ພະຍາກອນການສງອອກປະລມານຢາງພາລາຂອງ ສປປ ລາວ
Bibliographic record
Abstract
Lao PDR's rubber export trend; 2) To study the seasonal changes in the amount of Lao PDR's rubber export; 3) To find an appropriate forecasting model of the amount of Lao PDR's rubber export. The data used in this study is second-hand data obtained from the Statistics and Information Center from January 2010 to December 2021 by dividing the data into quarters of 48 values. By using Microsoft Office Excel 2016 to analyze trend, seasonal index and using MATLAB program to find forecasting models and compare the obtained models to the lowest MAD and MSE values. The results of the study found that: 1) Lao PDR's rubber exports tend to increase; 2) Lao PDR's rubber exports in the 4th quarter have the highest volume of rubber exports (88.08% more than usual), while the quarters with the lowest export volume are the 2nd quarter, the 1st quarter and the 3rd quarter (below the usual 43.47%, 41.78% and 2.82% respectively) and 3) Studying the forecasting model found: Gaussian 5 forecasting model is the most suitable forecasting model for this data set.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 0.012 |
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".