Bioherder Generated by Rhodococcus erythropolis as a Marine Oil Spill Treating Agent
Bibliographic record
Abstract
There is an urgent call for contingency planning with effective and eco-friendly oil spill cleanup responses. In situ burning, if properly applied, could greatly mitigate oil in water and minimize the adverse environmental impacts of the spilled oil. Chemical herders have been commonly used along with in situ burning to increase the thickness of spilled oil at sea and facilitate combustion. These chemical surfactant-based agents can be applied to the edges of the oil slick and increase its thickness by reducing the water–oil interfacial tension. Biosurfactants have recently been developed as the next generation of herds with a smaller environmental footprint. In this study, the biosurfactant produced by Rhodococcus erythropolis M25 was evaluated and demonstrated as an effective herding agent. The impact of environmental and operational factors (e.g., temperature, herder dose, spilled oil amount, water salinity, and operation location) on its performance was investigated. A five-factor fractional design was applied to examine the importance of these factors and their impact on herding effectiveness and efficiency. The results of this study showed that higher temperature and a higher dose of herder could result in an increased oil slick thickness changing rate. Differences in water salinity at the same temperature led to the same trend, that is, the herding process effectively goes up with increasing herder–oil ratio (HOR). Further large-scale testing needs to be conducted for evaluating the applicability of the developed bioherder in the field.
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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 | 0.000 | 0.000 |
| 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) | 0.011 | 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".