Bibliographic record
Abstract
The high level of construction activity in the Alberta pipeline projects leads to significant concerns about cost, schedule, safety, engineering productivity, and construction productivity in regards to pipeline operations and activities. Moreover, the growth of industry in Canada and specifically in Alberta means that more products are being processed today than ever before. Those products must be transmitted effectively to the desired area. However, pipeline projects, due to their characteristics and nature, are categorized differently than heavy industrial projects. These characteristics include the length of the pipes in addition to the vast area they cover and also different regulatory processes that pipeline projects follow in order to obtain necessary construction permits. These characteristics are different than other typical industrial projects. Comparatively, the time and work required through the front end-planning phase of pipeline projects sharply increase due to this fact that pipelines vary in lengths and the products they carried inside. Pipelines need a huge amount of study and design prior to the detailed engineering and construction phases. Moreover, the sensitivity of these projects requires special consideration. Several pipeline projects have been either delayed or stopped due to environmental impact concerns, hazardous risks, and public resistance. These specific characteristics, impact factors, and environmental risks create a pressing need for benchmarking of these projects. Benchmarking is a reliable comparison tool used to compare one project's data against other companies’ and operators’ data in the industry. A benchmarking system has been developed over the past several years as a collaboration work between COAA (Construction Owners Association of Alberta) and CII (Construction Industry Institute) to assess the performance of Alberta pipeline projects. An analysis of a research’s results indicated areas for enhancements. The purpose of the current research project is to expand and extend the previous benchmarking system, focusing on activities and methods utilized by engineering, procurement, and construction (EPC) owners and contractors to design and build the pipeline projects. The results of previous Alberta pipeline projects report indicate that specific metrics for pipeline projects need to be better defined and developed in order to build a new, valuable performance assessment system. These new metrics and performance assessment techniques will span the project life cycle from front end planning and detailed engineering through construction, commissioning, and start-up. The current areas for heavy industrial metrics, such as cost, schedule, safety, rework, and productivity will be the focus in developing these new metrics for pipeline projects. This research project contains an extensive literature review of pipeline construction specifically in Alberta in addition to the history and current practices of benchmarking. The data collection phase of the research includes two sets of interview and survey conducted among pipeline industry experts. Finally, conclusions and recommendations achieved from the analyses of gathered information are presented.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".