A Twist on Collaborative Delivery—Construction Manager at Risk and Progressive Design
Bibliographic record
Abstract
The City of Sherman, Texas, is in the midst of an infrastructure improvement program to support the recent growth of industry in the area. A significant element of the expansion program is the need for additional treatment of new industrial wastewater. After treatability studies concluded that a new industrial WWTP would be required, the City began the daunting task of delivering this new plant in less than 30 months with a design scarcely more than a concept. The team—including the City, Consulting Engineer, and Program Manager—sought collaborative delivery methods to bring a contractor on board. Based on Texas legal restrictions preventing the use of Design Build, the team selected Construction Manager at Risk (CMAR) as the optimal method to deliver the project. With the CMAR engaged, the full team was set. To deliver this project, the team used several logistical and management tools. Tools used included early procurement of long lead equipment, early works packages to allow construction to begin while design was finalized, team collaboration in the form of regular partnering sessions and teambuilding exercises, proactive budget management to ensure funding was secured in time, schedule collaboration to adjust and react quickly to unforeseen circumstances, and detailed quality control to ensure field work matched design intent. A detailed evaluation of each of these tools is discussed. Major challenges that the team overcame included rapid cohesion of a diverse team, rapid construction pace, public bidding of all work and equipment packages, design progression after bidding, and maintaining high work quality (both design and construction). The project is currently under construction and is on target to be completed on schedule in order to serve the industrial customers.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.001 | 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".