Forecasting timber prices with ARIMA model and factors influencing the price of timber in Czechia
Classification
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".
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study focuses on the analysis of the price development of sawlogs of grade III quality A/B in the Czech Republic for the period 2005–2024 and on the prediction of their future development using the ARIMA model (1,1,1). The analysis of the historical data showed considerable price volatility, with the minimum value recorded in the third quarter of 2020 (CZK 1303/m 3 ) and the maximum value in the third quarter of 2022 (CZK 3084/m 3 ). The average price was CZK 1982/m 3 with a standard deviation of CZK 1384.66/m 3 . The forecast shows a slight increase in prices until 2027, with projected values ranging between CZK 2377/ m 3 and CZK 2457/m 3 . The weak negative correlation between price and quantity harvested indicates the influence of other factors such as global demand, seasonality and market regulation. The study provides evidence for strategic planning in the forestry and wood processing sector.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it