Yield From Forest Harvesting Operations for the Production of Charcoal in the State of Minas Gerais
Bibliographic record
Abstract
The charcoal is a renewable natural resource, produced from wood by the process of carbonization and with great energetic importance. However, there is still little research and use of new technologies to optimize the use of wood in the production of charcoal. Therefore, the present work was aimed at analyzing the yield from forest harvesting operations for the production of charcoal. The research was developed at Vallourec e Mannesmann Florestal, a company located at Itapoã farm, municipality of Paraopeba, Minas Gerais. To this end, the harvest and timber transport operations in the short log system, the carbonization, and the properties of the charcoal produced were assessed. To this end, data was collected from eighteen 9-hour shifts for the Harvester, fifty-four 9-hour shifts for the forwarder, and 36 carbonization cycles. The equipment was analyzed working with three different log lengths—2.1 m, 3.7 m, and 5.0 m. The results demonstrate, during the cutting and processing, that the yield by cutting longer logs is higher. Likewise, at forwarding, the operation’s yield increases according to log length for thick and thin logs. Finally, concerning carbonization, the yield at the furnace loading stage was higher as the length of the log used increased, however, upon unloading the furnace, it was when it decreased.
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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 | 0.001 |
| 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) | 0.001 | 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".