Classification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".
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
This paper evaluates a novel biorefinery approach for the conversion of lignocellulosic biomass from pinewood. A combination of thermochemical and biochemical conversion was chosen with the main product being ethanol. Fast pyrolysis of lignocellulosic biomasss with fractional condensation of the products was used as the thermochemical process to obtain a pyrolysis-oil rich in anhydro-sugars (levoglucosan) and low in inhibitors. After hydrolysis of these anhydro-sugars, glucose was obtained which was successfully fermented, after detoxification, to obtain bioethanol. Ethanol yields comparable to traditional biochemical processing were achieved (41.3% of theoretical yield based on cellulose fraction). Additional benefits of the proposed biorefinery concept comprise valuable by-products of the thermochemical conversion like bio-char, mono-phenols (production of BTX) and pyrolytic lignin as a source of aromatic rich fuel additive. The inhibitory effect of thermochemically derived fermentation substrates was quantified numerically to compare the effects of different process configurations and upgrading steps within the biorefinery approach.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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