The Seasonal Fluctuation of Timber Prices in Hyrcanian Temperate Forests, Northern Iran
Bibliographic record
Abstract
Seasonal fluctuations play an important role in the pricing of a timber sale. A good understanding of timber price mechanisms and predictability in the timber market would be very practical for forest owners, managers, and investors, and is crucial for the correct functioning of the timber sector. This research aimed to analyze the effect of sale season on timber (sawlog and lumber) prices of high-value species groups (e.g., oriental beech, chestnut-leaved oak, common alder, velvet maple, and common hornbeam) in the Hyrcanian temperate forests (Northern Iran). The data were collected from official sale documents of the Azarroud Forestry Plan from 1992 to 2015. The relevant data of 592 sale lots at forest roadside were extracted into a data set. Then, the average timber prices (sawlog and lumber) per season/year in quarterly frequency were calculated. In doing so, two-time series of seasonal prices for the sawlog and lumber was obtained. The stationarity of the time series was statistically verified using the augmented Dickey–Fuller test. The effect of sale seasons on timber price was first analyzed using multiple linear regression analysis dummy variables. The results showed that autumn and summer have a significant positive effect on timber prices of 6.5% and 6.1%, respectively. Additionally, the decomposition of time series results showed that the highest prices of the sawlog and lumber were in quarter 3 and quarter 2, respectively, due to an increase in construction activities that picked up in the autumn season. Information about potential price fluctuations will be plausible and allow suppliers and users of sawlogs to adjust their supply and demand. This valuable information can be used in marketing and strategic forest management planning for Hyrcanian temperate forests and other temperate countries with similar conditions.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".