Constructing Ontology-based Exclusive Environmental Certification Systems
Bibliographic record
Abstract
Since the 1970s, governments around the world have developed environmental regulations and environmental management system (EMSs). Public and private enterprises are obligated to comply with EMSs to prevent environmental pollution. After the International Organization of Standardization (ISO) published the ISO 14001 EMS in September 1996, environmental certification became a focus of attention for enterprises worldwide. Subsequently, various EMSs, such as the ISO 14064-1 Greenhouse Gas Emission Auditing System in 2006 and the ISO 50001 Energy Management System in 2011, have been developed. Governments can demand environmental certification to enforce compliance with environmental regulations. Furthermore, environmental certification can motivate enterprises to improve environmentally relevant practices, conserve energy, and lower costs. This study establishes an ontological knowledge model of shareable and reusable environmental certification index systems. According to this knowledge model, an environmental certification system, an environmental performance index system, and a leather-industry-exclusive environmental certification system were created using the Web Ontology Language (OWL) found in the Protégé platform. The relationship between the EMSs and the performance indices was then generalized to explain the certification systems and their attainable performance indices to enterprise owners. Thus, enterprise owners can select applicable EMSs according to this EMS ontology.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".