Bibliographic record
Abstract
Velociraptor is the name of extinct dinosaur from the Cretaceous Era and a drilling rig left behind by a company that died during last year’s COVID-19 event. Both creatures have a larger-than-life image. The dinosaur is famous as a terrifying presence in the Jurassic Park movies. The cinematic terror was modeled after a far larger ancestor of the Velociraptor, a dinosaur the size of a wolf that fed on small animals, according to an account in National Geographic. The Raptor rig, which was famous as Canada’s entry into the race to become the world’s first fully automated land rig, was created by a company that ran out of cash in the summer of 2020 after the deal to drill its first well was cancelled. Its true capabilities will not be known unless the current owner finds a customer willing to support the work needed to finish the rig and find a first customer. Still, the story of the Raptor offers a look at the skills, resources, and partners needed to bring an automated rig to life. The Raptor was conceived in a different era, back when North American drillers were racing to keep up with the demand arising from the shale boom, oil was selling for $100/bbl, and it took a lot longer than it does now to drill a horizontal well. Among the beneficiaries was Reg Layden, a rig designer who had just developed an ultrafast-drilling heavy coiled tubing rig. The innovation drew positive notices, but drillers were not about to embrace coiled tubing, so he designed a rig with an automated rig floor that could connect sections of pipe far faster than humans. His vision of the future was equipped with two derricks to allow continuous connections. One derrick would be connecting a section of pipe while the other would be readying the next one. They would roll back and forth, allowing one to be over the drill center while the other was readying the next stand. The startup, Raptor Rig, brought in Halliburton as a backer. It bought 23% of the startup shares and began a series of cash advances at a time when competitors including Schlumberger and NOV were developing their own drilling automation innovations. By 2015, the price of a barrel of oil had dropped to around $50, but an investor who was acting as spokesman for Raptor, Cameron Chell, predicted it would have a working rig by 2017, according to a story in Rigzone. That deadline came and went and the advances from Halliburton exceeded $25 million. Work ended sometime during the summer of 2020. The depth of the trouble was revealed when Halliburton filed suit at the end of July 2020 demanding payment for the nearly $29 million loaned to Raptor, which was secured by the rig and other property, according to court records in Calgary, Alberta. That pushed the company to seek bankruptcy protection. Since Raptor lacked the cash to pay its debts, the court appointed a receiver to sell the assets to raise as much money as possible to pay its creditors.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.029 | 0.020 |
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".