Bibliographic record
Abstract
Introduction Getting the right information, into the right hands, in a timely manner, is a crucial aspect of oil and gas exploration and production, especially in harsh conditions. Many of the survey and surveillance jobs, including geological exploration, critical infrastructure inspection, pipeline monitoring, ice condition study and wildlife and other forms of environmental surveys can be dull, dirty and dangerous. These are well suited to robotic aviation. Information today is gleaned from a variety of sources such as installed monitors, terrestrial patrols, fixed and rotary wing aircraft and satellites. Managing the significant issues of cost, risk, safety, flexibility and responsiveness, let alone the disparate nature of the data sets received, is a daily challenge. Effective use of robotic aviation has the potential to greatly improve the situation. A robotic aircraft system that incorporates the airframe, sensor package and data fusion plan is a powerful tool. The quality of the data that can be gathered quickly, and in both a proactive and/or reactive manner has to be seen to be believed. What is even more significant is the huge reduction in footprint in terms of cost, risk and in the requirements for space, fuel and personnel. For example, the fuel required for a fixed wing robotic aircraft is less than one litre per hour, carrying similar or better sensors than a helicopter or light plane. ING Robotic Aviation developed its expertise with the Canadian Army in Afghanistan, and to this day retains a team deployed with the Royal Canadian Navy in the Indian Ocean. Experienced gleaned from military operations is already proving to be useful in the civil sphere, for example in conducting flare stack inspections for Irving Oil and wildlife counts that can be conducted accurately and unobtrusively.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.008 |
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".