Analysis of Swallow Nest Nitrite Levels and Export Volume Projections: A Statistical Approach to Quality Improvement and Global Market Development
Bibliographic record
Abstract
The global SARS-CoV-2 pandemic is impacting public health and living systems. Edible Bird's Nest (EBN), one of the variants of Swallow's Nest (SBW), is recognized as a medicinal food. The potential of SBW as a therapeutic agent is gaining attention due to its ability to inhibit viral hemagglutination activity. This study provides information on the prediction of SBW demand in the coming year so that it can contribute to determining Indonesia's SBW export performance. Net weight data (tons) of SBW export time series were obtained for 10 years from 2012 to 2022 in 10 countries, namely: Hong Kong, China, Singapore, USA, Vietnam, Canada, Taiwan, Thailand, Japan, and Cambodia, combined with laboratory examination on nitrite levels conducted at the Surabaya Agricultural Quarantine Center, Indonesia. This study shows the minimum limit on nitrite levels in SBW exports and the need for SBW exports in the next four years. One of the requirements for SBW exports is the minimum limit of nitrite levels. The ARIMA method has been implemented to forecast SBW export demand in 10 countries for the next four years. SBW export demand in the next four years is expected to decline. The findings make a significant contribution as a source of information for decision-makers involved in SBW export activities.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".