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
The International Association of Oil and Gas Producers (IOGP) Environmental Genomics Joint Industry Program (JIP) was formed in June 2019. The aim of the JIP is to facilitate the development of guidelines for the application of environmental genomics to support environmental management activities in the oil and gas industry. Towards this goal, a white paper summarizing the state-of-the-art in environmental genomics research and how it may be used to advance technology development opportunities for the oil and gas industry was drafted. More specifically, a series of applications and focus areas of primary interest to oil and gas companies were covered including: baseline assessments; detection of key species; rapid assessment of invasive species; population status and dynamics; monitoring of environmental effects of oil and gas activities; remediation and restoration; sampling design; data analysis and interpretation; community representation; species abundance, distribution and viability; and real-time on-site measurement and analysis. baseline assessments; detection of key species; rapid assessment of invasive species; population status and dynamics; monitoring of environmental effects of oil and gas activities; remediation and restoration; sampling design; data analysis and interpretation; community representation; species abundance, distribution and viability; and real-time on-site measurement and analysis. In addition to the literature review, consultation of professionals from academic, regulatory, and industrial backgrounds with expertise on these topics was conducted. While there was a consensus that the application of environmental genomics has advanced greatly in a short period of time with demonstrable benefit potential, there was acknowledgement that key aspects of best management practices are still lacking. Furthermore, while the majority of regulators interviewed were aware to varying degrees of the methodological limitations which restrict the present use of environmental genomics in regulatory affairs, it transpired that there is considerable appetite and capacity amongst the regulatory community to engage in collaborative research initiatives with the oil and gas industry and academia. Through these academic, regulatory, and industrial consultation, specific environmental genomics study areas and applications requiring further development and refinement were identified. These include: methodological standardization, persistence and dispersal of eDNA; eDNA data integration with various other data types; improvement of reference databases; and refinement of molecular indices. methodological standardization, persistence and dispersal of eDNA; eDNA data integration with various other data types; improvement of reference databases; and refinement of molecular indices. Based on the above and considering the most efficient path to greater regulatory uptake for environmental genomic approaches for the oil and gas industry, the JIP’s recommendation is to pursue a Common-Garden Experiment. Such experiment should seek the involvement and ultimately endorsement from the Regulators marking the path towards wider regulatory acceptance and uptake of eDNA-based approaches.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".