Prognostisering av träproduktsexport från Sverige
Bibliographic record
Abstract
This project aims to, with the help of identifying variables, gather data and machine learning, based on time series and analyzing the variables, create a forecast of the Swedish export of lumber to the rest of the world per quarter and year. To gain the best possible result, four different machine learning models are evaluated, those are linear regression, support vector regression, random forest, and neural network. By executing those models, the accuracy is analyzed to identify if such a forecasting model is valid to use in the lumber industry. The result of this project is most often generating a mean absolute percentage error of around ten per cent for the quarterly forecast and six per cent for the yearly forecast. The results, however, differ depending on which country that is studied, especially when it comes to how much of the variance that is explained and the square root of the mean squared error. The conclusion drawn from this project is that such a machine learning model is a valid tool to forecast exports, mostly if the goal is to achieve an overview of what the future will look like. You can also use the model to optimize logistics. However, this needs to be done with care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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 teacher head, 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".