Challenges in the development of environmental management systems on the modern university campus
Bibliographic record
Abstract
Purpose The purpose of this paper is to describe challenges and lessons learned in the design and development of a comprehensive, flexible environmental management system (EMS) in a real university setting; also to inform development of similar systems elsewhere and provide a modular, extensible software architecture for such efforts. Design/methodology/approach A modular, flexible software architecture was designed as the cornerstone of a comprehensive, secure web‐based data collection and analysis framework. Environmental data such as utility usages, waste generation and transportation services were identified, collected, and entered into the evolving system. The system is easily extensible to new environmental data types, and supported manual and automated data entry, custom “at‐the‐source data entry” mechanisms, and flexible tools for visually analyzing environmental data captured. Findings Development of automated EMS systems for large institutions is significantly complicated by profound heterogeneity in campus infrastructure, management policies, and limited data accessibility; legacy data are often incomplete or inaccurate. Successful EMS initiatives must explicitly address these challenges through realistic project planning, choice of software technologies, design of system architecture, and administrative commitment. Detailed insights in each of the above areas are provided. Originality/value The authors provide clarifying discussion of sustainability plans versus monitoring systems, place popular technological gadgets such as live building energy monitors into perspective within this framework, and describe design and implementation of a comprehensive environmental monitoring framework. The modular concept for system architecture, design approach, and lessons learned can inform the development of similar comprehensive EMS development efforts.
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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 | 0.018 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".