Enhancement of quality and quantity of woody biomass produced in forests using machine learning algorithms
Bibliographic record
Abstract
Forest is considered 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 wood rather than fossil fuels . The quality and quantity of woody biomass production are a function of some operations including genetic modifications, high-quality forestry , evaluation, monitoring, storage, and transportation. Due to surveying numerous related works, it was found that there is a considerable reviewing gap in analyzing and collecting the applications of Machine Learning in the quality and quantity of woody biomass. To fill this gap in the current work, the above-mentioned operations are explained followed by the applications of Machine Learning algorithms. Conclusively, Machine Learning and Deep Learning can be employed in estimating main effective factors on trees growth , classification of seeds, trees, and regions, as well as providing decision-making tools for farmers or governors, evaluation of biomass, understanding the relation between the woody bimass internal structure and bio-fuel production, the ultimate and proximate analyses, prediction of wood contents and dimensions, determination of the proportion of mixed woody materials, monitoring for early disease identification and classification, classifying trees diseases, estimating evapotranspiration , collecting information about forest regions and its quality, nitrogen concentration in trees, choosing viable storage sites for storage depots and improving the solution, classifying different filling levels in silage, estimating acetic acid synthesis and aerobic reactions in silage, determining crop quantity in silo, estimating the methane production, and monitoring and predicting water content, quality and quantity of stored biomass, forecasting the demand, path way and on-time performance predicting, truck traffic predicting, and behavioral analysis and facility planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".