Life Cycle Assessment of Guava Production and Distribution Systems
Bibliographic record
Abstract
Abstract The life cycle assessment of various processes and materials used during production phase of guava revealed that the production and application of agricultural inputs were the major contributors to global warming, fresh water aquatic ecotoxicity, terrestrial ecotoxicity, acidification and eutrophication as well as caused highest damage to the ecosystem. The application of zinc monosulphate as micronutrient had major impact on abiotic depletion, ozone layer depletion, human toxicity and photochemical oxidation as well as caused highest damage to human health and resources depletion. The life cycle assessment during distribution phase revealed that production and consumption of polyvinyl chloride crates for packaging of guava was a major contributor to abiotic depletion, global warming, human toxicity and eutrophication, whereas consumption of electricity for storage and marketing was major contributor to marine aquatic ecotoxicity, terrestrial ecotoxicity, photochemical oxidation and acidification. The life cycle assessment of various processes and materials on environmental impact indicators in relation to marketing supply chains revealed that abiotic depletion, global warming, ozone layer depletion, human toxicity, fresh water aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, photochemical oxidation, acidification and eutrophication were highest in marketing supply chains involving the maximum number of chain partners/ intermediaries. In order to minimize the impacts of production and distribution of guava on environment, human health, ecosystem and resources, it is necessary to remodel the production process of agricultural inputs, minimize the use of zinc monosulphate, pesticides, polyvinyl chloride crates and electricity and reduce the number of intermediaries in the supply chain.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".