Leveraging The CM/GC Project Delivery Approach for Remote High Elevation Dam Rehabilitation Projects In Colorado
Bibliographic record
Abstract
Colorado Parks and Wildlife (CPW) operates more dam and reservoir facilities in the State of Colorado than any other single entity and is in the process of completing rehabilitation upgrades to improve operational safety and modernization as required by the Office of the State Engineer. Many of these dam facilities have been operating 50 to 100 years in remote locations presenting challenges related to routine operations, maintenance, and completion of dam safety related repairs. Historically CPW has utilized design-bid-build procurement of construction services to complete these projects which presented additional challenges related to local contracting capabilities and undesirable contract disputes and claims. CPW recently pivoted to a CM/GC procurement process to contract for construction services for dam rehabilitation improvements where the remoteness, seasonal high-altitude limitations, complex topography, availability of local construction resources, stakeholder involvement, and other factors having an increased potential to negatively impact construction success and completion timeliness. The CM/GC delivery method has proven to be beneficial to the success of these projects by early construction contractor involvement as part of a collaborative Integrated Project Team to promote enhanced constructability solutions, innovations to improve quality and cost efficiency, risk identification and mitigation, and schedule and budget adherence. CPW has completed three projects using the CM/GC project delivery approach and select case histories (particularly the most recently completed Alberta Park Dam Rehabilitation in partnership with SEMA Construction) will be presented to demonstrate how this project delivery method overcame project site challenges and constraints to complete complex safety critical rehabilitation projects. The project scope on these projects included improving the capacity and function of service spillways, outletworks, and embankment stability and seepage features, and other dam safety infrastructure.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".