Refining chemicals from kraft mill waste for use in the food and pharmaceutical industries and in medical research
Bibliographic record
Abstract
Transforming industrial waste into high-value products is one of the strategies of the transition from traditional pulp mills to future biorefineries. This study focuses on natural compounds derived from tree-based recovery cycle waste in kraft pulp mills, which exhibit specific biological functions and hold potential for applications in drug discovery, medical research, pharmaceuticals, cosmetics, and food additives. The primary goals of this research are to identify and recover these compounds and to leverage neural networks to identify novel bioactive compounds in these wastes, based on their chemical structures. Compounds of interest have been identified and extracted from waste deposits found in the stripping systems of evaporation plants in kraft pulp mills. Three of the compounds that have been quantified are stigmasterol, resibufogenin, and glycodeoxycholic acid, all of them exhibiting beneficial properties. A deep learning system has been developed to identify novel compounds with potential benefits present in the deposits based on their chemical structures. Preliminary neural networks appeared to be moderately successful to predict compound functionality, identifying several promising candidate compounds with potential applications. The models are promising tools for biological activity screening of natural compounds. The functionality of some of these compounds may be further validated through in-vitro drug sensitivity and resistance testing (DSRT). This study highlights the potential of kraft mill deposits as a sustainable and cost-effective resource for producing high-value compounds.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".