Bibliographic record
Abstract
Much has been learned during the successful completion of the first major natural gas project in the Atlantic region. How to capture the lessons learned from the project holds many lessons in itself. Background A Lessons Learned Report was published to document the key lessons from a world class natural gas project; the first of its kind for Atlantic Canada. Because the project itself was very successful, capturing information on how the project team met and overcame the many challenges the project faced will be of benefit to those who undertake future projects of this size and complexity. Much can be learned from studying the lessons learned process (how the data was gathered, categorized, interpreted, prioritized, and summarized). This article describes the lessons learned process and what we have learned from it. It does not focus on what specific lessons were learned from the first major natural gas project in the Atlantic region. Rather, it deals with how the project team went about gathering the lessons and how the team members successfully set out the nuggets of what was learned for those who will manage and participate in future projects of a similar size and complexity. The Lessons Learned Process Objective and Approach A lessons learned team was formed to capture the key lessons learned during all phases of the project (front end planning and approvals, engineering design, construction, and commissioning), so that these lessons could be carried forward to guide others who will manage and participate in other similar East Coast developments or major capital projects. Task Team The lessons learned task team was comprised of representatives from the various organizational functions of the project. The team was supported by experienced facilitation and technical documentation consultants. Terms of Reference The terms of reference set out some boundary conditions or limitations. The terms specified that:The task team was to take direction from a steering.The task team was only assigned part-time and the members were not to be significantly diverted from their primary duties on the project.Part-time facilitation and documentation support was to be made available.The data was to be gathered before most of the project staff was demobilized.Data was not to be limited to only that from senior management, but was to include a representative sample of on-site supervisors.Data gathering was to include some individual and some group interviews with key project staff.A representative sample of contractors, suppliers, and regulators were also to be interviewed. Data Gathering Data was gathered by the task team through over ninety interviews with individuals and groups from all aspects of the project. Interviewees were asked to identify three to five things that they believed contributed most significantly to the project's success, and three to five things that they thought might have been done better. All interviewees were guaranteed anonymity and interview results were maintained in a confidential database. Interviewees were given the opportunity to validate their input by reviewing and approving or correcting the interview summary sent to them later.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".