How to explore for helium
Bibliographic record
Abstract
Summary Helium is a minor biproduct of some natural gas production in the USA, Qatar, Algeria, Canada, Australia and a couple more countries. Discovery of helium provinces has been serendipitous. A critical element for society, helium is used in medical cryogenics, as an inert atmosphere in manufacturing, a breathing mixture with oxygen, for leak detection and other industrial processes. Recently demand has outstripped supply and reliance on serendipity to deliver new discoveries is clearly inadequate. In 2012 we began to develop a strategy for helium exploration, borrowing from the petroleum exploration playbook to determine source of helium, primary and secondary migration processes and characteristics for trapping. An opportunity to test our strategy emerged in late 2015. From Lake Rukwa in Tanzania there were reports of cold helium seeps. The East African Rift, Archean granitic basement with a substantial and recent thermal event fitted our criteria for helium generation and migration. The gases proved to be nitrogen/helium mixtures free of petroleum gases and with maximum helium concentrations of around 10% some 30 times greater than the commercial threshold used in the USA. Exploration drilling in now underway in Tanzania whilst research continues to refine our understanding of natural helium systems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.079 | 0.039 |
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".