Fermentable sugars production by enzymatic processing of agave leaf juice
Bibliographic record
Abstract
Abstract The Mexican mezcal industry annually processes approximately 2.92 × 105 t of mezcal agave, generating roughly 1.46 × 105 t of agave leaves per year, which represents a potential carbon source of at least 8170 t via enzymatic processing of agave leaf juice. This carbon source is considered an attractive alternative to produce biofuels and/or chemical products since it is produced and used without adversely affecting the environment. The aim of this investigation was to determine the effect of temperature, pH, enzyme concentration, and bioreaction time on the enzymatic hydrolysis of agave leaf juice enriched in fructan to maximize the fermentable sugars production from three varieties of mezcal agave, using a low‐cost commercial brand of hydrolase. This process generated a sugar‐enriched juice of 80.07–136.12 g/L of reducing sugars. A Box‐Behnken experimental design and a mathematical surface response analysis of the hydrolysis were used for process optimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".