Linear Scheduling and 4D Visualization
Bibliographic record
Abstract
Described in this paper is a novel approach to four-dimensional (4D) computer-aided design (CAD). It involves a two-way symbiotic relationship between three-dimensional (3D) CAD software and a software implementation of linear planning that includes the ability to define a project product model and associate it with the process model. Strengths of the approach include the ability to readily modify construction sequences and examine their consequences using 4D CAD, and the ability to treat very large scale projects marked by significant repetition of their components. By building on a shared image of the project product model from both a design and construction perspective, the CAD model can be structured in a way that facilitates communication with the scheduling software and vice versa. Various challenges involved in making the 2-way process work are described, including consistency of product representation in the CAD and scheduling models, and the need to group CAD components at different levels of detail and locations to reflect the kinds of aggregation found in schedule representations of a project. The benefits of the approach include the ease with which different scheduling strategies can be explored and visualized, the links between 3D objects and activities can be maintained, and the completeness of the product model representations can be validated. A case study is used to illustrate the approach adopted and the challenges involved.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".