Employing 360° Video Panorama Technology to Determine the Impact of Details on the Collective Memory of the Urban Scene
Bibliographic record
Abstract
The urban scene on the campus is formed by a diverse array of elements.Organizing these elements and understanding their relationships contribute to creating an urban structure that meets individuals' needs.This study aims to present a practical methodology for identifying and revealing influential architectural details, focusing on the case of Mosul University.An interactive virtual environment was tested through a 360-degree video presentation of the case study, employing the Gazerecorder computer program with a group of students and professors from Mosul University, thus conducting in-depth interviews to identify the multiple architectural elements that contribute to shaping the architectural identity of the campus.These elements include built features such as details in facades, floors, and ceilings, as well as nonbuilt elements such as furniture, activities, and green spaces.These elements interact with various characteristics and relationships that contribute to creating a comprehensive image of the urban environment, with variations in impact and influence depending on the architectural context and geographical location of each element.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".