Blue economy: A new era of petroleum microbiology in a changing climate
Bibliographic record
Abstract
\nThe productivity and health of our ocean hold some good solutions to the world’s challenges in socio-economy. However, climate change and waste discharge are changing the marine capacity to buffer human impacts, further challenging the marine industry, primarily in offshore oil and gas, shipping, and fishery operations. These encourage the blue economy, a sustainable development approach to utilize marine resources. Petroleum microbiology dealing with microbes that can respond, degrade, and alter crude oils, offers an unprecedented opportunity to achieve the knowledge- and science-based blue economy. However, the new-era petroleum microbiology for supporting the blue economy has yet to be systematically discussed. This review introduces the climate change impacts on key marine industrial sectors, highlights the critical role of advanced petroleum microbiology in supporting sustainable development, and offers insight into the challenges and future research opportunities in availing of petroleum microbiology for benefiting our marine environment and responsible economic growth.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".