Prediction of Mechanical Availability in Mechanized Eucalyptus Forest Harvesting Using Artificial Neural Networks
Bibliographic record
Abstract
The planted forests in Brazil and in the world represent a significant slice of the forest sector in general, having the mechanization of activities, especially forest harvesting, is of great importance in the process. The objective was to estimate, through the use of Artificial Neural Networks, more reliable configurations to estimate the mechanical availability of harvester forest harvester-type equipment. The analyzed data were compiled and organized in a database of production monitoring of a company in the forest sector located in the southeast region of Brazil, later trained and validated according to neural network techniques. A trend was observed for the Resilient Propagation algorithm, where among all the trained ANNs, those that obtained the best R2 correlation values, the Quickpropagation training algorithm presented a correlation coefficient between the estimated values and observed values considered high, 0.9908, demonstrating that the trained networks are reliable. The Backpropagation training algorithm had a lower result, with only 75.77% of the estimated mechanical availability variation being explained by the observed mechanical availability. However, the application of artificial neural networks offers a practical solution to the problem of estimating mechanical availability quickly and accurately.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 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".