Achieving Predictable Outcomes for Modular Construction in Megaprojects
Bibliographic record
Abstract
Abstract Megaproject definition: A capital investment project with a total installed cost in excess of $1 billion USD with complex stakeholder arrangements. Rystad Energy estimates that global capital expenditure in oil and gasfields dropped $215bn between 2014 and 2015, shaving almost 0.3% off the size of the global economy. That trend continues in this low oil price environment. The effects of volatile and low oil prices vary along the value chain and are most acutely felt upstream with all national oil companies (NOCs) and international oil companies (IOCs) revising their capital investment schemes downwards. As the number and size of megaprojects continues to increase across all sectors of the capital projects industry, one fact stands out: megaprojects tend to underperform more often than not. More specifically, megaprojects fail to keep within their approved budgets, fail to meet their approved schedules, or fail to achieve their promised business objectives. To provide substance to this assertion, independent and empirical studies (by Ernst & Young and the I.P.A.) point out that the sector record in executing major oil and gas projects is far from palatable. Approximately 64% exceed their control budget and over 70% experience schedule delays. It is therefore necessary for the oil and gas industry to examine the underlying reasons for this disappointing failure in order to achieve predictable outcomes on investments.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".