Bibliographic record
Abstract
Two weeks. 13 bootcamps. 150 participants. Over 10,000 oil and gas wells represented in the form of more than 1 million lines of data. These were the key ingredients to a recent upstream-focused datathon - a data-intensive version of a hackathon - in which a group of petrotechnicals calling themselves the “Mighty DUC Hunters” claimed top honors. Albeit on a small scale, their path to victory highlights how some engineers are navigating the uncertainty surrounding an industry downturn that is being dictated by a global pandemic. Named after the shale sector’s fluctuating population of drilled-but-uncompleted wells, or DUCs, the “DUC Datathon” was organized by the SPE Calgary section and Untapped Energy, another nonprofit working to expand data science within the oil and gas business. And while hosted in Calgary, the event drew people from around the world since every aspect of it was held virtually. Such is the norm these days, but perhaps not to be taken for granted. The once full-time office workers who now must work together but separately from home do so in an effective manner thanks to widely available and often free software that keeps us all connected. Likewise, the ability to acquire basic data science skills today requires only a good internet connection and a strong personal commitment.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".