Bibliographic record
Abstract
Unmanned Aerial Vehicle Technology Making them fly was the easy part. Making them useful was the next challenge. And now unmanned aerial vehicles (UAVs) are being put to the test to determine where they are the most applicable to the oil and gas industry. Companies and university researchers developing these “flying computers” believe that their sector is ready to rapidly expand, because of recent technological advancements and legislation that will open up the unmanned skies in the industry’s largest potential market by 2015: the United States. UAVs are already being used by some oil and gas companies to inspect flare stacks and track migrating wildlife and ice floes in the Arctic. In the near future, UAVs will be used as important tools to respond to oil spills and pipeline monitoring, and in offshore installation and decommissioning operations. A few years ago, the militaries of the world held a virtual monopoly on the application of UAV technology, and UAVs available to the private sector were little more than eyes in the sky with limited functionality. Today, commercial UAVs are the benefactors of miniaturized electronics, partly thanks to the smartphone industry and advanced software programs specifically designed to make sense of the different types of data that can be gathered while flying. Equipped with lasers, high-definition cameras, thermal imaging systems that can “see” at night, and an array of other sensors, advocates of UAVs claim they are not simply cheaper alternatives to fixed-wing aircraft and helicopters, but in many ways are more capable and, without question, safer to operate. Last fall, BP completed an experimental flight in Prudhoe Bay, Alaska, using a quadrotor UAV developed by Canadian-based Aeryon Labs. Quadrotor UAVs, also called quadcopters, use four rotors to lift and move the aircraft and are known for their maneuverability. At the time, US authorities were only issuing flight certificates to determine the airworthiness of UAVs without allowing commercial operations. BP completed the test run to determine how Aeryon’s UAV could inspect oilfield equipment and pipelines to assess maintenance needs.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.066 |
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".