Risk benefit framework for using unmanned systems in industrial operations
Bibliographic record
Abstract
Our environment is constantly being threatened by human activity. Global warming and wildlife extinction, for example, are some of the consequences of our daily routine, which at the same time is also the cause of Earth contamination. Earth contamination can appear in different forms such as air pollution, ecosystem damage, contamination, etc. and among the businesses that contribute to these occurrences, the oil transporting activity can be found.\nOil transport, or in other words the existence of pipelines, is as the Canadian Energy Pipeline Association (CEPA) states ‘the major driver of Canada’s current and future prosperity’. However, even if they yield advantages, their hazards and their environmental impact cannot be forgotten; that is why, pipeline monitoring takes such an important role. In order to carry out this surveillance task many alternatives have been studied and many of them have already been implemented. Nevertheless, environmental impacts from pipelines have not ceased and as a result, new options are being explored. Among these new monitoring options, it seems that the use of Unmanned Systems alternative is taking shape and that, in the near future, they could be the answer for a reduction in the number of spills and leakages in pipelines. This reduction will at the same time be the response for a lower environmental impact concerning Oil sands and pipelines, and their activities.\nSo as to evaluate the suitability of this solution, it was therefore decided to perform a risk analysis of the use of such appliances in industrial operation activities.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".