Beyond the Construction, Design and Planning Scenarios of Eco-Buildings
Bibliographic record
Abstract
Lately, even in Romania, the interest in ecological homes has begun to take on proportions. Hugged by curiosity, but especially by reorienting towards a (more) healthier lifestyle, people seek to find out as much detail as possible about them - thus capturing the outline and the present work. Although eco-house construction is simple and eliminates many of the heavy stages of a classical construction, future homeowners omit, due to lack of information, this option, especially in the context in which we do not have a good filter of information about this subject, which will give us the real benefits we have.
 Ecologic houses seem to be a trend with strong growth, because the shapes that they can wear are extremely varied. The great variety, low price and promise of ecological housing is likely to convince people to completely change their lifestyle. How well are we prepared for sustainable development? How do we meet the demands of today's society? What does a green house look like, how does it behave in time, and how much does it cost? What are the trends in designing a green house? These are just a few questions that we will answer during the work.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".