A review on the application of machine learning in production of woody biomass from natural and planted forests
Bibliographic record
Abstract
The forest is considered as a significant source of woody biomass production. Sustainable production of wood, lower emittance of CO2 from burning, and lower amount of sulfur and heavy metals are the advantages of woods rather than fossil fuels. The utilization of biomass, as an energy resource, is required four main steps of production, pretreatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the production of the woody biomass raw material in forests because investigating numerous related works concluded that there is a considerable reviewing gap in analyzing and collecting the applications of Machine Learning in the woody biomass. To fill this gap in the current work, the origin of woods is explained and the application of Machine Learning in this section is scrutinized. Then, the multidisciplinary enhancement approaches in the production of plants as well as the role of Machine Learning in each of them are reviewed. Meanwhile, the role of natural and planted forests in the production of woody biomass is explained and the application of Machine Learning in these areas is surveyed. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning is widely utilized in the production of woody biomass to enhance the wood production quantity and quality, improve the predictions, enhance the harvesting techniques, and diminish the losses.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".