Inventorization and Environmental Management System
Bibliographic record
Abstract
Public concern over industrial impacts on environment has been increasing, with equal intensity in developed and developing countries. Public pressures have led to a flood of laws and regulations using a prescriptive “command and control” approach to protect the environment. Despite a quarter century of governmental efforts to protect the environment and the notable improvements of some grievously impaired environmental resources, development continues to degrade environment. In seeking better results, public and private sector sentiments have now been shifting towards supplemental voluntary approaches to moderate environmental impacts. Such voluntary initiatives normally include a systematic effort of inventorization of wastes and adoption of an environment management system (EMS). Environmental considerations have been assuming importance and several industries have already taken up the tasks of inventorization and developing an EMS on a priority basis simply because improvements in environmental performance have been saving resources and manufacturing costs, improving product quality, and productivity. In this chapter an attempt has been made to highlight the significance of both the tools: the inventorization of wastes and the environment management system. A discussion has been presented on ISO 14000 because; many industries are lately subscribing to such voluntary certification initiatives. A case study of a cigarette industry is presented for proper understanding of the environment management system. What are “Wastes”? Classically speaking, every manufacturing or production activity is aimed at value addition and creation of a product or a service that can be consumed.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.041 | 0.011 |
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".