Bibliographic record
Abstract
YEPP PerSPEctive - John MacArthur shares his thoughts on leadership. Humanity has always faced tough challenges. These challenges remain daunting. According to Jessica Williams' book 50 Facts That Should Change the World, one in five people in the world live on less than U.S. $1 per day and about the same proportion, some 800 million people, go hungry daily. Williams explains that in recent years, a third of the world's population has been at war, with a quarter of all armed conflicts involving a struggle for natural resources such as oil, minerals, metals, water, timber, and drug crops. The good news is that our industry can help tackle these problems; a secure supply of energy is critical to peaceful economic growth and a stable development environment. Global energy demand grew by more than 4% last year, exceeding 10 billion tons of oil equivalent (approximately 73 billion bbl) for the first time, and according to Intl. Energy Agency (IEA) projections, this could increase by more than half over the next quarter century. We will still rely on hydrocarbons, with 60% of demand to be met by oil and gas by 2030.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".