Application of the Markov Chain in Macroeconomic Analysis of a Managed Forest in the Amazon
Bibliographic record
Abstract
This study includes an economic analysis of the dynamics and prognosis of the forest structure of an area under sustainable forest management in the Amazon, in the primary production of tropical roundwood. It analyzes measurements taken between 2001 and 2011 and carries out forest forecasting for the period from 2001 to 2021 with the application of the Markov Matrix Chain. The variables measured by the probability matrix were transformed into equivalent annual rates and compared over the same period to the Brazilian macroeconomic indicators of gross domestic product (GDP) and the real interest rate of primary roundwood production (TJLP). The valuation of tree density utilized a series of average prices of world imports and exports of roundwood. Between 2001 and 2011, the parameters of the forest dynamics without valuation were similar to the TJLP (1.4% per year) and, when valued, the rates were close to the GDP of 3.5% per year. The forecast from 2001 to 2021 indicates that unrated economic groups behave in a similar way to the TJLP of 1% per annum and to the GDP of 2.2% per year, except the recovery category, which has a negative rate of 1.9% per year. The monitoring of tropical forests allows the achievement of economic indexes capable of assessing the anthropic and natural impacts in short periods of analysis and projecting them over time on natural capital.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".