Investigating the effects of several intervention on supply chain behavior: Evidence from West Nusa Tenggara Province, Indonesia
Bibliographic record
Abstract
This study analyzed the impact magnitudes and patterns of several intervention events, including eight earthquakes and Covid-19 pandemic, on the number of unloading and loading goods in the three main ports and airports in West Nusa Tenggara Province during 2015-2020. The multi-input intervention models are performed for twelve series data obtained from BPS-Statistics of West Nusa Tenggara Province. The results from the estimated response values show that generally the number of unloading and loading in the three main ports and airports have experienced mixed impact, i.e., negative, and positive impacts. As the main concern in this study, the negative impacts were more experienced by the number of unloading and loading goods in airports than in ports indicating that the supply chain in airports was more vulnerable to intervention. Lombok International Airport and Sultan M Kaharuddin Airport received the most negative impact during the period. Most intervention events have delayed impact patterns that are more experienced by the three airports than the three ports. Started in March 2020, Covid-19 produced the widest and biggest negative impacts. These impacts are even bigger than the impacts produced by the severe earthquakes that occurred in August 2018.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".