Environmental genomics applications for environmental management activities in the oil and gas industry: state-of-the-art review and future research needs
Bibliographic record
Abstract
Environmental genomics is a rapidly advancing field that promises to revolutionise the way in which industry conducts biodiversity monitoring. The International Association of Oil and Gas Producers Environmental Genomics Joint Industry Program (JIP) was formed in June 2019 with the aim of facilitating the development and uptake of environmental genomics within the oil and gas industry. Towards this goal, a white paper was produced that summarises the state-of-the-art in environmental genomics research, and the opportunities and limitations of applying environmental genomics within industry. The white paper included a comprehensive literature review, and importantly, involved consultation with professionals from academic, regulatory and industry backgrounds from across the globe that had expertise in environmental genomics applications. While this consultation revealed a consensus that the application of environmental genomics has advanced greatly in a brief period, with demonstrable benefits, there was an acknowledgement that key aspects are still lacking that would allow confident application of genomics approaches within industry. Through the review and consultation process, a range of knowledge gaps and areas requiring further development were identified. To elucidate which of these areas were most critical to the successful application of environmental genomics within industry, the JIP is drafting guidance that describes sampling design considerations, minimum standards for laboratory analyses and approaches to genomics data interpretation. Through the drafting of guidance, the JIP hopes to determine which gaps are most critical, enabling these to be prioritised for targeted research. The guidance will then be updated regularly to capture the latest research outcomes.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".