Bibliographic record
Abstract
Abstract With current market conditions keeping the price of oil below $100 a barrel for the foreseeable future, offshore asset owners and operators have become increasingly focused on cost reductions for maintenance and inspection. In turn, the emphasis on cost reductions has presented an even bigger challenge: ensuring that asset integrity is maintained at satisfactory levels that reduce the risk of safety while still benefiting from cost savings. Traditional methods of inspection and maintenance offered at a lower cost has proven to be the wrong solution as the value in these services has been significantly reduced. By implementing disruptive inspection and maintenance technologies, asset integrity can be effectively managed in a way that is safer and provides better data than traditional methods; all while providing long-term cost savings by minimizing POB (personnel on-board) and reducing major maintenance. These technologies and methods include: Utilization of high definition cameras, laser scanners and ROVs (remote operated vehicles) for tank inspections which significantly reduces, and in many cases, eliminates the need for personnel entry into tanks (i.e. cargo oil, ballast water, potable water tanks).Hot-tapping into live sea water systems and hull plating (while vessels are still in operation) in order to deploy cameras, pipe plugs and anodes which eliminate or significantly reduce the need for divers or ROVs (remote operated vehicles) in the following applications: external hull bottom surveys, isolation valve repair, and ICCP (impressed current cathodic protection) anode maintenance.Utilization of ROVs and crawlers with cleaning and measuring tools (callipers, photogrammetry tools) for class required mooring chain inspections which eliminates the use of divers.Utilization of non-evasive tools such as Real Time Radiography, Digital Radiography and Backscatter Computed Tomography to inspect composite wrap repairs and corrosion under insulation for insulated piping and pressure systems, eliminating the requirement to removal & reinstallation of insulation.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".