Potential Source of Energy and Chemical Products: Sections 5–8
Bibliographic record
Abstract
This chapter contains sections titled: Fermentation Modes of Industrial Interest Batch Process Fed-Batch Processes Semi-Continuous Processes Continuous Processes Industrial Processes Types of Bioreactors for Ethanol Production Solid Phase Fermentation (Ex-Ferm Process) Simultaneous Saccharification and Fermentation (SSF) Process Recycle Systems Novel Reactors for On-Line Product Removal Some Examples of Industrial Processes Ethanol from Corn Ethanol from Cassava Root Ethanol from Potatoes Ethanol from Jerusalem Artichoke Tubers (Topinambur) Ethanol from Carob Pod Extract Ethanol from Cellulose Dilute Sulfuric Acid Process Strong Acid Hydrolysis Process Ethanol Production from Agricultural Residues via Acid Hydrolysis Ethanol from Newspaper via Enzymatic Hydrolysis Ethanol from Municipal Solid Waste via Acid Hydrolysis Ethanol from Waste Sulfite Liquor (WSL) Ethanol from Whey By-Products of Ethanol Fermentation Waste Biomass Stillage Carbon Dioxide Fusel Oils Economic and Energy Aspects of Ethanol Fermentation Ethanol from Jerusalem Artichokes (A Case Study) Energetics Ethanol from Corn Ethanol from Sugarcane and Cassava Ethanol from Wood Ethanol from Cornstalks
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.169 | 0.071 |
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".