A Case Study on the Eco-Efficiency Performance of a Composite Processing Industry: Evaluation and Quantification of Potential Improvements
Bibliographic record
Abstract
In this study, an attempt was made in order to measure and evaluate the eco-efficiency performance of a pultruded composite processing company. For this purpose the recommendations of World Business Council for Sustainable Development (WCSD) and the directives of ISO 14301 standard were followed and applied. The main general indicators of eco-efficiency, as well as the specific indicators, were defined and determined. With basis on indicators’ figures, the value profile, the environmental profile, and the pertinent eco-efficiency ratios were established and analyzed. In order to evaluate potential improvements on company eco-performance, new indicators values and eco-efficiency ratios were estimated taking into account the implementation of new proceedings and procedures, at both upstream and downstream of the production process, namely: i) Adoption of a new heating system for pultrusion die-tool in the manufacturing process, more effective and with minor heat losses; ii) Recycling approach, with partial waste reuse of scrap material derived from manufacturing, cutting and assembly processes of GFRP profiles. These features lead to significant improvements on the sequent assessed eco-efficiency ratios of the present case study, yielding to a more sustainable product and manufacturing process of pultruded GFRP profiles.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".