Beyond waste in agriculture: Feedstock characterization for thermochemical conversion based on potato above-ground biomass
Bibliographic record
Abstract
Agricultural residues represent a valuable opportunity to develop circular bioeconomic systems centered on biomass. Characterizing this type of biomass can alleviate the pressure on current biomass sources (e.g., in forests and their biodiversity), enhance agricultural waste management, and reduce crop field emissions. Thus, this study aimed to evaluate the potential of agricultural plant-based residues as feedstock for thermochemical conversion processes, focusing on potato above-ground biomass to enhance herbaceous characterization. The gravimetric characterization of this type of biomass revealed a water content of 89 % for potato above-ground biomass with differences per plant section. Biomass abundance was also measured, showing that well-developed leaves and main stems were more plentiful. After 70 days after planting (DAP), maximum plant development was achieved, differing from heights development at 40 DAP. Hence, identifying specific times for residue recovery can help develop strategies for biomass recovery, thereby reducing pretreatment costs following the selection of suitable conversion technologies.
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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 | 0.000 |
| 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".