A review on the application of machine learning in production of woody biomass from natural and planted forests
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it