Effective R-value of enclosed reflective space for different building applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Many parts of the building envelopes contain enclosed airspaces. Also, the insulating glass units in fenestration systems, such as curtain walls, windows, and skylight devices, contain enclosed spaces that are normally filled with air or heavy gas such as argon, xenon, or krypton. The thermal resistance (R-value) of an enclosed space depends mainly on the type of the filling gas, emissivity of all surfaces that bound the space, the size and orientation of the space, the direction of heat flow through the space, and the respective temperatures of all surfaces that define the space. Assessing the energy performance of building envelopes and fenestration systems, subjected to different climatic conditions, requires accurate determination of the R-values of the enclosed spaces. In this study, a comprehensive review is conducted on the thermal performance of enclosed airspaces for different building applications. This review includes the computational and experimental methods for determining the effective R-value of enclosed reflective airspaces. Also, the different parameters that affect the thermal performance of enclosed airspaces are discussed. These parameters include the following: (a) dimensions, (b) inclination angles, (c) directions of heat flow, (d) emissivity of all surfaces that bound the space, and (e) operating conditions. Moreover, numerical simulations are conducted using a previously developed and validated model to investigate the effect of the inclination angle, direction of heat transfer, and the coating emissivity on the R-values of enclosed spaces when they are filled with different types of gases.
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.
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 | 0.000 | 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 it