Bibliographic record
Abstract
Introduction and Significance of StudyBuilding information modelling (BIM) is a 3D modelling process which provides anyone of interest, such as engineers and architects, with tools to be able to design, plan and manage the construction of any type of infrastructure.Due to the ability of BIM to store all relevant information related to a project in a single model, the use of BIM has drastically increased over the past few decades, which in turn means that it is key for civil engineers to keep up with the knowledge required to use the latest technologies being applied in the construction industries.In addition to BIM, Computational Fluid Dynamics (CFD) is the most ubiquitous software to simulate and evaluate indoor environments in all fields.Despite the benefit of accuracy obtained from traditional measurement methods, the CFD software provides individuals with the advantage of creating an environment that is not only more comfortable, but also healthier.Yang [2] CFD has gained momentum in relation to the domain of building environments, encompassing such areas as Indoor Air Quality and HVAC, as well as natural and mechanical ventilation.Thus, with the use of CFD one can easily verify design solutions [3].A study conducted by Huizenga et al. [4] to determine air quality and thermal comfort revealed that despite the tireless efforts of experts to create an environment that was comfortable, only 39% of the participants in the survey of the occupants of 215 building in US, Canada and Finland expressed their satisfaction with the results of said experts.Unequivocally, the most reliable method one could use to procure the most accurate depiction is through actual measurements.However, such a method is not feasible, as it is toxic, time-consuming and above all expensive in regard to extensive parametric studies.The CFD software is more frequently used in comparison to the experimental approach to analyze Indoor Air Quality issues by computing the airflow and pollutant distributions in buildings, in order to arrive at a numerical solution of the flow behaviour [5].Hence, with the usage of CFD to simulate indoor environments, resources can be better allocated by saving manpower and material resources.Moreover, another benefit of such a software is that the parameter Crimson
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".