Bibliographic record
Abstract
This paper demonstrates how the principals of forensic engineering can be applied to evaluation of schedules, daily diaries, status reports, meeting notes, and other project documentation to determine why delays occur on a project and which parties are responsible for delays. To understand the actual project history, the forensic engineer should conduct a thorough and fair evaluation of all available project documentation to understand the contractual requirements, project milestones, effects of changes, magnitude of delays to the Critical Path, and the basis of the delay claim dispute. The forensic engineer should have sufficient knowledge and experience with project planning, scheduling, and cost estimating to understand the technical basis of the project schedule. He or she must also evaluate and understand the schedule resource loading methodology, schedule logic structure, validity of the activity durations, actual sequence of events, and the material issues that affected the Critical Path. Use of the original planning software is usually necessary as well. The forensic engineer should conduct an impartial technical evaluation that addresses the important and material issues so responsibility for the delays can be determined and proven with a reasonable degree of certainty to resolve the dispute fairly.
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 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 | 0.021 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".