Bibliographic record
Abstract
Hospitals are structures where the use of energy and resources that affect both the environment and human health are intense. In particular, the health sector accounts for about 25% of the total carbon emissions of the community by itself and produces large quantities of toxic, harmful substances and contaminated water. Thinking of bringing hospitals out of this situation and making them more beneficial to the environment and human health have provided the emergence of the green hospital concept. In other words, the green hospital has referred to hospitals that use less energy, produce less waste, use more recyclable materials and become healthier organisations on this count. As green hospitals become widespread throughout the world, knowing how hospitals meet the requirements for green hospital has led to the emergence of green hospital certification systems. These certification systems assess hospitals in various areas and score points and certify to hospitals at different levels according to their scores. Hospitals having these certifications gain better patient outcomes, patient / employee safety and satisfaction, cost savings and productivity increase. In this respect, this study will focus on BREEAM for Healthcare, LEED for Healthcare, and Australian Green Star certification systems that are the most commonly used green hospital certification systems worldwide.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".