Bibliographic record
Abstract
The chemical industrial processes with their energy-intensive production methods and unpleasant image have become “business as usual”. Polycarbonate (PC) is one example of energy intensive methods that has become one of the largest industrial processes. Moreover, a large number of hazardous chemicals are used throughout its manufacturing stages. This study is concerned with environmental aspects of PC production. It investigates life cycle based environmental performance evaluation of supercritical fluid (SCF) Application to PC production, more specifically in i) Environmental performance assessment of SFC as chlorinating alternative and ii) Environmental performance assessment of plasticizing scenario as stabilizing step in the PC production. The advantages of using SCF are tied to the cleaner aspects of the technology, minimization of raw materials and energy demand, reduction of emissions and effluent discharge into the environment and maximization of environmental benefits. Environmental performance of polycarbonates production by SCF was compared with that of traditional production methods. It was observed that supercritical fluid technology has an impact on the emissions reduction compared with traditional methods. This research contributes to understanding the challenges that the industry of polycarbonate production will face in the future where the chemical emissions are increased resulting from production and energy consumptions.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".