashi and other Japanese foods. Nine types of kelp are harvested in Japan and the type and use vary by region, including “Rausu konbu” or “Rishiri konbu,” which are named after the harvest location, and traded under their brand name. Even in other areas, kelp is currently competing with the region as a de facto brand. Although kelp from different regions is not completely substitutable, there is competition in each region for a given kelp type. In this paper, based on price and production volume data of 20 municipalities in Hokkaido, which produces more than 80% of total kelp production in Japan, we estimate the competitiveness of each region, using a discrete/continuous model. The results of the analysis reveal that not only well-known brands such as “Rausu konbu” and “Rishiri konbu” but also some areas with large production volumes, have a degree of competitiveness that is not affected by annual fluctuations of production volumes. This tendency is confirmed by the stability of market competitiveness even in subsamples of the sample period. In some areas, due to a decrease in prices and increased production, a slight change in competitiveness occurred.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".