Peta Selera Pasar Teh Dunia
Bibliographic record
Abstract
<!--[if gte mso 9]> Normal 0 false false false MicrosoftInternetExplorer4 <![endif]--><!--[if gte mso 9]> <![endif]--><!--[if gte mso 10]> <! /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} --> <!--[endif] --> In order to increase Indonesia tea export market share is required product improvement of Indonesian tea supply to serve the market preference in each world tea market region. Research results showed that world tea market based on preference attibutes namely (1) tea type, (2) tea grade, and (3) organoleptic score apllying hierarchical cluster analysis, between-groups linkage method and Euclidean method can be classified in to five groups of tea markets. Market Group-1 consist of Poland, Hungary, USA, and Canada; Market Group-2 consist of West Europe Region, Australia, Japan, East Europe in general, Turkey, North America Region, South America Region in general, and India; Market Group-3 consist of Pakistan, Afghanistan, Egypt, Malaysia, and Singapore; Market Group-4 consist of Iran and Middle East Region in general; and Market Group-5 consist of Iraq, Syria, and Russian Region especially Russian Federation. Market Group-4 are markets typical having the highest preference due to only the best tea is accepted. On the other hand, Market Group-1 are tea markets having lowest preference, while others Market Groups are in medium preference.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.582 | 0.526 |
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".