Life Cycle Assessment (ISO 14040) Implementation in Foods of Animal and Plant Origin: Review
Bibliographic record
Abstract
The importance of environmental protection has been recently upgraded due to the continuously increasing environmental pollution load. Life Cycle Assessment (LCA), wellknown as ISO 14040, has been repeatedly shown to be a useful and powerful tool for assessing the environmental performance of industrial processes, both in the European and American continents as well as in many Asian countries (such as Japan and China). To the best of our knowledge, almost no information is provided in relation to LCA implementation in Africa apart from an article related to Egypt. Although food industries are not considered to be among the most heavily polluting ones, for some like olive oil, wine, dairy, and meat processing, their impact on the environment is a heavy burden. The introduction of LCA aimed at identifying both inputs and outputs to find out which are the most detrimental to the environment in terms of water/energy consumption and solid/liquid and gas releases. In this review, a thorough coverage of literature was made in an attempt to compare the implementation of LCA to a variety of products of both plant and animal origin. It was concluded that there is a high number of subsystems suggested for the same product, thereby, occasionally leading to confusion. An idea toward solving the problem is to proceed to some sort of standardization by means of several generic case studies of LCA implementation, similarly to what had happened in the case of Hazard Analysis and Critical Control Points (HACCP) implementation in the United States, Canada, Australia, United Kingdom, and other countries.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".