Screening and characterization of biosurfactant producers from petroleum hydrocarbon contaminated marine sources in North Atlantic Canada for oil spill responses
Bibliographic record
Abstract
As one of the oil spill responses, oil dispersion was found effective in open sea and under harsh conditions. However, currently used chemical surfactant-based dispersants may harm the environment due to the toxicity and persistency. Thus novel, environmentally friendly biosurfactant-based dispersants are desired. Biosurfactants are less toxic, biodegradable, and can be biologically produced. Their establishment is impeded by a lack of economic and versatile products. Discovery of new biosurfactant producers is the key to overcome the obstacles. This dissertation will thus fill the research gap through screening and characterization of biosurfactant producing microorganisms from petroleum hydrocarbon contaminated marine sources in the North Atlantic Canada. Fifty-five biosurfactant producers belong to 8 genera were isolated. Some of the isolated strains were found with properties such as greatly reducing surface tension, stabilizing emulsion and producing flocculant. Three strains with interesting characteristics and limited relevant publications were selected for genetype and phenotype characterization. The strains, the products and the bioprocess can be of great value to both scientific understanding and the environmental applications in offshore oil spill responses.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".