AUTOMATE AND STANDARDIZE MULTI-STORY BUILDING SCHEDULES THROUGH REPETITIVE SPATIOTEMPORAL MODEL
Bibliographic record
Abstract
Traditional planning models, based on Gantt-Precedence logic, focus on defining activities and establishing their constraints. Resources can then be assigned to those activities. Spatial constraints are usually neglected, resulting in site congestion or relaxation. This is due to the lack of representation of the sequence of work and supply flows on the site. Spatiotemporal models are considered more appropriate, especially for planning the construction of buildings projects. These models simultaneously consider activities, resources, and space as constraints to construct a realistic execution schedule. In addition, multi-story buildings share many common components and activities. Thus, it is plausible to think of establishing standardization in order to create an automated system to support the construction of validated schedules. The objective of this study is to develop an automated and standardized approach for creating construction schedules for multi-story buildings. Using the data from many studies, a mapping of multi-story construction schedules through repetitive spatiotemporal approaches can be created to allow for a consistent and systematic representation of the schedule.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".