Bibliographic record
Abstract
Strategic and operational management in the exploration and production business is characterised by prediction and decision making in a data-rich, high-uncertainty environment. Analysis of predictive performance since the 1970s by multiple researchers indicates that predictions are subject to over-confidence and optimism negatively impacting performance. The situation is the same for other areas of human endeavour also operating within data-rich, high-uncertainty environments. Research in the fields of psychology and neuroscience indicates the way in which the human brain perceives, integrates and allocates significance to data is the cause. Significant effort has been dedicated to improving the quality of predictions. Many individual companies review their predictive performance during long periods, but few share their data or analysis with the industry at large. Data that is shared is generally presented at a high level, reducing transparency and making it difficult to link the analysis to the geology and data from which predictions are derived. This extended abstract presents an analysis of predictive performance from the Eromanga Basin where pre-drill predictions and detailed production data during a period of decades is available in the public domain, providing an opportunity to test the veracity of past observations and conclusions. Analysis of the dataset indicates that predictions made using both deterministic and probabilistic methodologies have been characterised by over-confidence and optimism. The reasons for this performance are discussed and suggestions for improving predictive capability provided.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".